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Towards an Automatic Fuzzy Ontology Generation
Abstract
In recent years, the success of Semantic Web is strongly related to the diffusion of numerous distributed ontologies enabling shared machine readable contents. Ontologies vary in size, semantic, application domain, but often do not foresee the representation and manipulation of uncertain information. Here we describe an approach for automatic fuzzy ontology elicitation by the analysis of web resources collection. The approach exploits a fuzzy extension of Formal Concept Analysis theory and defines a methodological process to generate an OWL-based representation of concepts, properties and individuals. A simple case study in the Web domain validates the applicability and the flexibility of this approach.
... Since preparation and maintenance of domain ontologies is a time consuming activity, in literature several methods aimed at supporting the ontology learning from domain data by using text-mining and machine learning techniques [14], [15], [16], [17]. Furthermore, since real world is vague and uncertain, nowadays it is deeply investigated a fuzzy version of FCA where an attribute can be associated with an object with a certain degree (see [3], [4], [5], [6]). There is a plethora of methods to build a fuzzy lattice which are collected and compared in [7]. ...
... Our attention is focused on the methods presented in [6] and in [5]. The first one uses a fuzzy closure approach whereas the second one uses an one-sided threshold approach. ...
... In fact, this work points out that the one-sided threshold approach can be seen as a specialization of the fuzzy closure one. To do this, we exhibit two operators F and G such that, given a formal context K = (X, Y, I), the lattice of formal concepts is equal to that generated by the method introduced in [5]. First of all, these operators are defined for any residuated lattice. ...
... (1) What is the apposite information retrieval model? (2) How to execute and build ontology? ...
... (3) How to discover the relevant documents by ontology? 2 The Scientific World Journal ...
... They converse about approximating reasoning for additional enhancement of the ontology. de Maio et al. [2] described an approach by analyzing the web resource collection for automatic fuzzy ontology elicitation. This approach applicability is validated by web domain case study. ...
Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology.
... FOGA is also not able to produce fuzzy relational concepts from unstructured or semi-structured text documents. Others have produced fuzzy formal contexts but then use an α-cut on them to produce a crisp formal context [35] [36]. Only those entries in the fuzzy formal context with a membership degree greater than or equal to α are kept and converted to a crisp entry of 1. FCA is used on the crisp context to create the formal concepts and its lattice structure. ...
... One simple approach in [35] adds fuzziness by determining each object's membership in the extent as the minimum of the membership degrees of the attributes that the object possesses in the intent of the concept. Although the work in [36] uses the same method of creating the fuzzy formal context, instead of using clustering methods on the fuzzy formal concepts to create the hierarchy of the fuzzy ontology, it uses a direct transformation method. As part of constructing the hierarchy, it adds a membership degree between child and parent concepts calculated using a fuzzy set similarity measure between the fuzzy set extents of the child and parent concepts. ...
Although ontologies have become the standard for representing knowledge on the Semantic Web, they have a primary limitation, the inability to represent vague and imprecise knowledge. Much research has been undertaken to extend ontologies with the means to overcome this and has resulted in numerous extensions from crisp ontologies to fuzzy ontologies. The original web ontology language, and tools were not designed to handle fuzzy information; therefore, additional research has focused on modifications to extend them. A review of the fuzzy extensions to allow fuzziness in ontologies, web languages, and tools as well as several very current examples of fuzzy ontologies in real-world applications is presented.
... Regarding the FCA context generation, our approach includes a preprocessing activity that performs filtering and fuzzification of incoming sensor observations with respect to a set of linguistic terms (e.g., low, medium, high) by using different membership functions according to the kind of sensor. In addition, the formal context has been filtered using the one-sided threshold [8] algorithm that considers only observation values whose membership function is higher than a fixed threshold (usually, 0.6). A similar approach is illustrated in [21], where large sets of data are condensed, during lattice construction, by means of frequent pattern discovery and association rule mining. ...
... In order to provide a reliable, high-throughput, low-latency system of queuing real-time data streams, it is possible to adopt a distributed message queuing framework, such as Apache Kafka 7 that helps to deliver observation streams to the parallel processing supported by an infrastructure built upon Apache Storm. 8 Storm enables to set up a distributed architecture whose backbone is called topology. A topology is a graph of spouts and bolts that are connected by means of stream groupings. ...
Nowadays, one of the main challenges in the smart cities is mining high-level semantics from low-level activities. In this context, real-time data streams are continuously produced and have to be analysed by efficient and effective algorithms able to handle complexities related to big data in order to enable the core functions of Decision Support Systems in the smart city. These algorithms should receive input data coming from different city domains (or pillars) and process, aggregate and reason over them in a way that it is possible to find hidden correlations among different and heterogeneous elements (e.g., traffic, weather, cultural events) along space and time dimensions. This paper proposes the online implementation and deployment of Temporal Fuzzy Concept Analysis on a distributed real-time computation system, based on Apache Storm, to face with big data stream analysis in the smart city context. Such online distributed algorithm is able to incremetally generate the timed fuzzy lattice that organizes the knowledge on several and cross-domain aspects of the city. Temporal patterns, of how situations evolve in the city, can be elicited by both exploring the lattice and observing its growth in order to obtain actionable knowlege to support smart city decision-making processes.
... Constructing fuzzy ontologies based on formal concept analysis theory (Quan et al., 2006a(Quan et al., , 2006bChen et al., 2009;De Maio et al., 2009;Cross & Kandasamy, 2011) Constructing fuzzy ontologies from fuzzy database models (Blanco et al., 2005(Blanco et al., , 2008Ma et al., , 2010Ma et al., , 2011aMa et al., , 2011bZhang et al., 2008aZhang et al., , 2008bZhang et al., , 2011bZhang et al., , 2013aZhang et al., , 2013bZhang et al., , 2015) Constructing fuzzy ontologies from other data sources such as fuzzy narrower terms, fuzzy relations, among others (Widyantoro & Yen, 2001a, 2001bNikravesh et al., 2004;Angryk et al., 2006;Ceravolo et al., 2006;Nováček & Smrž, 2006;Ling et al., 2007;Tafazzoli & Sadjadi, 2008;Ghorbel et al., 2010;Inyaem et al., 2010;Alexopoulos et al., 2012) Querying over lightweight fuzzy DL ontologies (Straccia, 2006c;Pan et al., 2007Pan et al., , 2008 Querying over expressive fuzzy DL ontologies (Mailis et al., 2007;Cheng et al., 2008bCheng et al., , 2009aCheng et al., , 2009b Querying over fuzzy ontologies based on fuzzy relational databases (Buche et al., 2005;Bahri et al., 2009) Other fuzzy ontology query approaches (Widyantoro & Yen, 2001a, 2001bBulskov et al., 2002;Bandini et al., 2006;Knappe et al., 2007;Carlsson et al., 2010) Storing fuzzy ontologies (Barranco et al., 2007;Lv et al., 2009;Zhang et al., 2011a) ...
... Chen et al. (2009) focused on research on automatic fuzzy ontology generation from fuzzy context, where fuzzy FCA and fuzzy concept hierarchy structure were adopted to automatically generate primitive fuzzy ontology, and they also showed that how to use fuzzy concept lattices from fuzzy formal context to support the modeling automatic or semi-automatic for fuzzy ontology generation. Moreover, based on the FCA theory, De Maio et al. (2009) presented an approach for automatic generation of a fuzzy ontology. The approach indeed presented the mapping steps for translating the fuzzy lattice generated by FCA theory into an ontology. ...
Ontology, as a standard (World Wide Web Consortium recommendation) for representing knowledge in the Semantic Web, has become a fundamental and critical component for developing applications in different real-world scenarios. However, it is widely pointed out that classical ontology model is not sufficient to deal with imprecise and vague knowledge strongly characterizing some real-world applications. Thus, a requirement of extending ontologies naturally arises in many practical applications of knowledge-based systems, in particular the Semantic Web. In order to provide the necessary means to handle such vague and imprecise information there are today many proposals for fuzzy extensions to ontologies, and until now the literature on fuzzy ontologies has been flourishing. To investigate fuzzy ontologies and more importantly serve as helping readers grasp the main ideas and results of fuzzy ontologies, and to highlight an ongoing research on fuzzy approaches for knowledge semantic representation based on ontologies, as well as their applications on various domains,
in this paper
,
we provide a comprehensive overview of fuzzy ontologies
. In detail, we
first
introduce fuzzy ontologies from the most common aspects such as
representation
(including categories, formal definitions, representation languages, and tools of fuzzy ontologies),
reasoning
(including reasoning techniques and reasoners), and
applications
(the most relevant applications about fuzzy ontologies). Then,
the other important issues
on fuzzy ontologies, such as
construction
,
mapping
,
integration
,
query
,
storage
,
evaluation
,
extension
, and
directions for future research
, are also discussed in detail. Also, we make some
comparisons and analyses
in our whole review.
... However, the framework could be instantiated implementing other suitable techniques for ontology extraction (e.g., LDA [22], hierarchical clustering [13]). According to our previous works about ontology extraction, the knowledge structure resulting by applying FFCA, including concepts and their relationships, will be formalized in a machine understandable manner exploiting technologies of Semantic Web (i.e., OWL, RDFS, and so on), as detailed in [23]. ...
... Fuzzy Formal Concept according to the theory of FFCA is represented with both intentional and extensional information. Thus, Ontology Modeling achieves a translation of both intentional and extensional information into the corresponding classes and relations of the OWL ontology [23]. ...
... To increase the expressivity level of OWL, several new constructs were introduced along with their unique modeling features, such as extended annotations and complex data representations. The construction of an ontology has become both an art and an understanding of engineering processes [12,13]. ...
... We found that in the literature, some researchers performed work on the travel ontology domain, and their work was limited in scope and was designed for academic purposes only. Our ontology modeling approach is based on the type-2 fuzzy concept [12] and its incorporation with a secure ontology that is novel in its usage; this approach can handle any type of realworld scenario that is related to the air ticket booking domain. The most prominent aspect to our approach is the application of the ontology contents security feature, which prevents attempts of unauthorized contents from altering or removing information. ...
... It starts up whenever a learner logs into the system and loads the appropriate learning context, according to the target subjects the learner has to study and his/her preferences. [24]. ...
... According to Definition 3, each entry of the matrix represents the membership (g, m) ∈ [0, 1] for the row g (feed) and column m (terms). This value is computed by applying tf-idf-based techniques [24,29] to evaluate the most recurrent terms appearing in the textual content of the whole feed collection. -Concepts Hierarchy Building: the fuzzy formal context constitutes the input of this task. ...
Nowadays, Web 2.0 focuses on user generated content, data sharing and collaboration activities. Formats like Really Simple Syndication (RSS) provide structured Web information, display changes in summary form and stay updated about news headlines of interest. This trend has also affected the e-learning domain, where RSS feeds demand for dynamic learning activities, enabling learners and teachers to access to new blog posts, to keep track of new shared media, to consult Learning Objects which meet their needs.This paper presents an approach to enrich personalized e-learning experiences with user-generated content, through a contextualized RSS-feeds fruition. The synergic exploitation of Knowledge Modeling and Formal Concept Analysis techniques enables the design and development of a system that supports learners in their learning activities by collecting, conceptualizing, classifying and providing updated information on specific topics coming from relevant information sources. An agent-based layer supervises the extraction and filtering of RSS feeds whose topics cover a specific educational domain.
... Ontology construction by domain experts is a labor-intensive task. Thus, several (semi-)automated methods were suggested using rule-based approaches [15,25,29,33,43] later advancing to techniques based upon Formal Concept Analysis (FCA) [12,19,47,48] and Natural Language Processing (NLP) [2,8,16,41]. ...
... The use of fuzzy logic for creating concept lattices and ontologies has been studied previously by various researchers. There have been studies regarding fuzzy ontology creation (De Maio et al., 2009), (Tho et al., 2006, using fuzzy ontology and concept models in various domain-specific tasks and dataset (Parry, 2006), (Abulaish, 2009), (Quach and Hoang, 2018). As opposed to the previous work, we employ fuzzy logic using network metrics attributes. ...
Domain-specific conceptual bases use key concepts to capture domain scope and relevant information. Conceptual bases serve as a foundation for various downstream tasks, including ontology construction, information mapping, and analysis. However, building conceptual bases necessitates domain awareness and takes time. Wikipedia navigational templates offer multiple articles on the same/similar domain. It is possible to use the templates to recognize fundamental concepts that shape the domain. Earlier work in this domain used Wikipedia's structured and unstructured data to construct open-domain ontologies, domain terminologies, and knowledge bases. We present a novel method for leveraging navigational templates to create domain-specific fuzzy conceptual bases in this work. Our system generates knowledge graphs from the articles mentioned in the template, which we then process using Wikidata and machine learning algorithms. We filter important concepts using fuzzy logic on network metrics to create a crude conceptual base. Finally, the expert helps by refining the conceptual base. We demonstrate our system using an example of RNA virus antiviral drugs.
... Other proposals are based on the creation of ontologies based on the content of different social networks or websites [4] [5], using the Formal Concept Analysis (FCA) technique. A method for data analysis and knowledge representation. ...
Due to the ease of generating and storing written text documents, Natural Language Processing tools are increasingly integrated into software products on decision making in various fields. In this paper, we work with real requests from customers of a real estate company. The objective of the paper is to design a preliminary approximation of new intelligent algorithms that can carry out a semantic analysis of text documents in the business environment. For this purpose, different prototypes are developed, based on techniques and methods from different disciplines, such as Natural Language Processing, to process and structure text, Text mining to find the most relevant terms and Knowledge Engineering, to create an ontology with the objective of measure semantic similarity in business documents. The prototypes obtained are capable of determining the semantic similarity between simple phrases, structuring existing terms, and determining the subject on which they deal with requests from clients of a real estate company.
... Other proposals are based on the creation of ontologies based on the content of different social networks or websites [4] [5], using the Formal Concept Analysis (FCA) technique, a method for data analysis and knowledge representation. FCA defines a formal context to establish relationships between objects and attributes on a domain. ...
Due to the ease of generating and storing written text documents, Natural Language Processing tools are increasingly integrated into software products on decision making in various fields. In this paper, we work with real requests from customers of a real estate company. The objective of the paper is to design a preliminary approximation of new intelligent algorithms that can carry out a semantic analysis of text documents in the business environment. For this purpose, different prototypes are developed, based on techniques and methods from different disciplines, such as Natural Language Processing, to process and structure text, Text Mining to find the most relevant terms and Knowledge Engineering, to create an ontology with the objective of measuring semantic similarity in business documents. The prototypes obtained are capable of determining the semantic similarity between simple phrases, structuring existing terms, and determining the subject on which they deal with requests from clients of a real estate company.
... This rating mechanism helps to present pertinent content to the novel user and diminishes pursuit period from huge web pages. Once the user registers their profile before their search, the requested relevant web pages automatically displayed to the user with the evaluated and analyzed data 30,31 . The decision making concept describe episodic web access pages. ...
Relevant digital content collection and access are huge problems in digital libraries. It poses a greater challenge to the digital library users and content builders. In this present work an attempt has been made to design and develop a user-adaptive multi-agent system approach to recommend the contents automatically for the digital library. An adaptive dialogue based user-interaction screen has also been provided to access the contents. Once the new contents are added to the collection then the system should automatically alert appropriate user about the new content arrivals based on their interest. The user interactive Question Answering (QA) system provides enough knowledge about the user requirements.
... Furthermore, due to the fact that the proposed algorithm provides reliable membership functions, it is suitable to apply this algorithm into the constructions of various kinds of fuzzy systems. The fuzzy membership degrees generated from this BPA determination process are requisite in the areas such as the generation of fuzzy ontology [56,57] and fuzzy description logic [58]. ...
Under the circumstance of the complete frame of discernment, There are quantities sufficient researches applying the Dempster-Shafer evidence theory (D-S theory) to process uncertain information. However, in most real cases, the frame of discernment is not complete, and the classic evidence theory is not applicable in some degree, including the basic probability assignment (BPA) generation method. In this paper, under the assumption of the open world, the BPA determination issue is focused originally, and a new method based on triangular fuzzy member is proposed to determine BPA. First, the mean value, standard deviation, extreme values as well as the triangular membership function of each attribute can be determined. Then, a nested structure BPA function with an assignment for the null set can be constructed, using the intersection point of one test sample and the models above. Experiments are conducted with the proposed method to determine BPA, through which the classification accuracy rates are computed, analyzed and compared with those generated by other BPA determination methods, which demonstrates the efficiency of the proposed method both in the open world and in the closed world.
... In order to provide the necessary means to handle imprecise and uncertain information in the Semantic Web there are today many proposals for fuzzy extensions to ontologies 18,19,20,21,22,23,24 . Also, how to construct fuzzy ontologies has increasingly attracted attention and several strategies for constructing fuzzy ontologies were proposed in 25,26,27,28,29,30,31 . Being similar to the requirement of handling imprecise and uncertain information in the context of the Semantic Web, the incorporation of imprecise and uncertain information in UML models has been an important topic of data modeling research because such information extensively exists in many real-world applications. ...
The success and proliferation of the Semantic Web depends heavily on construction of Web ontologies. However, classical ontology construction approaches are not sufficient for handling imprecise and uncertain information that is commonly found in many application domains. Therefore, great efforts on construction of fuzzy ontologies have been made in recent years. In this paper, we propose a formal approach and develop an automated tool for constructing fuzzy ontologies from fuzzy UML models. , we propose formalization methods of fuzzy UML models and fuzzy ontologies, where fuzzy UML models and fuzzy ontologies can be represented and interpreted by their respective formal definitions and semantic interpretation methods. , we propose an approach for constructing fuzzy ontologies from fuzzy UML models, i.e., transforming fuzzy UML models (including the structure and instance information of fuzzy UML models) into fuzzy ontologies. , following the proposed approach, we implement a prototype transformation tool called that can construct fuzzy ontologies from fuzzy UML models. Constructing fuzzy ontologies from fuzzy UML models will facilitate the development of Web ontologies. , in order to show that the constructed fuzzy ontologies may be useful for reasoning on fuzzy UML models, we investigate how to reason on fuzzy UML models based on the constructed fuzzy ontologies, and it turns out that the reasoning tasks of fuzzy UML models can be checked by means of the reasoning mechanism of fuzzy ontologies.
... his rating mechanism helps to present pertinent content to the novel user and diminishes pursuit period from huge web pages. Once the user registers their proile before their search, the requested relevant web pages automatically displayed to the user with the evaluated and analyzed data 30,31 . he decision making concept describe episodic web access pages. he fuzzy association rule is used to detect repeatedly referred web ages 32,33 . ...
Background/Objectives: Web applications are growing at a massive promptness and its users rise at exponential speed. The deviations in technology have made it potential to capture the user's essence and interactions with web applications through web log file. Methods/Statistical Analysis: Due to huge amount of extraneous data in the web log, the original log file cannot be openly used in the Web Usage Mining (WUM) system. The web server log files used for mining several expedient patterns to analyze the access behavior of the user. Data pre-processing plays a dynamic role in Data Mining. Findings: We propose a new web log mining method for determining web access procedure from interpreted web usage logs which integrates data on user behaviors through self-rating and communication tracking. The raw data is preprocessed in order to improve the quality of data to be extracted. We discuss the significance of information pre-processing method and abundant steps elaborate in receiving the essential information effectively. This pre-processing method used to process and analysis the web log data for extraction of user search patterns. The fuzzy association rule minimizes users' exploration period and facilitate for enhance decision making. Applications/Improvements: The proposed data cleaning method removes the extraneous records from web log. With this progression, we create a Personalized Ontology, which assists various semantic web applications such as site perfection, business intelligence and recommendations for behavioral analysis. Finally, we illustrate the efficiency of this method by the experimental results in the framework of data pre-processing and cleansing extraneous data for personalized ontology.
... FFCA takes fuzzy formal context as input and gives fuzzy concept lattice as output [121] which is then used to develop fuzzy ontologies. Most of the research work to generate fuzzy concept lattices uses two major approaches, namely, i) a α-cut approach [123], and ii) fuzzy closure operator [124]. ...
Information retrieval has been an integral domain of storage and retrieval of meaningful information from the early age of computing. The last 15 years has seen a drastic shift of paradigm from classical database retrieval to web-based information retrieval. The WWW has established itself as one of the primary mode of information storage, sharing and searching. The size of the WWW and its users has made the retrieval and retrieval systems much more complicated as well as sophisticated and thus posed a grand challenge to the researchers and developers to design the modern information retrieval system which can return the query result as per user’s requirement effectively and efficiently. This survey paper is an attempt to find some of the challenges faced by modern retrieval system in effective retrieval of information and different methodologies of fuzzy logic trying to make this mammoth and complex task simpler.
... An intermediate step clusters documents into "similar" groups. De Maio et al [15] also use a threshold in their application of fuzzy FCA to create a semantic web ontology in OWL. Zhou et al [16] follow a similar approach, but use a membership "window" to select tuples from the fuzzy relation and convert to a crisp relation -that is, they take tuples whose membership is above a threshold but also below an upper limit. ...
... An intermediate step clusters documents into "similar" groups. De Maio et al. 16 also use a threshold in their application of fuzzy FCA to create a semantic web ontology in OWL. ...
Creative knowledge discovery—finding useful, previously unknown links between concepts—is a vital tool in unlocking the economic and social value of the vast range of networked data and services that is now available. We define “standard” knowledge discovery as the search for explanatory and predictive patterns in a specific domain, usually with a large volume of data. In contrast, creative knowledge discovery is concerned with the creation of new (and effective) patterns—either by generalization of existing patterns or by analogy to patterns in other domains. An important precondition for creative knowledge discovery is that we understand the relations within the data. Fuzzy formal concept analysis is a powerful approach that enables us to find embedded structure in data and to extract novel concepts that can be used in subsequent processing such as creative knowledge discovery.
This paper outlines a fast algorithm for computing fuzzy formal concepts and provides a brief illustration of the use of fuzzy formal concept analysis in creative knowledge discovery.
... A natural group is a set of objects linked by common properties. FCA can also be defined as a technique of data analysis, which enables representation of the relationships between object and attributes in a given domain [7]. In addition, FCA is identified within a formal context. ...
Recently, there has been rapid growth in the volume of digital data from semi-structured and unstructured sources, such as web pages, images, videos, tweets, blog posts, and emails. The volume of data increases daily, making it difficult to access the data efficiently. To tackle the problems, we can represent the data in a summarised form using fuzzy formal concept lattices. In a situation where data is added dynamically, the concept lattices may evolve and the degree of change can be measured using a distance metric. In previous studies, we have successfully measured the change between two fuzzy lattices, based on pairing concepts in the two lattices and finding the cost of converting each concept to its counterpart. In this paper, we present a modification to the distance measurement, namely Support-based Distance Measurement. The aim of this method is to measure the change between lattices with disjoint sets of objects and identical attributes. The distance is found by considering the change in support (or relative cardinality) for each concept’s extension. The method is illustrated by showing simple examples of its application to product review sites.
... In order to provide the necessary means to handle imprecise and uncertain information in the Semantic Web there are today many proposals for fuzzy extensions to ontologies 18,19,20,21,22,23,24 . Also, how to construct fuzzy ontologies has increasingly attracted attention and several strategies for constructing fuzzy ontologies were proposed in 25,26,27,28,29,30,31 . Being similar to the requirement of handling imprecise and uncertain information in the context of the Semantic Web, the incorporation of imprecise and uncertain information in UML models has been an important topic of data modeling research because such information extensively exists in many real-world applications. ...
The success and proliferation of the Semantic Web depends heavily on construction of Web ontologies. However, classical ontology construction approaches are not sufficient for handling imprecise and uncertain information that is commonly found in many application domains. Therefore, great efforts on construction of fuzzy ontologies have been made in recent years. In particular, XML is imposing itself as a standard for representing and exchanging information on the Web, topics related to the modeling of fuzzy data have become very interesting in the XML data context. Therefore, constructing fuzzy ontologies from fuzzy XML data resources may make the existing fuzzy XML data upgrade to Semantic Web contents, and the constructed fuzzy ontologies may be useful for improving some fuzzy XML applications.This paper proposes a formal approach and an automated tool for constructing fuzzy ontologies from fuzzy XML data resources. Firstly, we propose a formal definition of fuzzy XML models (including the document structure fuzzy DTDs and the document content fuzzy XML documents). On this basis, we propose a formal approach for constructing fuzzy ontologies from fuzzy XML models, i.e., transforming a fuzzy XML model (including fuzzy DTD and fuzzy XML document) into a fuzzy ontology. Also, we give the proof of correctness of the construction approach, and provide a detailed construction example. Furthermore, we implement a prototype tool called FXML2FOnto, which can automatically construct fuzzy ontologies from fuzzy XML models. Finally, in order to show that the constructed fuzzy ontologies may be useful for improving some fuzzy XML applications, we focus on investigating how to reason on fuzzy XML models (e.g., conformance, inclusion, and equivalence) based on the constructed fuzzy ontologies, and it turns out that the reasoning tasks of fuzzy XML models can be checked by means of the reasoning mechanism of fuzzy ontologies.
... An intermediate step clusters documents into "similar" groups. De Maio et al [22] also use a threshold in their application of fuzzy FCA to create a semantic web ontology in OWL. Zhou et al [23] follow a similar approach, but use a membership "window" to select tuples from the fuzzy relation and convert to a crisp relation -that is, they take tuples whose membership is above a threshold but also below an upper limit. ...
Digital obesity, or information overload, is a widely recognized yet largely unsolved problem. Lack of metadata—that is, a useful and usable description of what is represented by data—is one of the fundamental obstacles preventing the wider use of computational intelligence techniques in tackling the problem of digital obesity. In this paper, we propose the use of fuzzy formal concept analysis to create simple taxonomies, which can be used to structure data and a novel form of fuzzy association rule to extract simple knowledge from data organized hierarchically according to the discovered taxonomies. The association strength is monitored over time, as data sets are updated. Feasibility of the methods is shown by applying them to a large (tens of thousands of entries) database describing reported incidents of terrorism. © 2010 Wiley Periodicals, Inc.
Two methodologies for formal concepts derivation are considered: the classical one, which focuses on the posterior analysis of the object’s properties of the studied knowledge domain, and non-classical, the cornerstone of which is the a priori formation of the set of measured object’s properties and the determination of existential relations on this set. In the article, firstly, a position is fixed in the technological chain of the target transformation of the source data, where the difference between considered methodologies shows itself. Secondly, the commonality of these two approaches is established in the aspect of the unity of their hypothetical-deductive basis. In this case, the cognitive activity of the subject is expressed first in a priori and then in a posteriori conceptual scaling of the measured properties. The work demonstrates the need for the joint use of the considered methodologies at processing incomplete and inconsistent empirical data about studied knowledge domain. The intermediate consolidation of these methodologies is possible only on the basis of multi-valued logic.
Formal Concept Analysis (FCA) is a rigorous mathematical theory in the “Data mining” research field. It advances the classical approach to the Concept as to a fundamental epistemic element which is determined by extent and intent. FCA is suitable for mining formal ontologies from the experimental data representing Domains of Interest (DI). In this sense Fuzzy FCA (FFCA) is an adaptation of the FCA to real nature of such information. The genesis study of fuzziness of the formal contexts is a new approach, which necessitates the inclusion of special stages of primary data processing into designing ontologies. It is shown that some of the reasons for this fuzziness are inherent in the technology of generating a formal context from experimental data. Other fuzziness factors were revealed during the morphological analysis of the basic empirical structure - the "objects-properties" table. Interpretation of additional information is possible on the basis of elementary methods of fuzzy inference. Lastly, variants of FFCA application for fuzzy ontologies are analyzed.
Understanding situations occurring within the physical world by analyzing streams of sensor data is a complex task for both human and software agents. In the area of situation awareness, the observer is typically overwhelmed by information overload and by intrinsic difficulties of making sense of spatially distributed and temporal-ordered sensor observations. Thus, it is desirable to design effective decision-support systems and develop efficient methods to handle sensor data streams. The proposed work is for the comprehension of the situations evolving along the timeline and the projection of recognized situations in the near future. The system analyzes semantic sensor streams, it extracts temporal pattern describing events flow and provides useful insights with respect to the operators’ goals. We implement a hybrid solution for situation comprehension and projection that combines data-driven approach, by using temporal extension of Fuzzy Formal Concept Analysis, and goal-driven approach, by using Fuzzy Cognitive Maps. The cloud-based architecture integrates a distributed algorithm to perform Fuzzy Formal Concept Analysis enabling to deal with deluge of sensor data stream acquired through a sensor-cloud architecture. We discuss the results in terms of prediction accuracy by simulating sensor data stream to early recognize daily life activities inside an apartment.
After evolutions of Semantic web the Ontologies become popularity in the resent years to symbolize knowledge, knowledge distribution, and knowledge management. Ontology represents core knowledge in various enterprises. In computing technology, ontology is a formal depiction of information has a set of notions within an area, and the associations between notions. In our proposed system we use DL ontology for the computational query decidability. In order to ensure this, numerous constraints were enacted. These constraints consent OWL DL to offer competent reasoning support. In our proposed system we develop personalized ontology and ontology log data. The Ontology Log Data are well efficient source of facts, returns the modification takes in the domain. The main impact of our proposed system is able to imply OWL DL ontology and SPARQL query responses user's preferred queries. The personalized ontology and ontology log data are validated in insurance sector concept, where the analyses are modularized particularly with GUI approach deliberately for affluence usage of consumer.
Based on the introduction in this chapter, it is shown that lots of fuzzy Description Logics (DLs) and fuzzy ontologies have been investigated in order to handle fuzzy information in real-world applications. In particular, fuzzy DLs and fuzzy ontologies are the acknowledged key of representing and reasoning with knowledge in the Semantic Web. In this chapter, we focus our attention on the recent research achievements on fuzzy extension approaches of DLs and ontologies based on fuzzy set theory, and we provide a brief review of them. We introduce several fuzzy DLs in detail, including the tractable fuzzy DL f-DLR-Lite F,∩ proposed in Cheng et al. (2008); the expressive fuzzy DLs f-ALC (Straccia 1998) and FDLR (Zhang et al. 2008c; Ma et al. 2011); and the fuzzy DLs with fuzzy data types f-SHOIN(D) (Straccia 2005b) and F-ALC(G) (Wang and Ma 2008). A fuzzy DL reasoner called FRESG supporting fuzzy data information with customized fuzzy data types G (Wang et al. 2009) is introduced. Also, a definition of fuzzy ontologies is given in order to provide a general understanding of fuzzy ontologies. All of these are of interest or relevance to the discussions of successive chapters. After reviewing most of the proposals of fuzzy extensions, it has been widely approved that fuzzy DLs and fuzzy ontologies could play a key role in the Semantic Web by serving as a mathematical framework for fuzzy knowledge representation and reasoning in applications. However, the researches on fuzzy DLs/ontologies are still in a developing stage and still the full potential of fuzzy DLs/ontologies has not been exhaustively explored. Some very important issues on fuzzy DLs/ontologies may be important in order for fuzzy DL/ontology technologies to be more widely adoptable in the Semantic Web and other application domains, such as extraction, mapping, query, and storage, which have increasingly received attention as will be discussed in detail in the later chapters.
With the requirement of fuzzy Description Logic (DL) and ontology extraction, it is necessary and meaningful to extract fuzzy DL and ontology knowledge from various data resources. In particular, with the widespread studies and the relatively mature techniques of fuzzy databases as introduced in Chap. 3, much valuable information and implicit knowledge in the fuzzy data models may be considered as the main data resources for supporting fuzzy DL and ontology extraction. Extracting fuzzy DL and ontology knowledge from the fuzzy data models may facilitate the development of the Semantic Web and the realization of semantic interoperations between the existing applications of fuzzy data models and the Semantic Web. In this chapter, we introduce how to extract fuzzy DLs and ontologies from several fuzzy data models, including fuzzy ER and UML conceptual data models, fuzzy relational and object-oriented logical database models. The existing numerous contributions of fuzzy data models provide the rich data resource support for the fuzzy knowledge management in the Semantic Web. Besides of the extraction techniques of fuzzy knowledge, for realizing the fuzzy knowledge management in the Semantic We, the other issues about mapping, query and storage of fuzzy DLs/ontologies also have been attracting significant attention as will be introduced in the later chapters.
This paper investigates a formal approach which supports a critically significant step in object oriented analysis and software engineering. It is proposed to create an object class structure model based on an Ontological Data Analysis. Pragmatically important attributes and Ontological Data Analysis basic stages review is given.
This book goes to great depth concerning the fast growing topic of technologies and approaches of fuzzy logic in the Semantic Web. The topics of this book include fuzzy description logics and fuzzy ontologies, queries of fuzzy description logics and fuzzy ontology knowledge bases, extraction of fuzzy description logics and ontologies from fuzzy data models, storage of fuzzy ontology knowledge bases in fuzzy databases, fuzzy Semantic Web ontology mapping, and fuzzy rules and their interchange in the Semantic Web. The book aims to provide a single record of current research in the fuzzy knowledge representation and reasoning for the Semantic Web. The objective of the book is to provide the state of the art information to researchers, practitioners and graduate students of the Web intelligence and at the same time serve the knowledge and data engineering professional faced with non-traditional applications that make the application of conventional approaches difficult or impossible.
Concept lattices are being used in the area of knowledge discovery and data mining. Since the information used to create a formal context may have some uncertainty associated with it, a variety of methods have been proposed to create fuzzy formal contexts and to transform these into fuzzy concept lattices. This paper reviews two of these methods to creating fuzzy concept lattices: the one-sided thresholding approach and the fuzzy closure operator approach. A simple example is presented to illustrate the differences between the two and then bioinformatics data, specifically using a gene annotation data file, is used to further compare the results from the two approaches.
Nowadays, a multitude of users benefits from social interactions, blogging, wiki in order to share their own contents with each other (i.e., user-generated content). In fact, both Web 2.0 and Enterprise 2.0 applications have changed the knowledge sharing paradigm, and have introduced enabling features to foster information flow among users. Nevertheless, the availability of large amount of information targeted to human employment highlights reusing, reasoning and exploitation of available knowledge. Emerging Semantic Web technologies enable to codify information in a machine understandable way. Therefore, the latest web development trend is devoted to combine Web 2.0 features with semantic technologies (e.g. semantic tagging, semantic wiki). This scenario raises new requirements in terms of knowledge base extraction, update and maintenance. To this end, this work defines an ontology-based knowledge management platform that integrates methodologies aimed at supporting the life cycle of large and heterogeneous enterprise's knowledge bases. In particular, the defined architecture relies on hybrid methodologies which apply computational intelligence techniques and Semantic Web technologies to support Knowledge Extraction, Ontology Matching and Ontology Merging.
In this paper, we present an unsupervised dependency-based approach to extract semantic relations to be applied in the context of automatic generation of multiple choice questions (MCQs). MCQs also known as multiple choice tests provide a popular solution for large-scale assessments as they make it much easier for test-takers to take tests and for examiners to interpret their results. Manual generation of MCQs is a very expensive and time-consuming task and yet they often need to be produced on a large scale and within short iterative cycles. We approach the problem of automated MCQ generation with the help of unsupervised relation extraction, a technique used in a number of related natural language processing problems. The goal of Unsupervised relation extraction is to identify the most important named entities and terminology in a document and then recognise semantic relations between them, without any prior knowledge as to the semantic types of the relations or their specific linguistic realisation. We use these techniques to process instructional texts and identify those facts (terminology, entities, and semantic relations between them) that are likely to be important for assessing test-takers’ familiarity with the instructional material. We investigate an approach to learn semantic relations between named entities by employing a dependency tree model. Our findings show that an optimised configuration of our MCQ generation system is capable of attaining high precision rates, which are much more important than recall in the automatic generation of MCQs. We also carried out a user-centric evaluation of the system, where subject domain experts evaluated automatically generated MCQ items in terms of readability, usefulness of semantic relations, relevance, acceptability of questions and distractors and overall MCQ usability. The results of this evaluation make it possible for us to draw conclusions about the utility of the approach in practical e-Learning applications.
The Higher Education Evaluation and Accreditation Council of Taiwan (HEEACT) supported by the Taiwan government has annually executed universities’ assessment process similar to the one in the United States since 2005. The HEEACT holds numerous assessment workshops and develops many basic standards to encourage each university to set its educational goals, measure the success in achieving these goals, ask some tough questions for the university, and meet these questions’ requirements to continuous improvement in the university. In this paper, we apply the fuzzy ontology and fuzzy markup language (FML) to perform the knowledge extraction for university governance and management, and adopt National University of Tainan (NUTN), Taiwan, as an example to construct the FML-based fuzzy ontology for knowledge management. In addition, the FML-based knowledge management system which processes a simulation for NUTN self-assessment and forecasts whether NUTN passes the 2011 HEEACT university assessment or not is also presented in this paper. Finally, NUTN fully passed 2011 university assessment in Taiwan and the results of our simulations are similar to the opinions of the HEEACT reviewers. It shows that the proposed fuzzy ontology and FML-based knowledge management system can work effectively for university governance and management.
In the web advertising domain, contextual advertising and sponsored search are two of the main advertising channels used to display related advertisements on web pages. A major challenge for contextual advertising is to match advertisements and web pages based on their semantics. When a web page and its semantically related advertisements contain many different words, the performance of the traditional methods can be very poor. In particular, there are few studies presented for Chinese contextual advertising that are based on semantics. To address these issues, we propose an ontology-based approach to Chinese semantic advertising. We utilize an ontology called the Taobao Ontology and populate it by automatically adding related phrases as instances. The ontology is used to match web pages and advertisements on a conceptual level. Based on the Taobao Ontology, the proposed method exploits seven distance functions to measure the similarities between concepts and web pages or advertisements. Then, the similarities between web pages and advertisements are calculated by considering the ontology-based similarities as well as term-based similarities. The empirical experiments indicate that our method is able to match Chinese web pages and advertisements with a relatively high accuracy. Among the seven distance functions, Cosine distance and Tanimoto distance show the best performance in terms of precision, recall, and F-measure. In addition, our method outperforms two contextual advertising methods, i.e., the impedance coupling method and the SVM-based method.
The wide diffusion of community tagging sites and related folksonomies has made the knowledge discovery and retrieval still much more urgent topic. If tagging systems allow users to add freely keywords to web resources, clicking on a tag has the side effect of a tag-based query, since enables the users to explore related content. The collective knowledge expressed though user-annotated data has a big potential, but needs to be filtered in a digest form so that the search result better reflects the users' preferences and actual aims. Starting from these considerations, our work presents an agent-based approach for a scalable semi-automatic generation of annotation tags, personalized on each user's preferences and tastes. Primary is the role of agents which assist users in the tagging activities as well as the retrieval of resources related to their interest. A user- friendly interface proposes an integrated one-shot view for interacting with a tagging system.
Semantic annotation is at the core of Semantic Web technology: it bridges the gap between legacy non-semantic web resource descriptions and their elicited, formally specified conceptualization, converting syntactic structures into knowledge structures, i.e., ontologies. Most existing approaches and tools are designed to deal with manual or semi-/automatic semantic annotation that exploits available ontologies through the pattern-based discovery of concepts. This work aims to generate the automatic semantic annotation of web resources, without any prefixed ontological support. The novelty of our approach is that, starting from web resources, content with a high-level of abstraction is obtained: concepts, connections between concepts, and instance-population are identified and arranged into an ex-novo ontology. The framework is designed to process resources from different sources (textual information, images, etc.) and generate an ontology-based annotation. A data-driven analysis reveals the data and their intrinsic relationships (in the form of triples) extracted from the resource content. On the basis of the discovered semantics, corresponding concepts and properties are modeled, allowing an ad hoc ontology to be built through an OWL-based coding annotation. The benefit of this approach is the generation of knowledge structured in a quite automatic way (i.e., the human support is restricted to the configuration of some parameters). The approach exploits a fuzzy extension of the mathematical modeling of Formal Concept Analysis and Relational Concept Analysis to generate the ontological structure of data resources.
Information overload arises from the increasing number of online users, the interaction between those users, and the resulting volume of data produced. One area in which this problem appears is product review sites. Text mining and opinion mining may be useful in distilling knowledge from such sites, but it is not always easy to summarise views or to track changes in views over time. We propose the use of fuzzy formal concepts to summarise features and sentiment orientations and a novel approach to measuring the distance between fuzzy lattices so that we can compare the summaries. By using this method, we aim to track changes in the concepts of product review over certain periods of time.
The relationships between consumer emotions and their buying behaviors have been well documented. Technology savvy consumers often use the web to find information on products and services before they commit to buy. We propose a semantic web usage mining approach for discovering periodic web access patterns from annotated web usage logs, which incorporate information on consumer emotions and behaviors through self reporting and behavioral tracking. We use fuzzy logic to represent real-life temporal concepts (e.g. morning) and requested resource attributes (ontological domain concepts for the requested URLs) of periodic pattern-based web access activities. These fuzzy temporal and resource representations, which contain both behavioral and emotional cues, are incorporated into a Personal Web Usage Lattice that models the user's web access activities. From this, we generate a Personal Web Usage Ontology written in OWL, which enables semantic web applications such as personalized web resources recommendation. Finally, we demonstrate the effectiveness of our approach by presenting experimental results in the context of personalized web resources recommendation with varying degrees of emotional influence. Emotional influence has been found to contribute positively to adaptation in personalized recommendation.
Much research in the use of concept lattices for knowledge discovery and data mining has occurred in the past several years. Various approaches have also been proposed to create fuzzy formal contexts and to transform these into fuzzy concept lattices. This paper first briefly reviews concept lattices and then presents several approaches to creating fuzzy concept lattices. One of these approaches is demonstrated with bioinformatics data, specifically using gene annotation data files. The evidence code specified with an annotation is translated into a numeric value in (0, 1] and is interpreted as the degree of association between the gene or gene product and the annotating Gene Ontology term. These degrees of association are used to create the fuzzy formal context which can then be used to create a fuzzy concept lattice.
Fuzzy concept lattices are being used as the basis for creating fuzzy ontologies. Fuzzy formal contexts serve as the starting point for which a variety of proposed methods have been used to create fuzzy concept lattices from them. This paper reviews two of these methods: the one-sided threshold approach and the fuzzy closure operator approach and presents the first comparison between these two approaches. Some simple examples are used and then bioinformatics data, specifically several gene annotation data files. The results show that the fuzzy closure approach produces huge numbers of concepts as compared to the threshold approach, and the extents produced by the threshold approach are a subset of the extents produced by the fuzzy closure approach.
Nowadays, in enterprise environments there is a wide and consolidated utilization of software for the human resource management providing functionalities like organizational management, personnel development, training event management, etc. that lay upon a competencies repository mostly populated through expensive and inefficient data entry activities. The new trends in Web 2.0 see a paradigm namely Enterprise 2.0, for supporting business activities within organizations. Web 2.0 is mainly exploited to sustain collaboration, information exchange and knowledge sharing. This work introduces an agent-based framework for the dynamic refinement of employees' competencies profiles by analyzing and monitoring collaborative activities executed through Enterprise 2.0 tools (e.g. corporate blogs, enterprise wikis, etc.). A fuzzy extension of Formal Concept Analysis model supports the elicitation of implicit knowledge and the content structuring into a conceptual representation. The resulting concept-based organization of initial user-generated content will be exploited to provide automatic hints to human resources (HR) managers in order to support them in making safer decisions that involve employees' competencies.
Nowadays, the emphasis on Web 2.0 is specially focused on user generated content, data sharing and collaboration activities. Protocols like RSS (Really Simple Syndication) allow users to get structured web information in a simple way, display changes in summary form and stay updated about news headlines of interest. In the e-Learning domain, RSS feeds meet demand for didactic activities from learners and teachers viewpoints, enabling them to become aware of new blog posts in educational blogging scenarios, to keep track of new shared media, etc. This paper presents an approach to enrich personalized e-learning experiences with user-generated content, through the RSS-feeds fruition. The synergic exploitation of Knowledge Modeling and Formal Concept Analysis techniques enables the definition and design of a system for supporting learners in the didactic activities. An agent-based layer supervises the extraction and filtering of RSS feeds whose topics are specific of a given educational domain. Then, during the execution of a specific learning path, the agents suggest the most appropriate feeds with respect to the subjects in which the students are currently engaged in.
This work proposes a system process for extracting automatically a fuzzy ontology from a collection of web resources. The approach exploits the Formal Concept Analysis theory for structuring the elicited knowledge, viz. concepts and relations embedded in the resources information. A simple graphical interface provides a multi-facets view which allows final users to navigate across the concepts and the relative population.
The two main approaches to fuzzy concept lattice creation are the one-sided threshold approach and the fuzzy closure approach.
These two methods are applied to gene annotation data files that are converted into fuzzy formal contexts by translating evidence
codes into a degree of evidence strength in [0, 1]. Fuzzy factor analysis is also applied to this same test data. These three
methods are briefly described and then compared based on their results using the gene annotation data files.
The huge growth of data on the Web and the requirement of semantic content analysis make the knowledge management and data mining very difficult activities. The knowledge elicitation, codification, and storage need not trivial techniques to improve formal information structuring on the Internet. Ontologies provide conceptualization and processing knowledge, sharing of consolidate understanding, reusing of domain knowledge codification for many Web applications. Manual construction of a domain-specific ontology is an intensive and time-consuming process, which requires an accurate domain expertise, because of structural and logical difficulties in the definition of concepts, as well as conceivable relationships. At the same time, the ontology visualization process requires similar endeavors to support ontology management, exploration, and browsing. This work describes an automatic method for ontology design from the content analysis of Web resources. The approach exploits a fuzzy extension of formal concept analysis model for structuring the elicited knowledge, viz. concepts and relations embedded in the resources content. Final result is an effective ontology visualization through a navigable, facet-based view of the built ontology across the extracted concepts and their own population. Furthermore, the approach proposes a simple labeling of ontology concepts through a sketched and intuitive process. © 2010 Wiley Periodicals, Inc.
As the Semantic Web gains importance for sharing knowledge on the Internet this has lead to the development and publishing of many ontologies in different domains. When trying to reuse existing ontologies into their applications, users are faced with the problem of determining if an ontology is suitable for their needs. In this paper, we introduce OntoQA, an approach that analyzes ontology schemas and their populations (i.e. knowledgebases) and describes them through a well defined set of metrics. These metrics can highlight key characteristics of an ontology schema as well as its population and enable users to make an informed decision quickly. We present an evaluation of several ontologies using these metrics to demonstrate their applicability.
In this paper we discuss the development and application of a large formal ontology to the semantic web. The Suggested Upper Merged Ontology (SUMO) (Niles & Pease, 2001) (SUMO, 2002) is a "starter document" in the IEEE Standard Upper Ontology effort. This upper ontology is extremely broad in scope and can serve as a semantic foundation for search, interoperation, and communication on the semantic web.
The field of BioInformatics has become a major venue for the development and application of computational ontologies. Ranging from controlled vocabularies to annotation of experimental data to reasoning tasks, BioOntologies are advancing to form a comprehensive knowledge foundation in this field. With the Glycomics Ontology (GlycO), we are aiming at providing both a sufficiently large knowledge base and a schema that allows classification of and reasoning about the concepts we expect to encounter in the glycoproteomics field. The schema exploits the expressiveness of OWL-DL to place restrictions on relationships, thus making it suitable to be used as a means to classify new instance data. On the instance level, the knowledge is modularized to address granularity issues regularly found in ontology design. Larger structures are semantically composed from smaller canonical building blocks. The information needed to populate the knowledge base is automatically extracted from several partially overlapping sources. In order to avoid multiple entries, transformation and disambiguation techniques are applied. An intelligent search is then used to identify the individual building blocks that model the larger chemical structures. To ensure ontological soundness, GlycO has been annotated with OntoClean properties and evaluated with respect to those. In order to facilitate its use in conjunction with other biomedical Ontologies, GlycO has been checked for NCBO compliance and has been submitted to the OBO website
The Semantic Web relies heavily on the formal ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. Therefore, the success of the Semantic Web depends strongly on the proliferation of ontologies, which requires fast and easy engineering of ontologies and avoidance of a knowledge acquisition bottleneck. Ontology Learning greatly facilitates the construction of ontologies by the ontology engineer. The vision of ontology learning that we propose here includes a number of complementary disciplines that feed on different types of unstructured, semi-structured and fully structured data in order to support a semi-automatic, cooperative ontology engineering process. Our ontology learning framework proceeds through ontology import, extraction, pruning, refinement, and evaluation giving the ontology engineer a wealth of coordinated tools for ontology modeling. Besides of the general framework and architecture, we show in thi...
Formal Concept Analysis (FCA), which is based on ordered lattice theory, is applied to mine association rules from web logs. The discovered knowledge (association rules) can then be used for online applications such as web recommendation and personalization. Experiments showed that FCA generated 60% fewer rules than Apriori, and the rules are comparable in quality according to three objective measures. Full Text at Springer, may require registration or fee
Our OntoLearn system is an infrastructure for automated ontology learning from domain text. It is the only system, as far as we know, that uses natural language processing and machine learning techniques, and is part of a more general ontology engineering architecture. We describe the system and an experiment in which we used a machine-learned tourism ontology to automatically translate multiword terms from English to Italian. The method can apply to other domains without manual adaptation.
Since a decade ago, a person-century of effort has gone into building CYC, a universal schema of roughly 105 general concepts spanning human reality. Most of the time has been spent codifying knowledge about the concept; approximately 106 common sense axioms have been handcrafted for and entered into CYC's knowledge base, millions more have been inferred and cached by CYC. This paper studies the fundamental assumptions of doing such a large-scale project, reviews the technical lessons learned by the developers, and surveys the range of applications that are enabled by the technology.
This paper describes a system for supporting the user in the discovery of semantic Web services, taking into account personal requirements. Goal is to model an ad-hoc service request by filtering semantic specifications rather than the exploitation of strict syntax formats. Adaptive agent-based techniques help the user to compose his Web service request, exploiting the semantic annotation of the browsed Web resources. This annotation reflects concepts or ontological terms that are relevant for the user services request formulation. Once the request is formulated, the system returns the list of semantic Web services that match the query input and output concepts.
In many research fields such as Psychology, Linguistics, Cognitive Science and Artificial Intelligence, computing semantic similarity between words is an important issue. In this paper a new semantic similarity metric, that exploits some notions of the feature-based theory of similarity and translates it into the information theoretic domain, which leverages the notion of Information Content (IC), is presented. In particular, the proposed metric exploits the notion of intrinsic IC which quantifies IC values by scrutinizing how concepts are arranged in an ontological structure. In order to evaluate this metric, an on line experiment asking the community of researchers to rank a list of 65 word pairs has been conducted. The experiment’s web setup allowed to collect 101 similarity ratings and to differentiate native and non-native English speakers. Such a large and diverse dataset enables to confidently evaluate similarity metrics by correlating them with human assessments. Experimental evaluations using WordNet indicate that the proposed metric, coupled with the notion of intrinsic IC, yields results above the state of the art. Moreover, the intrinsic IC formulation also improves the accuracy of other IC-based metrics. In order to investigate the generality of both the intrinsic IC formulation and proposed similarity metric a further evaluation using the MeSH biomedical ontology has been performed. Even in this case significant results were obtained. The proposed metric and several others have been implemented in the Java WordNet Similarity Library.
The experimental evidence accumulated over the past 20 years indicates that text indexing systems based on the assignment of appropriately weighted single terms produce retrieval results that are superior to those obtainable with other more elaborate text representations. These results depend crucially on the choice of effective termweighting systems. This article summarizes the insights gained in automatic term weighting, and provides baseline single-term-indexing models with which other more elaborate content analysis procedures can be compared.
This paper proposes a new fuzzy FCA-based approach to conceptual clustering for automatic generation of concept hierarchy on uncertainty data. The proposed approach first incorporates fuzzy logic into Formal Concept Analysis (FCA) to form a fuzzy concept lattice. Next, a fuzzy conceptual clustering technique is proposed to cluster the fuzzy concept lattice into conceptual clusters. Then, hierarchical rela- tions are generated among conceptual clusters for constructing the con- cept hierarchy. In this paper, we also apply the proposed approach to generate a concept hierarchy of research areas from a citation database. The performance of the proposed approach is also discussed in the paper.
In many research fields such as Psychology, Linguistics, Cognitive Science, Biomedicine, and Artificial Intelligence, computing
semantic similarity between words is an important issue. In this paper we present a new semantic similarity metric that exploits
some notions of the early work done using a feature based theory of similarity, and translates it into the information theoretic
domain which leverages the notion of Information Content (IC). In particular, the proposed metric exploits the notion of intrinsic IC which quantifies IC values by scrutinizing how concepts are arranged in an ontological structure. In order to evaluate
this metric, we conducted an on line experiment asking the community of researchers to rank a list of 65 word pairs. The experiment’s
web setup allowed to collect 101 similarity ratings, and to differentiate native and non-native English speakers. Such a large
and diverse dataset enables to confidently evaluate similarity metrics by correlating them with human assessments. Experimental
evaluations using WordNet indicate that our metric, coupled with the notion of intrinsic IC, yields results above the state
of the art. Moreover, the intrinsic IC formulation also improves the accuracy of other IC based metrics. We implemented our
metric and several others in the Java WordNet Similarity Library.
this paper will describe the terminology development process at NCI, and the issues associated with converting a description logic based nomenclature to a semantically rich OWL ontology
Term dependence is a natural consequence of language use. Its successful representation has been a long standing goal for Information Retrieval research. We present a methodology for the construction of a concept hierarchy that takes into account the three basic dimensions of term dependence. We also introduce a document evaluation function that allows the use of the concept hierarchy as a user profile for Information Filtering. Initial experimental results indicate that this is a promising approach for incorporating term dependence in the way documents are filtered.
DOI:10.1016/j.advengsoft.2006.07.003 Currently, the numerical simulation of flow and/or water quality becomes more and more sophisticated. There arises a demand on the integration of recent knowledge management (KM), artificial intelligence technology with the conventional hydraulic algorithmic models in order to assist novice application users in selection and manipulation of various mathematical tools. In this paper, an ontology-based KM system (KMS) is presented, which employs a three-stage life cycle for the ontology design and a Java/XML-based scheme for automatically generating knowledge search components. The prototype KMS on flow and water quality is addressed to simulate human expertise during the problem solving by incorporating artificial intelligence and coupling various descriptive knowledge, procedural knowledge and reasoning knowledge involved in the coastal hydraulic and transport processes. The ontology is divided into information ontology and domain ontology in order to realize the objective of semantic match for knowledge search. The architecture, the development and the implementation of the prototype system are described in details. Both forward chaining and backward chaining are used collectively during the inference process. In order to demonstrate the application of the prototype KMS, a case study is presented. Author name used in this publication: K. W. Chau
In this paper, a fuzzy ontology and its application to news summarization are presented. The fuzzy ontology with fuzzy concepts is an extension of the domain ontology with crisp concepts. It is more suitable to describe the domain knowledge than domain ontology for solving the uncertainty reasoning problems. First, the domain ontology with various events of news is predefined by domain experts. The document preprocessing mechanism will generate the meaningful terms based on the news corpus and the Chinese news dictionary defined by the domain expert. Then, the meaningful terms will be classified according to the events of the news by the term classifier. The fuzzy inference mechanism will generate the membership degrees for each fuzzy concept of the fuzzy ontology. Every fuzzy concept has a set of membership degrees associated with various events of the domain ontology. In addition, a news agent based on the fuzzy ontology is also developed for news summarization. The news agent contains five modules, including a retrieval agent, a document preprocessing mechanism, a sentence path extractor, a sentence generator, and a sentence filter to perform news summarization. Furthermore, we construct an experimental website to test the proposed approach. The experimental results show that the news agent based on the fuzzy ontology can effectively operate for news summarization.
Ontology is an effective conceptualism commonly used for the semantic Web. Fuzzy logic can be incorporated to ontology to represent uncertainty information. Typically, fuzzy ontology is generated from a predefined concept hierarchy. However, to construct a concept hierarchy for a certain domain can be a difficult and tedious task. To tackle this problem, this paper proposes the FOGA (fuzzy ontology generation framework) for automatic generation of fuzzy ontology on uncertainty information. The FOGA framework comprises the following components: fuzzy formal concept analysis, concept hierarchy generation, and fuzzy ontology generation. We also discuss approximating reasoning for incremental enrichment of the ontology with new upcoming data. Finally, a fuzzy-based technique for integrating other attributes of database to the ontology is proposed.
The National Cancer Institute's Thesaurus and Ontology, Web Semantics: Science, Services and Agents on the World Wide Web
- J Golbeck
- G Fragoso
- F Hartel
- J Hendler
- J Oberthaler
- B Parsia
J. Golbeck, G. Fragoso, F. Hartel, J. Hendler, J. Oberthaler and B. Parsia, The National Cancer Institute's Thesaurus and Ontology, Web Semantics: Science, Services and Agents on the World Wide Web, Volume 1, Issue 1, December 2003, Pages 75-80, ISSN 1570-8268.
Available: http://openlearn.open.ac
- Openlearn Project
Concept Mining of Semantic Web Services by Means of Fuzzy Formal Concept Analysis (FFCA)
- G Fenza
- V Loia
- S Senatore