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Abstract
In this paper, we present new Fuzzy Object Oriented Data model(F-model) and define an extended Fuzzy Association algebra(FA-algebra). F-model supports fuzzy classes and fuzzy associations between fuzzy objects. Several kinds of fuzzy attribute values are defined and expressed in databases. By the extended FA-algebra based on fuzzy association patterns, fuzzy queries with fuzzy values and linguistic hedges are processed.
... Referring to the correspondence of propositions 1 1 The restriction to propositions as unit of consideration is for reasons of simplicity only. with states of the real world, we can give the following de nitions: ...
Introducing features of natural language into query languages for databases increases their power and ergonomics. Therefore, theories are needed which formalise these features of natural language. Focusing on indeterminateness we discuss some existing theories, especially fuzzy logics and Dempster-Shafer-Theory, in order to develop a paradigm which allows for a direct comparison of different theories with respect to their formalisation of some central aspects of natural language. Key Words: Database Queries, Natural Language, Indeterminateness INTRODUCTION In the area of database applications, a tendency to improve query languages by introducing features of natural language such as vague concepts can be recognised (e.g. [1]). The notions of truth, possibility, indeterminateness, probability, and ignorance are further important concepts of natural language and human thinking (cf. [2], [3]). Many different theories have been developed for their formalisation (e.g. [3] to [8]). However...
RDF datasets can be queried using the SPARQL language but are often irregularly structured and incomplete, which may make precise query formulation hard for users. The SPARQL AR language extends SPARQL 1.1 with two operators — APPROX and RELAX — so as to allow flexible querying over property paths. These operators encapsulate different dimensions of query flexibility, namely approximation and generalisation, and they allow users to query complex, heterogeneous knowledge graphs without needing to know precisely how the data is structured. Earlier work has described the syntax, semantics and complexity of SPARQL AR , has demonstrated its practical feasibility, but has also highlighted the need for improving the speed of query evaluation. In the present paper, we focus on the design of two optimisation techniques targeted at speeding up the execution of SPARQL AR queries and on their empirical evaluation on three knowledge graphs: LUBM, DBpedia and YAGO. We show that applying these optimisations can result in substantial improvements in the execution times of longer-running queries (sometimes by one or more orders of magnitude) without incurring significant performance penalties for fast queries.
Graph data models provide flexibility and extensibility, which makes them well-suited to modelling data that may be irregular, complex, and evolving in structure and content. However, a consequence of this is that users may not be familiar with the full structure of the data, which itself may be changing over time, making it hard for users to formulate queries that precisely match the data graph and meet their information-seeking requirements. There is a need, therefore, for flexible querying systems
over graph data that can automatically make changes to the user’s query so as to find additional or different answers, and so help the user to retrieve information of relevance to them. This chapter describes recent work in this area, looking at a variety of graph query languages, applications, flexible querying techniques and implementations.
The increasing complexity of real applications in the field of
multimedia information systems requires the enhancement of modelling
capabilities of object oriented data models (OODMs) in order to deal
with imprecise and uncertain data. Some fuzzy extensions of the OODMs
have been proposed in the literature, in which imprecision and
uncertainty are managed at the level of object attributes and relations.
What is still lacking is a unifying and systematic formalization of
these extensions. In this contribution, starting from an existing
graph-based-object model, the authors propose a fuzzy object oriented
data (FOOD) model for the management of imprecise data
The real world data may have two types of ambiguity, one is in a data value itself and the other in an association between values. For representing such data, we propose a possibility-distribution fuzzy-relational model. In this model, the former ambiguity is represented by a possibility distribution and the latter by a grade of membership. The relational algebra for such fuzzy data model is defined. The traditional operations, namely, the union, intersection, difference and extended Cartesian product are similarly defined as those in fuzzy set theory. And the special relational operations, namely, the projection, join, restriction and division, are newly defined for fuzzy databases.
The paper presents an object-centered representation, where both a range of allowed values and a range of typical values can be specified for the attributes describing a class. These ranges may be fuzzy. Then various kinds of (graded) inclusion relations can be defined between classes. Inheritance mechanisms are discussed in this framework, as well as other kinds of reasoning tasks such as classification. the architecture of a software system implementing these ideas is outlined.
The authors present a method for coping with the uncertainty and
fuzziness that cause problems in handling information. As these two
concepts carry different semantic meanings, they distinguish between
them by representing fuzzy information as conjunctive fuzzy sets and
uncertainty by means of generalized fuzzy sets. An object-oriented
methodology is chosen because it offers an excellent framework for
handling uncertainty in a way completely transparent to the user. This
treatment of uncertainty in a fuzzy object-oriented environment is
well-suited to databases; hence examples from this field are selected.
Genealogical databases, where uncertain information is very common, and
where information is continuously updated, are used as a frame of
reference. The proposed model also allows storage of as much information
about uncertain objects as possible
One of the fundamental tenets of modern science is that a phenomenon cannot be claimed to be well understood until it can be characterized in quantitative terms.l Viewed in this perspective, much of what constitutes the core of scientific knowledge may be regarded as a reservoir of concepts and techniques which can be drawn upon to construct mathematical models of various types of systems and thereby yield quantitative information concerning their behavior.
The application of the object-oriented (O-O) paradigm in the database management field has gained much attention in recent years. Several experimental and commercial O-O database management systems have become available. However, the existing O-O DBMSs still lack a solid mathematical foundation for the manipulation of O-O databases, the optimization of queries, and the design and selection of storage structures for supporting O-O database manipulations. This paper presents an association algebra (A-algebra) to serve as a mathematical foundation for processing O-O databases, which is analogous to the relational algebra used for processing relational databases. In this algebra, objects and their associations in an O-O database are uniformly represented by association patterns which are manipulated by a number of operators to produce other association patterns. Different from the relational algebra, in which set operations operate on relations with union-compatible structures, the A-algebra operators can operate on association patterns of homogeneous and heterogeneous structures. Different from the traditional record-based relational processing, the A-algebra allows very complex patterns of object associations to be directly manipulated. The pattern-based query formulation and the A-algebra operators are described. Some mathematical properties of the algebraic operators are presented together with their application in query decomposition and optimization. The completeness of the A-algebra is also defined and proven. The A-algebra has been used as the basis for the design and implementation of an object-oriented query language, OQL, which is the query language used in a prototype Knowledge Base Management System OSAM*.KBMS
Two fuzzy database query languages are proposed. They are used to query fuzzy databases that are enhanced from relational databases in such a way that fuzzy sets are allowed in both attribute values and truth values. A fuzzy calculus query language is constructed based on the relational calculus, and a fuzzy algebra query language is also constructed based on the relational algebra. In addition, a fuzzy relational completeness theorem such that the languages have equivalent expressive power is proved
An Object-Oriented semantic association model(OSAM*)”, AI in Industrial Engineering and Manufacturing: Theoretical Issues and Applications
- S Y W Su
- V Krishnamurthy
- H Lam