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Current techniques of knowledge management have some common defects in efficiency, scalability and applicability. Knowledge graph provides a new way for knowledge management and is a more flexible knowledge management method. Considering the specific features of crop diseases and pest data, this paper analyzed and classified the key techniques and...
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... relationships can also be represented by different types of edges. The structure of the knowledge map consisting of all the triples in Table 1 is shown in Figure 1. ...
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... main variables of environmental indicators are: average temperature, minimum temperature, average relative humidity, average rainfall, maximum rainfall, average sunshine hours, maximum soil moisture of 10 cm, average soil moisture of 20 cm, maximum soil temperature. The process of CBR is shown in Figure 10. Traditional reasoning based on logical rules promotes the automation process of reasoning for pest diagnosis and control measures to a certain extent, but it experiences obvious defects, such as insufficient learning ability, low data utilization rate, and inaccurate rate to be improved, which do not meet the requirements of practical application. ...
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... the processing results of requests are integrated, sorted, and recommended. The search engine framework based on knowledge map is shown in Figure 11. Blondet [80] proposed an advanced search engine based on knowledge mapping technology to search and list different types of biomedical entities, including genes, diseases, drugs, targets, and transcription factors associated with user queries with fast response. ...
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... answering system based on knowledge map maps the expression of natural language parsing into the vocabulary of knowledge map elements, which enhances the performance and expansibility of question answering system [81]. Figure 12 shows the flow chart of QA system based on knowledge map. ...
Citations
... However, reading the literature systematically and summarizing all the information as a whole also requires more time and effort. On the other hand, agricultural knowledge management methods have developed knowledge graph ontology technology [13]. Knowledge graphs have two advantages: First, because of the Linked Open Data (LOD) principle [14], which makes the data connected with other sources of knowledge; and second, due to the graph format, which enables the application of network analysis techniques [15]. ...
Background: Insect vectors spread 80% of plant viruses, causing major agricultural production losses. Direct insect vector identification is difficult due to a wide range of hosts, limited detection methods, and high PCR costs and expertise. Currently, a biodiversity database named Global Biotic Interaction (GloBI) provides an opportunity to identify virus vectors using its data. Objective: This study aims to build an insect vector search engine that can construct an virus-insect-plant interaction knowledge graph, identify insect vectors using network analysis, and extend knowledge about identified insect vectors. Methods: We leverage GloBI data to construct a graph that shows the complex relationships between insects, viruses, and plants. We identify insect vectors using interaction analysis and taxonomy analysis, then combine them into a final score. In interaction analysis, we propose Targeted Node Centric-Degree Centrality (TNC-DC) which finds insects with many directly and indirectly connections to the virus. Finally, we integrate Wikidata, DBPedia, and NCBIOntology to provide comprehensive information about insect vectors in the knowledge extension stage. Results: The interaction graph for each test virus was created. At the test stage, interaction and taxonomic analysis achieved 0.80 precision. TNC-DC succeeded in overcoming the failure of the original degree centrality which always got bees in the prediction results. During knowledge extension stage, we succeeded in finding the natural enemy of the Bemisia Tabaci (an insect vector of Pepper Yellow Leaf Curl Virus). Furthermore, an insect vector search engine is developed. The search engine provides network analysis insights, insect vector common names, photos, descriptions, natural enemies, other species, and relevant publications about the predicted insect vector. Conclusion: An insect vector search engine correctly identified virus vectors using GloBI data, TNC-DC, and entity embedding. Average precision was 0.80 in precision tests. There is a note that some insects are best in the first-to-five order. Keywords: Knowledge Graph, Network Analysis, Degree Centrality, Entity Embedding, Insect Vector
... It also enables the description of knowledge and the modeling of associative relationships between entities in the world through graphical models 11 . Big data management based on knowledge graph technology has the advantages of standardized expression, high correlation, and strong ability to be mined in depth, which can effectively query, discover and infer complex relationships between things and concepts from big data 12,13 , and has become an important paradigm for big data integration and analysis in many research fields of life sciences 14 , including intelligent retrieval of big data in agriculture and biology [15][16][17][18] , precision medical treatment 19 , intelligent bio-breeding 20, 21 , drug screening 22 , microbial colony-disease prediction 23,24 , and diagnosis of crop diseases and pests 13 . However, the application of knowledge map in pig intestinal microbiota and feed efficiency is still in the preliminary research stage. ...
... It also enables the description of knowledge and the modeling of associative relationships between entities in the world through graphical models 11 . Big data management based on knowledge graph technology has the advantages of standardized expression, high correlation, and strong ability to be mined in depth, which can effectively query, discover and infer complex relationships between things and concepts from big data 12,13 , and has become an important paradigm for big data integration and analysis in many research fields of life sciences 14 , including intelligent retrieval of big data in agriculture and biology [15][16][17][18] , precision medical treatment 19 , intelligent bio-breeding 20, 21 , drug screening 22 , microbial colony-disease prediction 23,24 , and diagnosis of crop diseases and pests 13 . However, the application of knowledge map in pig intestinal microbiota and feed efficiency is still in the preliminary research stage. ...
Feed efficiency (FE) is essential for pig production, has been reported to be partially explained by gut microbiota. Despite an extensive body of research literature to this topic, studies regarding the regulation of feed efficiency by intestinal microbiota remain fragmented and mostly confined to disorganized or semi-structured unrestricted texts. Meanwhile, structured databases for microbiota analysis are available, yet they often lack a comprehensive understanding of the associated biological processes. Therefore, we have devised an approach to construct a comprehensive knowledge graph by combining unstructured textual intelligence with structured database information and applied it to investigate the relationship between pig intestinal microbes and FE. Firstly, we created the pimReading knowledge base and the domain ontology of pig gut microbiota by annotating, extracting, and integrating semantic information from 157 scientific publications. Secondly, we created the pimPubtator by utilizing PubTator to expand the semantic information related to microbiota. Thirdly, we created the pimDatabase by mapping and combining the ADDAGMA, gutMGene, and KEGG databases based on the ontology. These three knowledge bases were integrated to form the Pig Intestinal Microbial Knowledge Graph (PIMKG). Additionally, we created three biological query cases to validate the performance of PIMKG. These cases not only allow us to identify microbes with the most significant impact on FE but also provide insights into the metabolites produced by these microbes and the associated metabolic pathways. This study introduces PIMKG, mapping key microbes in pig feed efficiency and guiding microbiota-targeted optimization.
... Qi et al. [11] proposed a method for constructing a Chinese meteorology and agriculture knowledge graph based on semistructured data. Liu et al. [12] described the application of crop disease and pest knowledge graphs in expert systems, search engines, and knowledge-based question-answering systems. Chen et al. [13] summarized the applications of multimodal knowledge graphs in agriculture, focusing on intelligent question answering, disease and pest recognition, and agricultural product recommendation research. ...
Currently, there is a wealth of data and expert knowledge available on monitoring agro-meteorological disasters. However, there is still a lack of technical means to organically integrate and analyze heterogeneous data sources in a collaborative manner. This paper proposes a method for monitoring agro-meteorological disasters based on a spatio-temporal knowledge graph. It employs a semantic ontology framework to achieve the organic fusion of multi-source heterogeneous data, including remote sensing data, meteorological data, farmland data, crop information, etc. And it formalizes expert knowledge and computational models into knowledge inference rules, thereby enabling monitoring, early warning, and disaster analysis of agricultural crops within the observed area. The experimental area for this research is the wheat planting region in three counties in Henan Province. The method is tested using simulation monitoring, early warning, and impact calculation of the past two occurrences of dry hot wind disasters. The experimental results demonstrate that the proposed method can provide more specific and accurate warning information and post-disaster analysis results compared to raw records. The statistical results of NDVI decline also validate the correlation between the severity of wheat damage caused by dry hot winds and the intensity and duration of their occurrences. Regarding remote sensing data, this paper proposes a method that directly incorporates remote sensing data into spatio-temporal knowledge inference calculations. By integrating remote sensing data into the regular monitoring process, the advantages of remote sensing data granted by continuous observation are utilized. This approach represents a beneficial attempt to organically integrate remote sensing and meteorological data for monitoring, early warning, and evaluation analysis of agro-meteorological disasters.
... ), and can effectively support knowledge retrieval and reasoning for different applications [10,11]. ...
A table is a convenient way to store, structure, and present data. Tables are an attractive knowledge source in various applications, including knowledge graph engineering. However, a lack of understanding of the semantic structure and meaning of their content may reduce the effectiveness of this process. Hence, the restoration of tabular semantics and the development of knowledge graphs based on semantically annotated tabular data are highly relevant tasks that have attracted a lot of attention in recent years. We propose a hybrid approach using heuristics and machine learning methods for the semantic annotation of relational tabular data and knowledge graph populations with specific entities extracted from the annotated tables. This paper discusses the main stages of the approach, its implementation, and performance testing. We also consider three case studies for the development of domain-specific knowledge graphs in the fields of industrial safety inspection, labor market analysis, and university activities. The evaluation results revealed that the application of our approach can be considered the initial stage for the rapid filling of domain-specific knowledge graphs based on tabular data.
... Google proposed the knowledge graph (KG) in 2012, and Hogan et al. [10] provided a comprehensive introduction to the knowledge graph. At the same time, many knowledge graph methods have sound application effects in various agricultural fields, such as agricultural knowledge services and pest diagnoses [11]. Chen et al. [12] proposed an Agricultural KG (AgriKG) for effectively integrating fragmented information generated using multiple applications for agricultural entity retrieval and agricultural knowledge Q&A. ...
This study proposes an improved link prediction model for predicting the "suitable for people" relationship within the knowledge graph of tea. The relationships between various types of tea and suitable target groups have yet to be fully explored, and the existing InteractE model still does not adequately capture a portion of the complex information around the interactions between entities and relationships. In this study, we integrate SENet into the feature layer of the InteractE model to enhance the capturing of helpful information in the feature channels. Additionally, the GCN layer is employed as the encoder, and the SENet-integrated InteractE model is used as the decoder to further capture the neighbour node information in the knowledge graph. Furthermore, our proposed improved model demonstrates significant improvements compared to several standard models, including the original model from public datasets (WN18RR, Kinship). Finally, we construct a tea dataset comprising 6698 records, including 330 types of tea and 29 relationship types. We predict the "suitable for people" relationship in the tea dataset through transfer learning. When comparing our model with the original model, we observed an improvement of 1.4% in H@10 for the WN18RR dataset, a 7.6% improvement in H@1 for the Kinship dataset, and a 5.2% improvement in MRR. Regarding the tea dataset, we achieved a 4.1% increase in H@3 and a 2.5% increase in H@10. This study will help to fully exploit the value potential of tea varieties and provide a reference for studies assessing healthy tea drinking.
... The knowledge base related to plant pests and diseases is still traditional and based only on plant experts, causing the information obtained to be limited and static [7], [8]. However, agricultural production and related studies of insect pests and plant diseases generate enormous volumes of data [9]- [13]. ...
... Applying knowledge graphs in smart agriculture involves converting fragmented agricultural big data into a comprehensive knowledge graph [23]. The knowledge graph contains diverse facets, including plant bug pests and diseases, plant variations, and smart agriculture applications [7], [24]. By visualizing links and interconnections within the knowledge graph, farmers and agricultural specialists can acquire valuable insights to enhance crop yields, manage resources efficiently, and understand the agricultural landscape [22]. ...
... Relationships are objective relationships between describing concepts, entities, and events. Examples of relationships are disease causes, control methods, selection, and application methods [7]. The construction of a knowledge graph for rice pests and diseases is a methodical procedure that entails collecting, arranging, and categorizing information in a format based on graphs [31]. ...
The agricultural industry in Indonesia confronts the simultaneous task of augmenting food production to satisfy escalating demand while proficiently handling crop losses caused by pests and diseases. This study introduces a novel approach that leverages knowledge graphs to transform traditional, expert-based knowledge into a dynamic and interconnected system for addressing rice pests and diseases. The process of constructing a knowledge graph consists of (1) domain extraction; (2) ontology design; (3) class definition; (4) property definition; (5) instance definition; and (6) build knowledge graph. The construction process entails retrieving data from the DBpedia through SPARQL queries, constructing a knowledge graph using an iterative approach, and designing ontologies. This involves defining essential classes like plants, pests, diseases, and pathogens, formalizing properties, and defining individuals. The ontology is transformed into a knowledge graph using Neo4J. Therefore, the knowledge graph will enhance decision-making and modeling processes in rice pests and diseases.
... To construct the Knowledge grid Top-down strategy is used Usefulness of the knowledge is determined, and the Integrity, precision and data quality is considered essential parameters of the knowledge grid. Liu xiaoxue et al. [4] presented the major analysis and Current examination of Information Grids for Harvest insects and Illnesses. It reviews constructing a knowledge grid to deduct crop diseases and pests through the expert structure. ...
... The knowledge graph (KG) method has achieved good application results in various agricultural fields, such as agricultural knowledge services and pest diagnosis [12]. Chen et al. [13] proposed an agricultural KG (AgriKG) for effective integration of fragmented information generated by many applications in the agricultural field, used for agricultural entity retrieval and agricultural knowledge Q&A. ...
... For relation r , the indicator tensor I in the meteorology KG can be sliced into r I , and its embedding matrix is represented by R . The crossentropy loss between the ground truth of interactions Y and the predicted value by the recommendation model based on Ripplenet is measured by the first term of Equation (12). According to the second term, the squared error between the ground truth of the meteorology KG r I and the reconstructed indicator matrix T E RE will be returned. ...
... as the following loss function for the recommendation model based on Ripplenet. See Equation(12) for details. ...
The rapid increase in the number of new maize varieties and the intensification of market competition have raised the need to precisely promote new maize varieties to suitable planting areas and fully exploit the variety potential and win the market competition. This paper proposes a precise recommendation method for suitable planting areas of maize varieties based on a knowledge graph. The meteorology knowledge graph of maize ecological regions is constructed at county-scale and a RippleNet recommendation model is used to mine the potential spatial correlation of maize variety suitability in different meteorological environments. The county-scale precise recommendation for suitable planting areas is then realized. In total, 331 maize varieties and agricultural meteorological data of 59 experimental areas in the Huang-Huai-Hai ecological region are used for model training and testing (accuracy 76.3%). Through experimental comparison, the recommendation accuracy of this method is 24.3% higher than that of six traditional machine learning methods, 11.2% higher than that of graph attention networks, and 5.8% higher than that of graph convolution neural networks. This study provides a data-driven solution for the precise recommendation and market positioning of maize varieties, enhances the scientificity of variety recommendation and helps to fully exploit their planting potential.
... JIA Zhonghao et al. proposed integrating the network embedding method into the feature extraction method and established a scenic spots recommendation system based on a tourism knowledge graph [2]. The authors of a different paper took tourism websites as the main data source, carried out the entity extraction and entity alignment of the heterogeneous data and constructed a Chinese tourism knowledge graph [3]. In order to construct the tourism knowledge graph, the authors proposed the GRU_ATT distantly supervised relation extraction model for text extraction [4]. ...
As an important infrastructure in the era of big data, the knowledge graph can integrate and manage data resources. Therefore, the construction of tourism knowledge graphs with wide coverage and of high quality in terms of information from the perspective of tourists’ needs is an effective solution to the problem of information clutter in the tourism field. This paper first analyzes the current state of domestic and international research on constructing tourism knowledge graphs and highlights the problems associated with constructing knowledge graphs, which are that they are time-consuming, laborious and have a single function. In order to make up for these shortcomings, this paper proposes a set of systematic methods to build a tourism knowledge graph. This method integrates the BiLSTM and BERT models and combines these with the attention mechanism. The steps of this methods are as follows: First, data preprocessing is carried out by word segmentation and removing stop words; second, after extracting the features and vectorization of the words, the cosine similarity method is used to classify the tourism text, with the text classification based on naive Bayes being compared through experiments; third, the popular tourism words are obtained through the popularity analysis model. This paper proposes two models to obtain popular words: One is a multi-dimensional tourism product popularity analysis model based on principal component analysis; the other is a popularity analysis model based on emotion analysis; fourth, this paper uses the BiLSTM-CRF model to identify entities and the cosine similarity method to predict the relationship between entities so as to extract high-quality tourism knowledge triplets. In order to improve the effect of entity recognition, this paper proposes entity recognition based on the BiLSTM-LPT and BiLSTM-Hanlp models. The experimental results show that the model can effectively improve the efficiency of entity recognition; finally, a high-quality tourism knowledge was imported into the Neo4j graphic database to build a tourism knowledge graph.
... The aspects of the issue that have been analyzed are represented as nodes along the tree. Logical conditions are used to characterize individual tree aspects, which are referred to as nodes [38]. The names of the features are what give the nodes of a tree their identity, while the alternative values that a feature might have been what give the edges their names, and the various classes are what give the leaves their names. ...
In strategic human resource management, one of the most critical issues to focus on is the correct selection and placement of people. Within the confines of this framework, the reason for the study that was conducted was to explore the machine learning approaches that proved to be the most effective in assisting with the recruitment of personnel and the assessment of their positions. To accomplish this goal, a in a series of tests involving workers in the public sector, categorization algorithms were used. The purpose of these tests was to determine which employees would be the ideal fit in which workstations and to determine how workers should be distributed. For supporting the decision support system, an algorithm model was created. Used in the process of recruiting and evaluating potential workers based on the results of the tests that were given. The most important results of this study support the idea that using the People's Evaluation for Recruitment and Promotion Algorithm Model (EERPAM) would make hiring and promoting people in a company fairer.
