Mapping ontology vertices to a line using hypergraph framework

Zhu, Linli; Hua, Gang; Gao, Wei

doi:10.1016/j.ijcce.2020.04.001

Cited by 5 publications

(7 citation statements)

References 54 publications

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“…Most existing solutions analyze ontology lexical information using the text similarity method and the dictionary-based similarity method, such as LiLy [4], Ontology Mapping by Particle Swarm Optimisation (MapPSO) [14], UFOme, Integrated Learning In Alignment of Data and Schema (ILIADS) [15], and Automated Semantic Mapping of Ontologies with Validation (ASMOV) [16].…”

Section: A Analysis Of Present Situationmentioning

confidence: 99%

“…The string-based method [17] is original and frequently used, including the edition distancebased method [18], [19], the prefix/suffix similarity method, and the Jaro-Winler fraction method. The descriptive document-based method [4], [47] views an entity's relevant texts as the descriptive document, and determines whether entities are interrelated by processing the descriptive documents of different entities and performing similarity computations. The descriptive document-based method is related to lexical information and also involves the structural information of the entity [4], [19].…”

Section: A Analysis Of Present Situationmentioning

confidence: 99%

“…Section III.A showed that the problems described below are neglected in the similarity method adopted by many existing schemes and the S-Match method for computing lexical relation [4], [7]:…”

Section: B Challengesmentioning

confidence: 99%

“…Methods for representing the ontology graph include the RDF(S) graph [18], senses sub-graph, layered entity graph [15], [45], and directed/undirected graph. Schemes for computing similarity between nodes in the graph include similarity flooding [26], [44], graph matching [27], circuit model [4], Bayesian decision theory [28], Markov network [29], association rule paradigm [30], and so on.…”

Section: A Analysis Of Present Situationmentioning

confidence: 99%

“…The application systems can therefore share, reuse, and interoperate knowledge in a broader field by using these correspondences when transmitting heterogeneous information. Ontology matching is widely used in ontology engineering, biological medicine, P2P information sharing, Web service composition, and the semantic Internet of Things [4]. Ontology matching can be described as the function shown in Equation 1 and represented in Fig.…”

Section: Introductionmentioning

confidence: 99%

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Ontology Matching: State of the Art, Future Challenges, and Thinking Based on Utilized Information

et al. 2021

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Information used in existing ontology matching solutions are usually grouped into four categories: lexical information, structural information, semantic information, and external information, respectively. By summarizing and analyzing the approaches for utilizing the same kind of information, this paper finds that lexical information is mainly analyzed based on text and dictionary similarity. Similarly, structural information and semantic information are mainly analyzed based on graph structure and reasoner, respectively. The approaches for aggregating information analysis results are discussed. Challenges in the analysis of various types of information for existing ontology matching solutions are also described, and insights into directions for future research are provided.

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Section: A Analysis Of Present Situationmentioning

confidence: 99%

Section: A Analysis Of Present Situationmentioning

confidence: 99%

Section: B Challengesmentioning

confidence: 99%

Section: A Analysis Of Present Situationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ontology Matching: State of the Art, Future Challenges, and Thinking Based on Utilized Information

et al. 2021

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Dual hesitant fuzzy graphs with applications to multi-attribute decision making

Bai

Ding

Zhang

2020

International Journal of Cognitive Computing in Engineering

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Research on domain ontology construction based on the content features of online rumors

Zhao,

Liu,

Zhang

et al. 2024

Sci Rep

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Online rumors are widespread and difficult to identify, which bring serious harm to society and individuals. To effectively detect and govern online rumors, it is necessary to conduct in-depth semantic analysis and understand the content features of rumors. This paper proposes a TFI domain ontology construction method, which aims to achieve semantic parsing and reasoning of the rumor text content. This paper starts from the term layer, the frame layer, and the instance layer, and based on the reuse of the top-level ontology, the extraction of core literature content features, and the discovery of new concepts in the real corpus, obtains the core classes (five parent classes and 88 subclasses) of the rumor domain ontology and defines their concept hierarchy. Object properties and data properties are designed to describe relationships between entities or their features, and the instance layer is created according to the real rumor datasets. OWL language is used to encode the ontology, Protégé is used to visualize it, and SWRL rules and pellet reasoner are used to mine and verify implicit knowledge of the ontology, and judge the category of rumor text. This paper constructs a rumor domain ontology with high consistency and reliability.

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Mapping ontology vertices to a line using hypergraph framework

Cited by 5 publications

References 54 publications

Ontology Matching: State of the Art, Future Challenges, and Thinking Based on Utilized Information

Ontology Matching: State of the Art, Future Challenges, and Thinking Based on Utilized Information

Dual hesitant fuzzy graphs with applications to multi-attribute decision making

Research on domain ontology construction based on the content features of online rumors

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