Yinghui Wang
ABSTRACT
Ontology, as a representation of shared conceptualization for variety of specific domains, is the core of the semantic web. Concept hierarchy is one of the most popular backbones of ontology which organizes the concepts according to hyponymy relationships, and stores massive entities as the instances of the concepts. An open concept hierarchy, e.g., Wikipedia, always needs to be constantly updated by adding new entities and concepts. In this paper, we propose an automatic solution for ontology update by inserting new entities and generating new concepts for concept hierarchy. The method only requires very limited information of new entity, i.e., the attributes of each entity. The solution is based on a hybrid strategy synthesizing the benefits from the structure of the concept tree and the content of the attributes. The content of the attributes is used to measure the similarity between an entity and a concept. The structure of the concept tree is used to determine which concepts need to be measured. During similarity measurement, the solution also synthesizes the statistical and rule-based factors. The effectiveness of the proposed method is verified by the experiments extending the Chinese and English Wikipedia concept hierarchy with new entities and concepts.
- Bryl, V., Giuliano, C., Serafini, L., Tymoshenko, K. 2010. Supporting Natural Language Processing with Background Knowledge: Coreference Resolution Case. (In 9th ISWC International Semantic Web Conference. Springer Berlin Heidelberg(2010),80-95. Google Scholar
Digital Library
- Xiang, C., Jiang, T., Chang, B., Sui, Z.: ERSOM. 2015. A Structural Ontology Matching Approach Using Automatically Learned Entity Representation. In Conference on Empirical Methods in Natural Language Processing.Google Scholar
- Euzenat, J., Shvaiko, P. 2007. (Ontology Matching. Springer, Heidelberg. Google ScholarDigital Library
- Shvaiko, P., Euzenat, J. 2013. Ontology matching: state of the art and future challenges. In IEEE Transactions on Knowledge & Data Engineering(2013). 25, 158-176. Google ScholarDigital Library
- Silva, N., Rocha, J. 2003. Semantic Web Complex Ontology Mapping. In Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference. IEEE Computer Society, Halifax (2003), 82-88. Google ScholarDigital Library
- Leacock C. and Chodorow M. 1998. Combining Local Context and WordNet Similarity for Word Sense Identification in WordNet. In C. Fellbaum, editor, WordNet: An electronic lexical database. MIT Press(1998), 265-283.Google Scholar
- Jiang, J. J., Conrath, D. W. 1997. Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy.11512--0.Google Scholar
- Do, H.H., Rahm, E. 2001. COMA - a system for flexible combination of schema matching approaches. In Proceedings of the Very Large Data Bases Conference (VLDB)(2001), 610--621. Google ScholarDigital Library
- Giunchiglia, F., Yatskevich, M. 2004. Element level semantic matching. In Proceedings of Meaning Coordination & Negotiation Workshop at Iswc (2004).Google Scholar
- Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems(2013). 26, 3111--3119. Google ScholarDigital Library
- Ngo, D. H., Bellahsene, Z. 2012. YAM++: a multi-strategy based approach for ontology matching task. In International Conference on Knowledge Engineering and Knowledge Management. Springer, Galway City(2012), 421-425. Google ScholarDigital Library
- Xiang, C., Jiang, T., Chang, B., Sui, Z.2015. ERSOM: A Structural Ontology Matching Approach Using Automatically Learned Entity Representation. In Conference on Empirical Methods in Natural Language Processing (2015).Google Scholar
- Ekbal, A., Sourjikova, E., Frank, A., Ponzetto, S. P.2010. Assessing the challenge of fine-grained named entity recognition and classification. In Named Entities Workshop. Stroudsburg (2010), 93--101. Google ScholarDigital Library
- Xiao, L., Weld, D. S. 2012. Fine-Grained Entity Recognition. In AAAI Conference on Artificial Intelligence. Toronto (2012). Google ScholarDigital Library
Index Terms
- Automatic Update of Ontology Concept Hierarchy with New Entity Insertion and New Concept Generation Based on Semantic Measurement
Recommendations
Concept ontology construction for sports video
A2CWiC '10: Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in IndiaOntology is a kind of concept model that describes system at the level of semantic knowledge. It aims to access domain knowledge in a general way and provides a common understanding of concepts in the domain so as to realize knowledge sharing and reuse ...
Concept Lattice-Based Semantic Web Service Matchmaking
ICCSN '10: Proceedings of the 2010 Second International Conference on Communication Software and NetworksMatchmaking is the basis of service discovery and composition. Using ontology semantically express the capabilities of services, accurately match, discovery and composition service. Domain ontologies and Formal Concept Analysis (FCA) aim at modeling ...
Video semantic concept detection using ontology
ICIMCS '11: Proceedings of the Third International Conference on Internet Multimedia Computing and ServiceSemantic concept detection in video is a challenge for video semantic content analysis. The performance of semantic concept detection methods depends on representing the video semantic content exactly. In this paper, perception concept and semantic ...
Comments