2007 OntologyMiningForSemanticInterpretation

• (Tao et al., 2007) ⇒ Xiaohui Tao, Yuefeng Li, Richi Nayak. (2007). “Ontology Mining for Semantic Interpretation of Information Needs.” In: Proceedings of KSEM.

Subject Headings: Ontology Mining, Library of Congress, Semantic Relation.

Quotes

Abstract

• Ontology is an important technique for semantic interpretation. However, the most existing ontologies are simple computational models based on only “super-” and “sub-class” relationships. In this paper, a computational model is presented for ontology mining, which analyzes the semantic relations of “part-of”, “kind-of” and “related-to”, and interprets the semantics of individual information need. The model is evaluated by comparing the knowledge mined by it, against the knowledge generated manually by linguists. The proposed model enhances Web information gathering from keyword-based to subject(concept)-based. It is a new contribution to knowledge engineering and management.

5. Related Work

• Much effort has been invested in ontology learning or mining for semantic interpretation. Staab & Studer [13] formally define an ontology as a 4-tuple of a set of concepts, a set of relations, a set of instances and a set of axioms. Slightly different, Maedche & Staab [9] have another slightly different definition which differentiates the relations to hierarchical and plain relations. Zhong [16] proposed a learning approach for task (or domain-specific) ontology, which employs various mining techniques and natural-language understanding methods.
• Li & Zhong [6] proposed an semi-automatic ontology learning method, in which a class is called compound concept assembled by primitive classes that are the smallest concepts and can not be divided any further. Navigli et al. build an ontology called OntoLearn [10] to mine the semantic relations amont the concepts from Web documents. Gauch et al. [3] used reference ontology and personalized user profile built based on the categorization of online portals and propsed to learn personalized ontology for users. However, their work does not specify the sematnic relationships of "part-of" and "kind-of" existing in the concepts but only "super-class" and "sub-class".
• Sing et al. [7] developed ConceptNet ontology and tried to specify common sense knowledge. However, ConceptNet does not count expert knowledge. Developed by King et al. [4], IntelliOnto is the one closest to the goal of facilitating the human user's concept model. It is built based on DDC system, and tries to describe the world knoweldge. Unfortunately, IntelliOnto covers only a limited number of concepts, which limintes the coverage of the world knowledge described.

6. Conclusions

• In this paper, a computational model is propose for ontology mining. Two novel concepts, specificity and exhaustivity, are introduced for analyzing the semantic relations in the ontology. The model aims (i) to discover knowledge from the ontology, and (ii) to help interpret the semantic meaning underlying from a user's information need.

References

• 3 Susan Gauch, Jason Chaffee, Alexander Pretschner. (2003). Ontology-based personalized search and browsing. Web Intelligence and Agent Systems 1(3-4): 219-234.
• 4 John D. King, Yuefeng Li, Xiaohui Tao, Richi Nayak. (2007). Mining world knowledge for analysis of search engine content. Web Intelligence and Agent Systems (WIAS) 5(3):233-253
• 6 Yuefeng Li, Ning Zhong. (2006). Mining Ontology for Automatically Acquiring Web User Information Needs. IEEE Trans. Knowl. Data Eng. (TKDE) 18(4):554-568.
• (Liu and Singh, 2004) ⇒ Hugo Liu, and Push Singh. (2004). “ConceptNet — A Practical Commonsense Reasoning Tool-Kit." BT Technology Journal, Springer.
• 9. Maedche Staab (2001). “Ontology learning for the Semantic Web
• 13 Steffen Staab, and Rudi Studer (eds.). (2004). “Handbook on Ontologies.” In: International Handbooks on Information Systems, Springer Verlag, http://www.aifb.uni-karlsruhe.de/WBS/sst/handbook/
• 16. Ning Zhong. (2002). “Representation and Construction of Ontologies for Web Intelligence.” In: International J. Found. Comput. Sci. (IJFCS) 13(4):555-570,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2007 OntologyMiningForSemanticInterpretation	Xiaohui Tao Yuefeng Li Richi Nayak			Ontology Mining for Semantic Interpretation of Information Needs		Proceedings of KSEM	http://www.springerlink.com/content/7j1p83w875641q06/			2007