Knowledge-level management of web information

Authors:
Seung Yeol Yoo;Achim Hoffmann
Affiliations:
School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
Venue:
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Year:
2005

Citing 8
Cited 1

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Word sense disambiguation in information retrieval revisited

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Discovery of Context-Specific Ranking Functions for Effective Information Retrieval Using Genetic Programming

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Semantic indexing using WordNet senses

RANLPIR '00 Proceedings of the ACL-2000 workshop on Recent advances in natural language processing and information retrieval: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 11

Query-topic focused web pages summarization

PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence

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Abstract

We present a knowledge-rich software agent, ContextExplicator, which mediates between the Web and the user's information or knowledge needs. It provides a method for incremental knowledge-level management (i.e., knowledge discovery, acquisition and representation) for heterogeneous information in the Web. In ContextExplicator, the incremental knowledge management works through iterative negotiations with the human user: Automatic Word-Sense Disambiguation and Induction. General knowledge (e.g., from a lexicon) and previously discovered knowledge support the sense-disambiguation & sense-induction of a word in the given documents, resulting in an improved and refined organization of previously discovered knowledge, Interactive Specialization of Query Criteria. At a given moment, the user can reduce certain semantic ambiguities of previously discovered knowledge by selecting one of the context-words which are suggested by ContextExplicator to discriminate between sets of retrieved documents. The selected context-word is also used to direct the discovery of new knowledge in the given documents, and Visualization of the Discovered Knowledge. The discovered knowledge is represented in a conceptual lattice. Each lattice-node represents a single word-sense or a conjunction of senses of multiple words. To each node the respectively identified documents are associated. Each web-document is multi-classified into relevant word-sense clusters (lattice nodes), according to the occurrences of specific word-senses in the respective web-document. As a conceptual lattice allows the user to navigate the word-sense clusters and the classified web-documents with multi-level abstractions (i.e., super-/sub-lattice nodes), it provides a flexible scheme for managing knowledge and web-documents in a scalable way.