Tolerance Rough Set Based Attribute Extraction Approach for Multiple Semantic Knowledge Base Integration
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Summary
This paper presents an innovative approach which bases on extracting common class attributes and establishing unified category-attribute templates from online SKBs, and shows the effectiveness of the proposed approach.
- Type
- article
- Published
- 2011-11-21
- Cited by
- 2
- References
- 63
- OpenAlex
- https://openalex.org/W1989215773
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:45490133
Keywords
Computer science, Vagueness, Class (philosophy), Rough set, Set (abstract data type)
References
- A SEMANTIC WIKI FOR COLLABORATIVE KNOWLEDGE FORMATION
- Automatically Labeling Semantic Classes
- From Wikipedia to Semantic Relationships: a Semi-automated Annotation Approach
- Deriving a Large-Scale Taxonomy from Wikipedia
- What You Seek Is What You Get: Extraction of Class Attributes from Query Logs
- Integrating Cyc and Wikipedia: Folksonomy meets rigorously defined common-sense
- Organizing and Searching the World Wide Web of Facts - Step One: The One-Million Fact Extraction Challenge
- Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
- The Use of WordNet in Information Retrieval
- HLT-NAACL 2003 : Human Language Technology conference of the North American Chapter of the Association for Computational Linguistics: companion volume : short parers, student research workshop, demonstrations, tutorial abstracts : May 27 to June 1, 2003, Edmonton, Alberta, Canada
- Rough Sets and Data Mining: Analysis of Imprecise Data
- Snowball: extracting relations from large plain-text collections
- Rough Sets
- Instance-Based Question Answering: A Data-Driven Approach
- TOLERANCE APPROXIMATION SPACES
- A probabilistic approach for adapting information extraction wrappers and discovering new attributes
- Rough function model and rough membership function
- Rough membership functions
- Mining employment market via text block detection and adaptive cross-domain information extraction
- Internet encyclopaedias go head to head
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