Paper Info
Title
Extracting Taxonomies from Data - A Case Study Using Fuzzy Formal Concept Analysis
Abstract
Taxonomies and, more generally, ontologies, are at the core of the semantic web. In practice, it is rare to find data with meta-data markup in accordance with a full ontology, due to the intensive manual effort involved in the production and maintenance of both the ontology and the data. In many cases, however, data is stored in XML documents or relational tables with implicit taxonomic information such as product type, location, business category, etc. In this work we aim to use methods from formal concept analysis (FCA) to extract such embedded taxonomies, as a starting point for creation of a formal ontology or for further processing of the data. Due to noise, data incompleteness, etc, a soft computing approach is necessary for all but the simplest cases.
Year
DOI
Venue
2009
10.1109/WI-IAT.2009.260
Web Intelligence/IAT Workshops
Keywords
Field
DocType
data incompleteness,formal ontology,fuzzy formal concept analysis,case study,extracting taxonomies,full ontology,meta-data markup,business category,formal concept analysis,intensive manual effort,embedded taxonomy,xml document,implicit taxonomic information,intelligent agent,xml,data mining,taxonomy,ontologies,soft computing,fuzzy systems,relational databases,semantic web
Ontology (information science),Ontology-based data integration,Data mining,Ontology,Information retrieval,Relational database,Computer science,Semantic Web,Formal ontology,Formal concept analysis,Markup language
Conference
Citations 
PageRank 
References 
2
0.44
4
Authors
2
Name
Order
Citations
PageRank
Andrei Majidian1252.98
Trevor Martin2455.66