Discovery of ontologies from knowledge bases

Suryanto, Hendra; Compton, Paul

doi:10.1145/500742.500764

Cited by 7 publications

(7 citation statements)

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“…According to Maedche and Staab, there are as many ontology learning approaches as types of data sources [30]. We distinguish ontology learning approaches from texts, from dictionaries [31], from knowledge bases [32], from semi-structured schema [33] and relational data [34]. In this section, we are interested mainly in approaches related to ontology learning from web (including texts).…”

Section: Progress In Ontology Engineering Researchmentioning

confidence: 99%

Knowledge Harvesting for Business Intelligence

Mustapha

Aufaure

2013

Business Intelligence

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Section: Progress In Ontology Engineering Researchmentioning

confidence: 99%

Knowledge Harvesting for Business Intelligence

Mustapha

Aufaure

2013

Business Intelligence

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“…Many research work had been dedicated for building and maintaining ontologies: (a) ontology learning methodologies from dictionary, from text, from XML documents, or from knowledge base [6,10,17], (b) ontology merging methods of existing ontologies [14,13], (c) ontology re-engineering methods [8], and finally (d) ontology construction from scratch [7,6,9].…”

Section: Related Workmentioning

confidence: 99%

POEM: An Ontology Manager Based on Existence Constraints

Lammari

Mouza

Métais

2008

Lecture Notes in Computer Science

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Abstract. Whereas building and enriching ontologies are complex and tedious tasks, only few tools propose a complete solution to assist users. This paper presents POEM (Platform for Ontology crEation and Management) which provides a complete environment for ontology building, merge and evolution. POEM's algorithms are based on existence constraints and Boolean equations that allow to capture the semantic of concepts. POEM also integrates a semantic fusion that relies on WordNet and supports different storage formats. The different functionalities are implemented as a set of web services.

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“…For example, Becker [17] used formal concept analysis for this task, in the context of document retrieval. In similar work, Suryanto [18] used lattices for inferring user interests. Also similar is the work of Moldovan [13] who addresses the problem of building richer models of people's interests by relying on knowledge elicitation from the user and taxonomic representation of computer terms.…”

Section: Related Workmentioning

confidence: 99%

MECUREO ontology and modelling tools

Apted

Kay

2004

IJCEELL

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This paper describes MECUREO, a system that automatically constructs an ontology from a dictionary. We describe its use in the context of a dictionary of computer science. MECUREO parses this to construct a graph representing a lightweight ontology of computer science. This paper describes MECUREO's approach to parsing the dictionary and the construction of the ontology graph. We also describe MECUREO's point query facility. This generates a small ontology for use in a particular teaching context, such as single lesson. We report two evaluations of the effectiveness of the ontology construction, one based on a qualitative concept mapping experiment and the other a quantitative comparison of the computer science ontology with another similar ontology.

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Discovery of ontologies from knowledge bases

Cited by 7 publications

References 0 publications

Knowledge Harvesting for Business Intelligence

Knowledge Harvesting for Business Intelligence

POEM: An Ontology Manager Based on Existence Constraints

MECUREO ontology and modelling tools

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