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Using Context-Aware and Semantic Similarity Based Model to Enrich Ontology Concepts
2015 (English)In: Natural Language Processing and Information Systems, Springer International Publishing , 2015, p. 137-143Conference paper, Published paper (Refereed)
##### Abstract [en]

Domain ontologies are a good starting point to model in a formal way the basic vocabulary of a given domain. However, in order for an ontology to be usable in real applications, it has to be supplemented with lexical resources of this particular domain. The learning process of enriching domain ontologies with new lexical resources employed in the existing approaches takes into account only the contextual aspects of terms and does not consider their semantics. Therefore, this paper proposes a new objective metric namely SEMCON which combines contextual as well as semantic information of terms to enriching the domain ontology with new concepts. The SEMCON defines the context by first computing an observation matrix which exploits the statistical features such as frequency of the occurrence of a term, term’s font type and font size. The semantics is then incorporated by computing a semantic similarity score using lexical database WordNet. Subjective and objective experiments are conducted and results show an improved performance of SEMCON compared with tf*idf and $$\chi ^2$$.

##### Place, publisher, year, edition, pages
Springer International Publishing , 2015. p. 137-143
##### National Category
Computer Sciences
##### Identifiers
OAI: oai:DiVA.org:lnu-89060DiVA, id: diva2:1350239
##### Conference
International Conference on Applications of Natural Language to Information Systems
Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-09-11

#### Open Access in DiVA

No full text in DiVA

#### Search in DiVA

Kastrati, Zenun
##### On the subject
Computer Sciences

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#### Altmetric score

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Cite
Citation style
• apa
• ieee
• modern-language-association-8th-edition
• vancouver
• Other style
More styles
Language
• de-DE
• en-GB
• en-US
• fi-FI
• nn-NO
• nn-NB
• sv-SE
• Other locale
More languages
Output format
• html
• text
• asciidoc
• rtf