lnu.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Adaptive Concept Vector Space Representation Using Markov Chain Model
2014 (Engelska)Ingår i: Proceedings of the International Conference on Knowledge Engineering and Knowledge Management, Springer International Publishing , 2014, s. 203-208Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper proposes an adaptive document representation (concept vector space model) using Markov Chain model. The vector space representation is one of the most common models for representing documents in classification process. The document classification based on ontology classification approach is represented as a vector, whose components are ontology concepts and their relevance. The relevance is represented the by frequency of concepts’ occurrence. These concepts make various contributions in classification process. The contributions depend on the position of concepts where they are depicted in the ontology hierarchy. The hierarchy such as classes, subclasses and instances may have different values to represent the concepts’ importance. The weights to define concepts’ importance are generally selected by empirical analysis and are usually kept fixed. Thus, making it less effective and time consuming. We therefore propose a new model to automatically estimate weights of concepts within the ontology. This model initially maps the ontology to a Markov chain model and then calculates the transition probability matrix for this Markov chain. Further, the transition probability matrix is used to compute the probability of steady states based on left eigenvectors. Finally, the importance is calculated for each ontology concept. And, an enhanced concept vector space representation is created with concepts’ importance and concepts’ relevance. The concept vector space representation can be adapted for new ontology concepts.

Ort, förlag, år, upplaga, sidor
Springer International Publishing , 2014. s. 203-208
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:lnu:diva-89054OAI: oai:DiVA.org:lnu-89054DiVA, id: diva2:1350219
Konferens
19th International Conference on Knowledge Engineering and Knowledge Management
Tillgänglig från: 2019-09-11 Skapad: 2019-09-11 Senast uppdaterad: 2019-09-11

Open Access i DiVA

Fulltext saknas i DiVA

Datavetenskap (datalogi)

Sök vidare utanför DiVA

GoogleGoogle Scholar

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 2 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf