Semantic similarity measurement based on knowledge mining

An artificial neural net approach

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

This article presents a new approach to automatically measure semantic similarity between spatial objects. It combines a description logic based knowledge base (an ontology) and a multi-layer neural network to simulate the human process of similarity perception. In the knowledge base, spatial concepts are organized hierarchically and are modelled by a set of features that best represent the spatial, temporal and descriptive attributes of the concepts, such as origin, shape and function. Water body ontology is used as a case study. The neural network was designed and human subjects' rankings on similarity of concept pairs were collected for data training, knowledge mining and result validation. The experiment shows that the proposed method achieves good performance in terms of both correlation and mean standard error analysis in measuring the similarity between neural network prediction and human subject ranking. The application of similarity measurement with respect to improving relevancy ranking of a semantic search engine is introduced at the end.

Original languageEnglish (US)
Pages (from-to)1415-1435
Number of pages21
JournalInternational Journal of Geographical Information Science
Volume26
Issue number8
DOIs
StatePublished - Aug 2012
Externally publishedYes

Fingerprint

neural network
ranking
Ontology
Semantics
semantics
Neural networks
ontology
Multilayer neural networks
Search engines
Error analysis
error analysis
logic
search engine
engine
Water
water
Experiments
experiment
prediction
performance

Keywords

  • geospatial knowledge discovery
  • geospatial semantics
  • ontology
  • ranking
  • search engine
  • semantic similarity
  • spatial data mining

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences

Cite this

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