Semantic similarity measurement based on knowledge mining: an artificial neural net approach

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.

Keywords:

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

Acknowledgement

This work was funded by the Earth Science Information Partnership (ESIP) Product and Service Committee and the Federal Geographic Data Committee (FGDC). The authors are grateful to Dr. Chaowei Yang, George Mason University, for providing his valuable comments on this research. The authors also thank Mr. Steve McClure for proofreading the article.