Content-based ontology matching for GIS datasets

ABSTRACT

The alignment of separate ontologies by matching related concepts continues to attract great attention within the database and artificial intelligence communities, especially since semantic heterogeneity across data sources remains a widespread and relevant problem. In particular, the Geographic Information System (GIS) domain presents unique forms of semantic heterogeneity that require a variety of matching approaches.

Our approach considers content-based techniques for aligning GIS ontologies. We examine the associated instance data of the compared concepts and apply a content-matching strategy to measure similarity based on value types based on N-grams present in the data. We focus special attention on a method applying the concepts of mutual information and N-grams by developing 2 separate variations and testing them over GIS dataset including multi-jurisdictions. In order to align concepts, first we find the appropriate columns. For this, we will exploit mutual information between two columns based on the type distribution of their content. Intuitively, if two columns are semantically same, type distribution should be very similar. We justify the conceptual validity of our ontology alignment technique with a series of experimental results that demonstrate the efficacy and utility of our algorithms on a wide-variety of authentic GIS data.

REFERENCES

• 1: Thomas R. Gruber, A translation approach to portable ontology specifications, Knowledge Acquisition, v.5 n.2, p.199-220, June 1993 [doi>10.1006/knac.1993.1008]
• 2: Guillermo Nudelman Hess, Cirano Iochpe, Alfio Ferrara, Silvana Castano, "Towards Effective Geographic Ontology Matching," GeoS 2007, pp. 51--65.
• 3: William Sunna, "Multilayered Approach to Aligning Heterogeneous Ontologies", Ph.D. dissertation, University of Illinois at Chicago, 2007.
• 4: William Sunna and Isabel Cruz, "Structure-based Methods to Enhance Geospatial Ontology Alignment", Second International Conference on Geospatial Semantics, Mexico City, Mexico, November 2007.
• 5: Bing Tian Dai, Nick Koudas, Divesh Srivastava, Anthony K. H. Tung, and Suresh Venkatasubramanian, "Validating Multicolumn Schema Matchings by Type," 24th International Conference on Data Engineering (ICDE), pp. 120--129, 2008.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.4 Knowledge Representation Formalisms and Methods
          Subjects: Semantic networks

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.4 Knowledge Representation Formalisms and Methods
          Subjects: Representations (procedural and rule-based)

General Terms:
Algorithms, Design, Experimentation, Human Factors, Measurement, Reliability

Keywords:
dataset, geographic information systems, ontology, ontology alignment, schema matching