Semantically Conceptualizing and Annotating Tables

Enabling a system to automatically conceptualize and annotate a human-readable table is one way to create interesting semanticweb content. But exactly “how?” is not clear. With conceptualization and annotation in mind, we investigate a semantic-enrichment procedure as a way to turn syntactically observed table layout into semantically coherent ontological concepts, relationships, and constraints. Our semanticenrichment procedure shows how to make use of auxiliary world knowledge to construct rich ontological structures and to populate these ontological structures with instance data. The system uses auxiliary knowledge (1) to recognize concepts and which data values belong to which concepts, (2) to discover relationships among concepts and which datavalue combinations represent relationship instances, and (3) to discover constraints over the concepts and relationships that the data values and data-value combinations should satisfy. Experimental evaluations indicate that the automatic conceptualization and annotation processes perform well, yielding F-measures of 90% for concept recognition, 77% for relationship discovery, and 90% for constraint discovery in web tables selected from the geopolitical domain.