At the heart of today's information-explosion problems are issues involving semantics, mutual understanding, concept matching, and interoperability. Ontologies and the Semantic Web are offered as a potential solution, but creating ontologies for real-world knowledge is nontrivial. If we could automate the process, we could significantly improve our chances of making the Semantic Web a reality. While understanding natural language is difficult, tables and other structured information make it easier to interpret new items and relations. In this paper we introduce an approach to generating ontologies based on table analysis. We thus call our approach TANGO (Table ANalysis for Generating Ontologies). Based on conceptual modeling extraction techniques, TANGO attempts to (i) understand a table's structure and conceptual content; (ii) discover the constraints that hold between concepts extracted from the table; (iii) match the recognized concepts with ones from a more general specification of related concepts; and (iv) merge the resulting structure with other similar knowledge representations. TANGO is thus a formalized method of processing the format and content of tables that can serve to incrementally build a relevant reusable conceptual ontology.

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Abstract. Valuable local information is often available on the web, but encoded in a foreign language that non-local users do not understand. Can we create a system to allow a user to query in language L1 for facts in a web page written in language L2? We propose a suite of multilingual extraction ontologies as a solution to this problem. We ground extraction ontologies in each language of interest, and we map both the data and the metadata among the language-specific extraction ontologies. The mappings are through a central, language-agnostic ontology that allows new languages to be added by only having to provide one mapping rather than one for each language pair. Results from an implemented early prototype demonstrate the feasibility of cross-language information extraction and semantic search. Further, results from an experimental evaluation of ontology-based query translation and extraction accuracy are remarkably good given the complexity of the problem and the complications of its implementation.

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