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.

Schema mapping produces a semantic correspondence between two schemas. Automating schema mapping is challenging. The existence of 1: n (or n :1) and n:m mapping cardinalities makes the problem even harder. Recently, we have studied automated schema mapping techniques (using data frames and domain ontology snippets) that not only address the traditional 1:1 mapping problem, but also the harder 1: n and n:m mapping problems. Experimental results show that the approach can achieve excellent precision and recall. In this paper, we share our experiences and lessons we have learned during our schema mapping studies.

Realizing the Semantic Web involves creating ontologies, a tedious and costly challenge. Reuse can reduce the cost of ontology engineering. Semantic Web ontologies can provide useful input for ontology reuse. However, the automated reuse of such ontologies remains underexplored. This paper presents a generic architecture for automated ontology reuse. With our implementation of this architecture, we show the practicality of automating ontology generation through ontology reuse. We experimented with a large generic ontology as a basis for automatically generating domain ontologies that fit the scope of sample natural-language web pages. The results were encouraging, resulting in five lessons pertinent to future automated ontology reuse study.

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Abstract. The semantic web represents a major advance in web utility, but it is difficult to create semantic-web content because pages must be semantically annotated through processes that are mostly manual and require a high degree of engineering skill. Furthermore, users need an effective way to query the semantic web, but any burden we place on users to learn a query language is unlikely to garner sufficient user support and interest. If we want users to take advantage of the semantic web, we must devise a means for transforming existing (non-semantic) web pages into semantic web pages, and we must provide a simple and unrestricted interface for processing user queries. We propose using information extraction ontologies to handle both of these challenges. We show how a successful ontology-based data-extraction technique can (1) automatically generate semantic annotations for ordinary web pages, and (2) support free-form, textual queries. Our approach demonstrates how the semantic web can be created for and used by ordinary people. We have created an initial prototype to demonstrate that our proposal works.