What can be done with the Semantic Web? An overview of Watson-based applications

Thanks to the huge efforts deployed in the community for creating, building and generating semantic information for the Semantic Web, large amounts of machine processable knowledge are now openly available. Watson is an infrastructure component for the Semantic Web, a gateway that provides the necessary functions to support applications in using the Semantic Web. In this paper, we describe a number of applications relying on Watson, with the purpose of demonstrating what can be achieved with the Semantic Web nowadays and what sort of new, smart and useful features can be derived from the exploitation of this large, distributed and heterogeneous base of semantic information

Using Cross-Lingual Explicit Semantic Analysis for Improving Ontology Translation

Semantic Web aims to allow machines to make inferences using the explicit conceptualisations contained in ontologies. By pointing to ontologies, Semantic Web-based applications are able to inter-operate and share common information easily. Nevertheless, multilingual semantic applications are still rare, owing to the fact that most online ontologies are monolingual in English. In order to solve this issue, techniques for ontology localisation and translation are needed. However, traditional machine translation is difficult to apply to ontologies, owing to the fact that ontology labels tend to be quite short in length and linguistically different from the free text paradigm. In this paper, we propose an approach to enhance machine translation of ontologies based on exploiting the well-structured concept descriptions contained in the ontology. In particular, our approach leverages the semantics contained in the ontology by using Cross Lingual Explicit Semantic Analysis (CLESA) for context-based disambiguation in phrase-based Statistical Machine Translation (SMT). The presented work is novel in the sense that application of CLESA in SMT has not been performed earlier to the best of our knowledge

Term Extraction and Disambiguation for Semantic Knowledge Enrichment: A Case Study on Initial Public Offering (IPO)

Domain knowledge bases are a basis for advanced knowledge-based systems, manually creating a formal knowledge base for a certain domain is both resource consuming and non-trivial. In this paper, we propose an approach that provides support to extract, select, and disambiguate terms embedded in domain specific documents. The extracted terms are later used to enrich existing ontologies/taxonomies, as well as to bridge domain specific knowledge base with a generic knowledge base such as WordNet. The proposed approach addresses two major issues in the term extraction domain, namely quality and efficiency. Also, the proposed approach adopts a feature-based method that assists in topic extraction and integration with existing ontologies in the given domain. The proposed approach is realized in a research prototype, and then a case study is conducted in order to illustrate the feasibility and the efficiency of the proposed method in the finance domain. A preliminary empirical validation by the domain experts is also conducted to determine the accuracy of the proposed approach. The results from the case study indicate the advantages and potential of the proposed approach

Data-driven Synset Induction and Disambiguation for Wordnet Development

International audienceAutomatic methods for wordnet development in languages other than English generally exploit information found in Princeton WordNet (PWN) and translations extracted from parallel corpora. A common approach consists in preserving the structure of PWN and transferring its content in new languages using alignments, possibly combined with information extracted from multilingual semantic resources. Even if the role of PWN remains central in this process, these automatic methods offer an alternative to the manual elaboration of new wordnets. However, their limited coverage has a strong impact on that of the resulting resources. Following this line of research, we apply a cross-lingual word sense disambiguation method to wordnet development. Our approach exploits the output of a data-driven sense induction method that generates sense clusters in new languages, similar to wordnet synsets, by identifying word senses and relations in parallel corpora. We apply our cross-lingual word sense disambiguation method to the task of enriching a French wordnet resource, the WOLF, and show how it can be efficiently used for increasing its coverage. Although our experiments involve the English-French language pair, the proposed methodology is general enough to be applied to the development of wordnet resources in other languages for which parallel corpora are available. Finally, we show how the disambiguation output can serve to reduce the granularity of new wordnets and the degree of polysemy present in PWN