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.

Cross-lingual ontology matching as a challenge for the Multilingual Semantic Web

Recently, the Semantic Web has experienced significant advancements in standards and techniques, as well as in the amount of semantic information available online. Nevertheless, mechanisms are still needed to automatically reconcile information when it is expressed in different natural languages on the Web of Data, in order to improve the access to semantic information across language barriers. In this context several challenges arise [1], such as: (i) ontology translation/localization, (ii) cross-lingual ontology mappings, (iii) representation of multilingual lexical information, and (iv) cross-lingual access and querying of linked data. In the following we will focus on the second challenge, which is the necessity of establishing, representing and storing cross-lingual links among semantic information on the Web. In fact, in a “truly” multilingual Semantic Web, semantic data with lexical representations in one natural language would be mapped to equivalent or related information in other languages, thus making navigation across multilingual information possible for software agents.