Ontology-based semantic interpretation of cylindricity specification in the next-generation GPS

Cylindricity specification is one of the most important geometrical specifications in geometrical product development. This specification can be referenced from the rules and examples in tolerance standards and technical handbooks in practice. These rules and examples are described in the form of natural language, which may cause ambiguities since different designers may have different understandings on a rule or an example. To address the ambiguous problem, a categorical data model of cylindricity specification in the next-generation Geometrical Product Specifications (GPS) was proposed at the University of Huddersfield. The modeling language used in the categorical data model is category language. Even though category language can develop a syntactically correct data model, it is difficult to interpret the semantics of the cylindricity specification explicitly. This paper proposes an ontology-based approach to interpret the semantics of cylindricity specification on the basis of the categorical data model. A scheme for translating the category language to the OWL 2 Web Ontology Language (OWL 2) is presented in this approach. Through such a scheme, the categorical data model is translated into a semantically enriched model, i.e. an OWL 2 ontology for cylindricity specification. This ontology can interpret the semantics of cylindricity specification explicitly. As the benefits of such semantic interpretation, consistency checking, inference procedures and semantic queries can be performed on the OWL 2 ontology. The proposed approach could be easily extended to support the semantic interpretations of other kinds of geometrical specifications

Semantic Question Answering System over Linked Data using Relational Patterns

Hakimov S, Tunc H, Akimaliev M, Dogdu E. Semantic Question Answering System over Linked Data using Relational Patterns. In: EDBT/ICDT LWDM 2013. 2013.Question answering is the task of answering questions in naturallanguage. Linked Data project and Semantic Web communitymade it possible for us to query structured knowledge bases likeDBpedia and YAGO. Only expert users, however, with theknowledge of RDF and ontology definitions can build correctSPARQL queries for querying knowledge bases formally. In thispaper, we present a method for mapping natural languagequestions to ontology-based structured queries to retrieve directanswers from open knowledge bases (linked data). Our tool isbased on translating natural language questions into RDF triplepatterns using the dependency tree of the question text. Inaddition, our method uses relational patterns extracted from theWeb. We tested our tool using questions from QALD-2, QuestionAnswering over Linked Data challenge track and found promisingpreliminary results

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

MultiFarm: A benchmark for multilingual ontology matching

In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages – Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish – we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism

Guidelines to Study Differences in Expressiveness between Ontology Specification Languages: A Case Of Study

We focus on our experiences on translating ontologies between two ontology languages, FLogic and Ontolingua, in the framework of Methontology and ODE. Rather than building "ad hoc" translators between languages or using KIF, our option consists of translating through ODE intermediate representations. So, we have built direct translators from ODE intermediate representations to Ontolingua and FLogic, and we have also built reverse translators from these two languages to ODE intermediate representations. Expressiveness of the target languages is the main feature to analyse when automatically generating ontologies from ODE intermediate representations. Therefore, we analyse the expressiveness of Ontolingua and FLogic for creating classes, instances, relations, functions and axioms, which are the essential components in ontologies. The motivation for this analysis can be found in the (KA)² initiative and can be easily extended to any other domains and languages

How to Find Suitable Ontologies Using an Ontology-based WWW Broker

Knowledge reuse by means of outologies now faces three important problems: (1) there are no standardized identifying features that characterize ontologies from the user point of view; (2) there are no web sites using the same logical organization, presenting relevant information about ontologies; and (3) the search for appropriate ontologies is hard, time-consuming and usually fruitless. To solve the above problems, we present: (1) a living set of features that allow us to characterize ontologies from the user point of view and have the same logical organization; (2) a living domain ontology about ontologies (called ReferenceOntology) that gathers, describes and has links to existing ontologies; and (3) (ONTO)2Agent, the ontology-based www broker about ontologies that uses the Reference Ontology as a source of its knowledge and retrieves descriptions of ontologies that satisfy a given set of constraints. (ONTO)~Agent is available at http://delicias.dia.fi.upm.es/REFERENCE ONTOLOGY

Translating near-synonyms: Possibilities and preferences in the interlingua

This paper argues that an interlingual representation must explicitly represent some parts of the meaning of a situation as possibilities (or preferences), not as necessary or definite components of meaning (or constraints). Possibilities enable the analysis and generation of nuance, something required for faithful translation. Furthermore, the representation of the meaning of words, especially of near-synonyms, is crucial, because it specifies which nuances words can convey in which contexts.Comment: 8 pages, LaTeX2e, 1 eps figure, uses colacl.sty, epsfig.sty, avm.sty, times.st

A Word Sense-Oriented User Interface for Interactive Multilingual Text Retrieval

In this paper we present an interface for supporting a user in an interactive cross-language search process using semantic classes. In order to enable users to access multilingual information, different problems have to be solved: disambiguating and translating the query words, as well as categorizing and presenting the results appropriately. Therefore, we first give a brief introduction to word sense disambiguation, cross-language text retrieval and document categorization and finally describe recent achievements of our research towards an interactive multilingual retrieval system. We focus especially on the problem of browsing and navigation of the different word senses in one source and possibly several target languages. In the last part of the paper, we discuss the developed user interface and its functionalities in more detail