01751nas a2200205   4500008004100000245009600041210006900137260000900206300001400215490000600229520107200235653001101307653001301318653002301331653001401354100001801368700001801386700002401404856011701428       2006                        eng d00aGraphOnto: OWL-based ontology management and multimedia annotation in the DS-MIRF framework0 aGraphOnto OWLbased ontology management and multimedia annotation  c2006  a214 - 2190 v43 aWe present here GraphOnto, a software component and an API that allow the definition and editing of both standard-based and domain OWL ontologies and their use in multimedia information system components. In our working environment an OWL Upper Ontology that captures the MPEG-7 MDS is utilized and OWL domain ontologies extend the upper ontology with domain knowledge. The ontologies imported in GraphOnto are parsed so that graphical ontology browsing and editing interfaces are automatically generated. The ontologies are used to guide the semantic multimedia annotation in a standardized manner. OWL Ontology mappings, expressed using OWL constructs, are supported and allow the definition of personalized ontologies. Personalization of the GraphOnto user interface on the application, task and user levels is provided. In addition, GraphOnto interoperates with an MPEG-7 Repository both for semantic entity reuse during multimedia content annotation and for semantic description storage. GraphOnto has been evaluated against Protégé and clearly outperforms it.10aMPEG 710aOntology10aOntology standards10aSemantics1 aPolydoros, P.1 aTsinaraki, C.1 aChristodoulakis, S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-33845937025&partnerID=40&md5=b72758db21f6febb306c3712ffb375fb01471nas a2200205   4500008004100000245014500041210006900186260000900255300001400264490000600278520073700284653001101021653003101032653001301063653001401076100002001090700001401110700002401124856011701148       2006                        eng d00aUser interactions with multimedia repositories using natural language interfaces - OntoNL: An architectural framework and its implementation0 aUser interactions with multimedia repositories using natural lan  c2006  a220 - 2260 v43 aWe present a generalized architectural framework for constructing and using natural language interfaces for interactions with multimedia repositories. The system allows the users to specify natural language queries about the multimedia content with rich semantics. It uses an extensive set of methodologies and tools for linguistic processing, and utilizes the MPEG-7 and the domain ontologies to reduce the ambiguities in the natural language and to rank the results. We describe the implementation of this framework for supporting interactions with a multimedia repository, described with the MPEG-7 MDS (Multimedia Description Schemes) structures, that also uses User Profile information for better ranking of the result queries.10aMPEG 710aNatural language interface10aOntology10aSemantics1 aKaranastasi, A.1 aZotos, A.1 aChristodoulakis, S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-33845925229&partnerID=40&md5=78be53ce109a805416d2c49e78d10de501899nas a2200229   4500008004100000245009100041210006900132260000900201300001400210490000600224520117600230653002101406653000901427653001301436653001401449653001201463100002101475700001601496700002001512700002001532856011701552       2006                        eng d00aX-Similarity: Computing Semantic Similarity between concepts from different ontologies0 aXSimilarity Computing Semantic Similarity between concepts from   c2006  a233 - 2370 v43 aSemantic Similarity relates to computing the similarity between concepts (terms) which are not necessarily lexically similar. We investigate approaches for computing semantic similarity by mapping terms to an ontology and by examining their relationships in that ontology. More specifically, we investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ontology) and between medical terms (using the MeSH ontology of medical and biomedical terms). The most popular semantic similarity methods are implemented and evaluated using WordNet and MeSH. The focus of this work is also on cross ontology methods which are capable of computing the semantic similarity between terms stemming from different ontologies (WordNet and MeSH in this work). This is a far more difficult problem (than the single ontology one referred to above) which has not been investigated adequately in the literature. X-Similarity, a novel cross-ontology similarity method is also a contribution of this work. All methods examined in this work are integrated into a semantic similarity system which is accessible on the Web.10aContent analysis10aMeSH10aOntology10aSemantics10aWordNet1 aPetrakis, E.G.M.1 aVarelas, G.1 aHliaoutakis, A.1 aRaftopoulou, P.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-33845952568&partnerID=40&md5=5bd213d7f778b7c4e7b32133f4b456f8