ASMOV (Automated Semantic Matching of Ontologies with Verification) is a novel algorithm that uses lexical and structural characteristics of two ontologies to iteratively calculate a similarity measure between them, derives an alignment, and then verifies it to ensure that it does not contain semantic inconsistencies. In this paper, we describe the ASMOV algorithm, and then present experimental...

Semantic matching (SM) for textual information can be informally defined as the task of effectively modeling text matching using representations more complex than those based on simple and independent set of surface forms of words or stems (typically indicated as bag-of-words). In this perspective, matching named entities (NEs) implies that the associated model can both overcomes mismatch betwe...

An ontology typically provides a vocabulary that describes a domain of interest and a specification of the meaning of terms used in the vocabulary. Depending on the precision of this specification, the notion of ontology encompasses several data and conceptual models, for example, classifications, database schemas, fully axiomatized theories. Ontologies tend to be put everywhere. They are viewe...

Considering the existing problems in Web service such as lack of semantic information and interoperability, atomic services can not cooperate with each other and be combined into composite services. In this paper, based on extended UDDI, we present a semantic annotation framework for Web service description and composition. The novel service matching algorithm not only takes into account the he...

A decision procedure for a class of semantic matching problems was proposed in 3], but it yielded eeciency problems, principally because of redundancies. We present in this paper a new semantic matching algorithm for a restricted class of convergent rewrite systems, its theoretical properties, and an eecient implementation. This class of rewrite systems is particularly interesting for functiona...

The amount of machine oriented data on the web as well as the deployment of agent/Web Services are simultaneously increasing. This poses a service-discovery problem for client agents wishing to discover Web Services to perform tasks. We discuss a prototype mathematical service broker and look at an approach to circumventing the ambiguities arising from alternative but equivalent mathematical re...

Many AI tasks require determining whether two knowledge representations encode the same knowledge. For example, rule-based classification requires matching rule antecedents with working memory; information retrieval requires matching queries with documents; and some knowledge-acquisition tasks require matching new information with already encoded knowledge to expand upon and debug both of them....

Life service information plays an important role in people’s life, such as weather conditions, so the study of how to get life service information has important significance. This paper put forward a question processing method called “integrated semantic algorithm” in Q&A System of life service information. The new algorithm was based on the semantic web, word order similarity algorithm and the...

We present OWLS-MX3, the first adaptive hybrid semantic service matchmaker for OWL-S services. It learns how to best combine logic-based, text similarity and ontological structure matching for hybrid semantic selection of OWL-S services to given queries. OWLS-MX3 performs structural semantic matching to compensate for certain cases of text matching failures which are caused by the observed char...

Supersense tagging is a problem of finding a corresponding semantic super tag (eg. Phenomenon, Act) based on syntactic information and annotated corpora. However, we employ semantic information rather than syntactic one and annotated corpora, because Korean language has relatively flexible syntactic structure and is lack of annotated corpora. To construct the automatic sense tagging system for ...