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One of the challenging problem that Web service technology is now facing is effective service discovery. To solve the deficiencies of Web service description, matching and choosing under WSDL language, this paper presents a web service discovery method based on keyword clustering and concept expansion, mainly from the
more accurately and simply. RDF is a language for representing metadata. An enormous number of keywords on the semantic Web are very important to make practical applications of the semantic Web because most users prefer to search with keywords. In this paper, we classify queries with keyword conditions into three patterns
We present an ontological approach for interpreting natural language commands as well as an application to intelligent robots. In our approach, we first extract meaningful keywords from the user commands by adopting lexico-semantic pattern matching. A structured representation is then built by extracting and arranging
With the increasing growth in popularity of Web services, the discovery of relevant services becomes a significant challenge. In order to enhance the service discovery is necessary that both the Web service description and the request for discovering a service explicitly declare their semantics. Some languages and
suggest the ways that make and renew the ontology, which are related with the keywords that users input in the search engine, automatically for the automatic generation of ontology that is not limited by specific domain. Input keyword and relation keywords become OWL, and the relation among the created OWL is expressed by
schema based on Domain Repository has been proposed in this paper. Three steps: 1) Extract original keywords by word frequency statistics. 2) Match original keywords with concepts in Domain Repository in this page's field. 3) Increase the weight of words more related to this field then abstract its meaning to build the
semantics, automatically generates a set of formal queries, in the query language of the user's choice, which attempt to capture what the user had in mind when she or he wrote those keywords. The system uses ontologies and a description logics reasoner to perform a semantic enrichment of user keywords to improve the
Nowadays, with the appearance of more and more web services, it has been one of the key points that how to find the target service quickly and precisely. Traditional methods of web service discovery are only based on the keyword matching, but it's very difficult to realize more detailed and intelligent services, and
In the World-Wide Web context, availability of software components increases the possibility of applying a reuse approach in software development. Thus, component retrieval is a key problem, both for software industry and for end-users. Several problems should be solved: components formalisms are very different from one source to another, knowledge for guiding components retrieval is often poor and...
In the era of information explosion, information retrieval has become a bottleneck in information sharing and integration. However currently, the existing information retrieval methods are mainly based on keyword matching, which can not fully take advantage of the information context and potential knowledge. All of
information using natural language, by means of an ontology-based Question Answering (QA) system [14] and b) complements the specific answers retrieved during the QA process with a ranked list of documents from the Web [3]. Our results show that ontology-based semantic search capabilities can be used to complement and enhance
) Discipline Ontology is constructed, which is the formalization for concepts and the relationships between concepts existing in some discipline domain. OWL is adopted as Discipline Ontology description language; 2) Inference rules are defined on the basis of Discipline Ontology. Semantic extension on keyword from user is
This system proposes Indian-logic ontology based Context-aware Query Refinement model to support context-sensitive semantic search in keyword based search engine. This is by formulating effective query using Indian logic based Ontology for Context identification to overcome ambiguous query terms and increase the
technique that makes use of unified medical language system, an ontology knowledge source from National Library of Medicine. We compare our method with keyword-only approach, and our ontology-based method performs clearly better. Our method also shows potential to be used in other information retrieval areas
Assigning keywords to articles can be extremely costly. In this paper we propose a new approach to biomedical concept extraction using semantic features of concept graphs to help in automatic labeling of scientific publications. The proposed system extracts key concepts similar to author-provided keywords. We
This paper is started from addressing the common automatic method of ontology construction. Then, from viewpoint of the military intelligent processing, the two-level domain ontology architecture is designed. One level is the keyword ontology. The other level is the instance ontology. Different level has different
In today's fast-growing information age, currently available methods for finding and using information on the Web are often insufficient. Today's retrieval methods are typically limited to keywords searches or sub-string matches, therefore, users may often miss critical information when searching the Web. After
in a keyword-based photo retrieval process.We use metadata about the photo shot context (address location, nearby objects, season, light status...) to generate a bag of words for indexing each photo. We extend the Vector Space Model in order to transform these shot context words into document-vector terms. In addition
With the advent of Web 2.0, RESTful web services are becoming increasingly popular to emphasize the web as platform. There are already many RESTful web services and the number of services is increasing rapidly. Thus, it can be difficult to find specific services using keyword based retrieval. To solve this problem, a
This paper describes a new approach of enhancing textual document search and retrieval. The approach tries to take advantage of structured query languages in search and retrieval. For this purpose the semantic model of the document is created. The semantic model of the document is an ontology-like structured semantic
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