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(UDDI) was not designed to accommodate these emerging requirements. To solve the problems of storing QoS in UDDI and aggregating QoS values, three different approaches, namely type, keyword based and ontological approaches to model QoS tModel (technical model) have been proposed. The aim is to study these approaches and
used to replace keyword-based expertise profile to concept based expertise profile. An analysis of the prototype system indicates that ontology-based expertise matching supports users browsing and searching expertise more efficiently and effectively.
This paper presents a semantic-aware classification algorithm that can leverage the interoperability among semantically heterogeneous learning object repositories using different ontologies. The proposed algorithm is to map sharable learning objects, using meanings instead of just keyword matching, from heterogeneous
Access to scientific literature information is a very important, as well as time-consuming daily work for scientific researchers. Current methods of retrieval are usually limited to keyword-based searching using information retrieval techniques. In this paper, we present SemreX which implements efficient large-scale
With the popularity of Web services, how to discover suitable Web services to support Web services composition has become a challenge. Traditional keyword search is insufficient due to its lower recall and precision. This paper proposes an effective Web service discovery strategy based on Web services description
Inherent ambiguity of short keyword queries demands for enhanced methods for Web retrieval. In this paper we propose to have twice query expansion, the first query expansion is progressed by determining the relatedness between two word senses via structural and domain relatedness computation based on WordNet and
Systems to date have labels which are assigned by a person, e.g., tagging an object or a place with a keyword or phrase. Given some entities already labelled, a formal mechanism of generating labels using spatial context (detectable by sensors) is useful, not only to create new labellings without manual effort, but
The traditional retrieval system based on classification and keyword is difficult to satisfy the high quality retrieval requirement. A prototype of an intelligent retrieval system is provided, and the running process, reason and query of the system are discussed. Ontologies are introduced to describe the semantic
Extended semantic network is an innovative tool for knowledge representation and ontology construction, which looks for sets of associations between nodes semantically and proximally as opposed to the present method of keyword association. Our objective here is to achieve semi-supervised knowledge representation
A new method to compute the similarity of two blog posts is proposed in this paper. This method mainly has two parts including keywords extraction and semantic similarity measurement. During keywords extraction part, the method utilizes particular post features to extract keywords from one blog post with the aim to
The syntactic approach of most of Web search engines still has the drawback of not considering the semantics of the keywords entered by the user. So, users usually have to browse many hits looking for the information they want. In this paper, we present a system that, given a set of keywords with well defined
In this paper we propose a new technique allowing to map documents' keywords into relative distance space, which is based primarily on senses of these terms. We use WordNet ontology to retrieve multiple senses of keywords with the aim of generating multidimensional space for our data. The focus of this work is mainly
Current many information retrieval methods are based on purely keywords for representing the user needs. One of the main problems with this method is that it does not formally capture the explicit meaning of a keywords query, and ignores the documents that may be different in content but related with them. To improve
using keywords graph to contribute special techniques for exploring those groups and the relationships among them. Interactions between users and the created keywords graph are also provided. Compared to other applications on blog visualization, our approach utilized the ontology knowledge to analysis and automatically
Using automatic extraction of keywords and semi-automatic creation of domain ontologies it is possible to achieve a richer description of learning resources, with positive effects on subsequent searches. Based on this concept, we developed a repository named TREE - Teaching Resources for Engineering Education, to
search techniques. In this paper, we introduce an associated semantic network as the semantic representation model; use semantic keywords, a linguistic ontology in semantic similarity calculation and use learner relevance feedback to complete automatic semantic annotation. After several iterations of learner relevance
distributed inverted index by concept for documents. Based on them, semantic search is implemented with inference technique of Description Logic between concept descriptions of documents and query. Simulation experiments show that our approach is much higher on retrieval perform than traditional method of literal keywords
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