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This paper fuses the techniques such as semantic network, the individuality service and agent, and references various research achievements of semantics Web on knowledge expression, RDF data manipulation and semantic retrieval, to propose an information retrieval model by combination of semantic with keyword based on
Database keyword search (DB KWS) has received a lot of attention in database research community. Although much of the research has been motivated by improving performance, recent research has also paid increased attention to its role in database contents exploration or data mining. In this paper we explore aspects
Image annotation becomes increasingly more important as the Web continues to grow. We propose a novel approach to enhancing keyword-based Web-image annotation in folksonomy, where a volunteer user is notified what kind(s) of keywords are necessary, and that keywords have been sufficiently provided by other volunteer
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
keyword search, meaning that the user needs to know the correct keywords before being able to retrieve the content of Quran. In this paper, we propose a system that supports the end user in querying and exploring the Quran ontology. The system comprises user query reformulation against the Quran ontology stored and annotated
conceptual model is well defined, a set of rules for keyword searching is created to verify preciseness of output produced. The rules created in this paper will be executed on Herbal Research E-Centre prototype.
Metacat to improve metadata search in multiple ways: (i) by expanding standard keyword searches with ontology term hierarchies; (ii) by allowing keyword searches to be applied to annotations in addition to traditional meta-data; and (iii) by allowing more structured searches over annotations via ontology terms. We describe
The development and maintenance of domain knowledge based system need a lot of manual operations, and with the increasing amount of contents in the system, it is more and more difficult to find the relevant information. The keyword based search usually can not return the accurate result. To solve these problems, this
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
This paper presents an overview of the emerging field of emotion detection from text and describes the current generation of detection methods that are usually divided into the following three main categories: keyword-based, learning-based, and hybrid recommendation approaches. Limitations of current detection methods
form of an ontology which represents the distinct areas of Software Engineering knowledge inspired by SWEBOK (Software Engineering Body of Knowledge). Finally, the process of the classification of texts within the ontology is carried out in three steps: keyword analysis, processing of the document. We believe our proposal
With the number of registered Web services growing, Identifying desired Web service is crucial for Web users. Current keyword based service search are inefficient in two main aspects: poor scalability and lack of semantics. Firstly ,the users are overwhelmed by the huge number of irrelevant services returned. Secondly
of ontology-aware keyword search of XML documents with a particular focus on EMR XML documents. Our current prototypes and experiments use the health level seven (HL7) clinical document architecture (CDA) Release 2.0 standard of EMR representation and the systematized nomenclature of human and veterinary medicine
information. Keyword based information retrieval technique helps in improving recall of user query result, but having low precision. To improve precision, we adopt semantic information retrieval technique. We are proposing architecture for semantic based information retrieval, in which plain text is read semantically and the
being unlike the keyword-based system that ignores the semantic relationships between words. Applying the proposed IR system to the given domain can make the managers of mobile communication companies retrieve the customers' complaints information more precisely and hence make decisions more reasonably
In recent years, there is global demand for Islamic knowledge by both Muslims and non-Muslims. This has brought about number of automated applications that ease the retrieval of knowledge from the holy books. However current retrieval methods lack semantic information they are mostly base on keywords matching approach
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
users to shift through and find relevant information. The information retrievals commonly used are based on keywords. These techniques used keyword lists to describe the content of information, but one problem with such list is that they do not say anything about the symantic relationships between keywords, nor do they
User modeling is a key technology in implementing personalized services. This paper tried to solve disadvantage of lack of semantic information of keyword, and designed a user profiles modeling method based on the category knowledge base, combining the keywords and the ontology concepts. In the model the user profiles
engines are keyword-matching mechanism-based, and the existing full-text query search engines are inadequate at retrieving relevant information from various oral queries. With only predefined words and sentence-based recommendations, a social robot may not suggest the correct items, if items retrieved along with the
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