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domain of knowledge management. In this paper we describe an industrial demonstration of fuzzy ontologies in information retrieval in the paper industry where problem solving reports are annotated with keywords and then stored in a database for later use. Furthermore, using Bellmann-Zadeh's principle to fuzzy decision
How to find the teaching resources according to users' demand quickly and accurately on the Internet is urgent to be solved. This paper proposes a design of pretreatment for keyword-based search over network teaching resource database based on ontology. Firstly, the teaching ontology is created according to the
conference sessions, and several other virtual and in-person activities. The resulting taxonomy comprises 455 terms arranged in 14 branches and six levels. This taxonomy was found to satisfy four criteria for validity and reliability: (1) keywords assigned to a set of abstracts were reproducible by multiple researchers, (2) the
The traditional layout of news websites, the combination of classified hierarchical browsing, headline recommendation and keyword-based search, has been used for many years. The keyword-based search is considered to be the most powerful tool for news browsing and retrieval. Unfortunately, the keyword-based query
Matching search technology based on query keyword has been widely used by traditional search way. It still belongs to pure keyword matching and can not acquire satisfactory search results. The essential reason is that traditional Web search lacks semantic understanding to user's search behaviors. In this study, we
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.
precision ratio and novelty ratio than that of web search engines. Based on case studies, we found that there are four main types of query suggestion within digital library environments, namely spelling suggestion, hot keyword suggestion, personalized suggestion and semantic suggestion. These approaches are, however, hardly to
Internets are important in everyone's life like searching keyword, college, social network and online shopping, when user using the internet for searching the keyword they getting some problem. That is when user searching for the keyword for some meaning but they will get different meaning for that keyword. Because
based on keyword indexing, there are many records in their result lists that are irrelevant to the user's information needs. It is shown that for retrieving more relevant and precise results, the following two points should be concerned: First of all, the query (either it is generated by a human or an intelligent agent
needed to search and find relevant information. For tabular structures embedded in HTML documents, typical keyword or link-analysis based search fails. The next phase envisioned for the WWW is automatic ad-hoc interaction between intelligent agents, web services, databases and semantic web enabled applications. A large
Traditional information gathering systems are mostly keyword-based that are lack of semantic comprehension and analysis ability and can't guarantee the comprehensiveness and accuracy of information gathering. This paper proposes Chinese patent information gathering model based on domain ontology, which can visualize
combine folksonomy, keyword and facet-based retrieval methods to retrieve software requirements related to users' interests. We add semantic ontology and users' feedback to obtain better software requirements that satisfy users' preferences to enhance software requirements retrieval performance. Finally, we demonstrate the
data in the relational database into ontology. Our system supports expressive, user-centric queries such as a structured query and a keyword query, as well as a visual query. This paper shares the lessons learned from our experience on lifelog management, data migration over different data models, and ontology design.
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
machines interacting with other machines to yield results which are user oriented and precise. A New Integrated Case And Relation Based Page Rank Algorithm have been proposed to rank the results of a search system based on a user's topic or query. This paper proposes an optimized semantic searching of keywords represent by
detail. The paper presents the similarity algorithm of domain keywords and common words respectively and integrates them into the question similarity. Experimental results show that the proposed method can achieve good performance and the system is applied.
In recent years, the application of ontology has been already toward the diversification under the development of the semantic Web technology. The main application of ontology is information retrieval. With the utilization of ontology, we expect to offer more correct information for users. Although, most of the applications of ontology are information retrieval but they lacks of the interaction with...
Current search engine performances need to be improved because often the result suggested by search engine are determine the popularity of a given page for its associated keywords but does not match specific user expectations. Previous researches have indicated that only 20% to 45% of the common search results are
they do well for keyword search strings such as "ocean'08 conference information", they are quite inadequate for searching against structured data such as "time- series ocean surface temperature or salinity levels in the Gulf of Mexico". Traditional search engines deploy various complex algorithms, take into account the
We introduce in this paper a system called EDGT, which determines the semantic relationships among Gene Ontology terms. EDGT accepts Keyword-based queries with the form Q (“t1”, “t2”, ‥, “tn”) and Loosely Structured queries with the form
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