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reports, etc. The system provides keyword-based and semantic-driven data matching methodology to extract the specific information from the textual clinical documents. The matching methodology provides the capabilities to recognize the selected keywords and the related semantics in the documents. Through the extraction
investigation about the improvements in the accuracy of a search system provided by network analysis techniques supporting the discovery of relations among the items stored in the repository. For this reason, we have developed the SEEN prototype, a keyword search tool exploiting network analysis. SEEN has been evaluated against a
Existing methods for Blog keyword extraction usually exploit the context in the specified blog. In this paper, we propose to provide a knowledge context by using small number of nearest neighbor blogs to improve keyword extraction performance. Specifically, knowledge context is build by adding several topic related
Textual web pages dominate web search engines nowadays. However, there is also a striking increase of structured data on the web. Efficient keyword query processing on structured data has attracted enough attention, but effective query understanding has yet to be investigated. In this paper, we focus on the problem of
This paper proposes a new keyword extraction method that uses bag-of-concept to extract keywords from Arabic text. The proposed algorithm utilizes semantic vector space model instead of traditional vector space model to group words into classes. The new method built word-context matrix where the synonym words will be
We introduce a question-answering system that responds to a keywords-query by extracting information from linked data and generating reports in natural language (NL). Using entity disambiguation and distributed word similarity, we matched each keyword to a related entity and property in linked data. To extract keyword
In this paper we describe the use of keyword extraction in a data management platform for the storage, publication, and sharing of scientific and engineering datasets primarily related to the stress of concrete structures under earthquake conditions. To improve discoverability of datasets and assist scientists who
Internet is becoming an increasingly important platform for ordinary life and work. It is expected that keyword extraction can help people quickly find hot spots on the web, since keywords in a document provide important information about the content of the document. In this paper, we propose to use text clustering
To bridge the semantic gap between low-level visual features and high-level semantic concepts, this paper puts forward a novel feedback mechanism which is based on both instance and keyword features. In offline part, keyword space model is first constructed and updated using manifold ranking annotation; in online
Keyword search is considered to be an effective information discovery method for both structured and semi-structured data. In XML keyword search, query semantics is based on the concept of Lowest Common Ancestor (LCA). However, naive LCA-based semantics leads to exponential computation and result size. In the
Keyword extraction is an automated process that collects a set of terms, illustrating an overview of the document. The term is defined how the keyword identifies the core information of a particular document. Analyzing huge number of documents to find out the relevant information, keyword extraction will be the key
the interfaces of web services, we make operations defined in WSDL files which compose web services as the base units for searching and organize all information of operations and corresponding components as documents, which will facilitate IR-Style keyword searching. In order to improve the precision of searching, we
Collection and analysis of information about network public opinion has currently become an effective means to get people thinking and recommendations by the government departments. In this paper, we presents a method of BBS(Bulletin Board System) hot topic analysis based on multiple keywords combination, this method
Text keywords at different semantic levels have different semantic representation abilities. Although words have been organized by semantic dictionaries (e.g. WordNet) with exact semantics, the dictionaries can not be constructed automatically by machine and there are still many words which are not included in the
Due to the huge number of research articles in the biomedical domain, it becomes more and more important to develop methods to find relevant articles of our specific research interests. Keyword extraction is a useful method to find important topics from documents and summarize their major information. Unfortunately
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
processes:- classification and tag selection. The classification process involves automatic keyword extraction using Rapid Automatic Keyword Extraction (RAKE) algorithm which uses the keyword — score matrix. The generated top scored keywords are added to the train dataset dynamically, which can be used further. This add
The content of a text is mainly defined by keywords and named entities occurring in it. In particular for news articles, named entities are usually important to define their semantics. However, named entities have ontological features, namely, their aliases, types, and identifiers, which are hidden from their textual
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
popularity and co-occurrence data. We describe a prototype that leverages the Wikipedia category structure to allow a user to semantically navigate pages from the Delicious social bookmarking service. In our system a user can perform an ordinary keyword search and browse relevant pages but is also given the ability to broaden
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