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The integration of the classical Web (of documents) with the emerging Web of Data is a challenging vision. In this paper we focus on an integration approach during searching which aims at enriching the responses of non-semantic search systems (e.g. professional search systems, web search engines) with semantic information, i.e. Linked Open Data (LOD), and exploiting the outcome for providing an overview...
Classical XML keyword search based on the Lowest Common Ancestor (LCA) framework requires users to be well versed with data and semantic relationships between the query keywords to extract meaningful response, restricting its applicability. GKS (Generic Keyword Search), on the other hand, allows users to browse and
Semantic and keyword web based technique is becoming a generic issue in an application of Information Retrieval (IR). Most of the researchers used different web techniques for finding relevant information and find the keyword based search, which are not able to fetch the relevant search result because they do not know
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
We propose a Discovery approach to find web services composition flows sorted by similarity. The approach extracts information from BPEL files. When creating new web services composition, the discovery result can be reused directly or provide reference. We import the lexical semantic in matching keywords. By analysis
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
It is truly said by Michelangelo Antonisoni that “We live in a society that compels us to go on using the concepts, and we no longer know what they mean”. A central challenge in understanding the vital concepts of a thesis lies in the complexity of jargons. Our goal is to build an interactive Web application coupled with a chat interface to make learning or knowledge gaining easier. Thus our Web application...
Image Re-Positioning System (NIRS) that automatically learns offline the various visual semantic features for different queries through keyword expansions. This novel approach significantly improves both accuracy as well as efficiency in image re-ranking for efficient image retrieval. Our experimental results show that
in an electronic health record (EHR) system, keyword search within the chart may produce many results that are not relevant or that may overlook related expressions and concepts entirely. In addition, some medical events, such as the occurrence of symptoms, are associated with important attributes such as location or
we introduce a self-designed semantic engine, performing sentiment analysis and semantic keyword extraction. These novel ontologies represent the mined information and thus, describe the users interest in automatic analyzed topics and map them to the meta data of items in recommendation engines.
As the size of experimental data, documents, and web pages grows larger, it will be difficult for scientists to search for appropriate data and grid web-services through keyword-based grid search portal interfaces. A well designed and dynamically updated knowledge base can help understand user queries when they are
Structural engineering experiment plays an important role in the civil infrastructure design and research. The diversity and heterogeneity of information representation among multiple experimental sites makes the experiment information integration difficult and lead to the poor accuracy when making the keyword
Previous multikeyword search in DHT-based P2P systems often relies on multiple single keyword search operations, suffering from unacceptable traffic cost and poor accuracy. Precomputing term-set-based index can significantly reduce the cost but needs exponentially growing index size. Based on our observations that 1
: the morphological, syntactic, and semantic levels. The basic unit of our search engine is the meaning not the word structure as structural research engines. It operates by keyword, subject, and syntactic analysis. We have developed a program that will be a search engine in the texts of the Holy Quran. ()
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
creative ideas to customers. In order to solve this problem, this paper presents algorithms to achieve customers' target. This project can be divided into three parts. The first part is to enrich and to analyse the input keywords by semantic web. The second part is to general raw ideas and relevant ideas by an inference
Images that used to characterize high-definition Images from web is very difficult task. So, In this paper we propose unique web Image re-ranking framework that offline and online learned Images visual and semantic meaning regarding with numerous query keywords. These visual and semantic meaning of Images extended to visual
Information retrieval becomes a very complex process for search engines on the Web, this is due to, first, the staggering growth speed of the number of web site and, in the other hand, the search algorithms by keywords (terms) used currently are not suitable to better exploit this huge information quantity. These
. 2) Creating rules for combining individuals and atoms of concepts. To overcome these, we propose an approach of extending SWRL by adding new keywords as OWL ontology classes and properties, and post-translating them using rewrite meta-rules. These internal and external enrichments of the concepts leads to hybrid
In this paper we present several content-based recommendation methods for a QA system that rely and use extensively the structure of a domain-specific taxonomy. Our goal is to add semantics to a typical content-based RS in order to improve the quality of the recommendations by mapping relevant keywords from the
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