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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
In the research field of XML retrieval with keyword-based approach, a variant of Lowest Common Ancestors (LCAs) have been widely accepted to provide how keywords are connected by ancestor relationship. However, returning a whole subtree or a partial subtree based on LCA nodes is insufficient for identifying how
Meaningful and useful return information is extraordinary important for information retrieval and XML keyword search. In this work, based on analysis the structure of XML document, we propose an algorithm to classify return matched nodes, we present formal analysis on LCA (lowest common ancestor) nodes ranking and LCA
Search engines are one of the most powerful tools in the Web world today for data retrieval and exploration. Most search engines identify the key word in the sentence or phrase or list of words given by the user and starts mining the Web for the occurrence of keyword in the Web pages. Quite often searching for the key
) Discipline Ontology is constructed, which is the formalization for concepts and the relationships between concepts existing in some discipline domain. OWL is adopted as Discipline Ontology description language; 2) Inference rules are defined on the basis of Discipline Ontology. Semantic extension on keyword from user is
In the age of Internet, with the online information explosive growth, people want to find information we need in the cyberworld fleetly and exactly. The information retrieval method based on the keyword or the simple logic-combination of the keywords has been unable to meet the people's need of information getting to
expression styles rather than their themes. Brief biography is a typical text class which differs from others in its genre. In order to differentiate biographical data from other texts quickly and effectively, we constructed two knowledge bases and compared their performances, i.e. topic keyword (content) and simple linguistic
task of ad hoc information retrieval is, finding documents within a corpus like Bible, that are relevant to the user remains a hard challenge. Sometimes the relevant documents may not contain the specified keyword. The lack of the given term in a document does not necessarily mean that the document is not a relevant
components rather than a single Database table. So to minimise the time constraint, memory space and to do a smart search a new IR system is introduced. In the proposed system, searches can be divided into three categorise, namely (i) Main topic search (ii) Subtitle search and (iii) Keyword search. So the system would search
explosion has became the main character of this age. Searching and making use of network information becomes more difficult. Therefore, automatically extraction on keyword is required. This paper uses the idea of classification to complete the task of Key-Phrase extraction, which uses SVM to build classification model and uses
The authors' information retrieval approach automatically extracts users' intentions when they interact with a device to access information, obviating the need for keyword inputs. The approach extracts these intentions by analyzing basic operations such as zooming, centering, and panning on a map, and applying them as
the definition questions, the sentences or paragraphs with higher relevance can be extracted to become the answer based on the relevance ranking between the candidate sentences or paragraphs and questions by combined the computing method of keywords weighting and the method of semantic similarity between the sentences
the information retrieval based on text keywords rather than temporal semantics. To address this issue, a Temporal semantics Information Retrieval (TSIR) System is proposed to deal with the Chinese temporal information retrieval. The TSIR system is deployed on Hadoop and implemented by the means of MapReduce. Firstly
how to eliminate ambiguity more easily and recommend more interested web pages to users. To resolve the above problems, we propose a novel mechanism named SSTAG, and it can recommend a set of Super-tags to users for their choices based on keywords input. As various topics related to the keywords, the Super-tags are
same concept. But the same type of classification is not successfully handled if it happens to be based on spatial keywords. This is due to the inherent ambiguity and uncertainty that is associated with the spatial terms found in natural language descriptions. Text documents imply the usage of natural language and as such
Traditional text retrieval techniques greatly consume system resources. Although some file-sharing software realizes file positioning and high-speed downloads, they have no enough capacity to analysis variety format Chinese documents and to extract keywords. At the same time, during the operation of system, it exist
keywords (descriptive terms), then we modify the ontology accordingly by adding the cluster's terms as semantic terms under the “SubSubSubconcept = lecture” to which these documents belong. This research is implemented and evaluated on a real platform HyperManyMedia at Western Kentucky University.
a series of Keywords. The main focus of this paper lies with matching of standard questions and questions asked by users. An experimental system based on the proposed method has been built, and the results of our experiments shows the proposed method is effective for question matching.
relations and bi relation patterns; TCMW-MODEL is used for acquiring the sets of the domain keywords in the traditional Chinese medicine. Our experimental results show the precision/recall of data extraction using this system is as good as those from the templates based on structured information extraction. The domain experts
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