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) preferences of the user with respect to a set of keywords. These preferences may then be used to rank the daily news, so that the user is recommended those items that match better with his/her interests. The cyclic preference learning methodology described in this paper is illustrated with a case example based on real news from
consideration the huge amount of data users and files that are present in the cloud, it is important that multiple keywords should be allowed in the searching request and retrieve the files relevant to those keywords. There are some methods and solutions offered to provide privacy and security for the data over the cloud server
This paper proposes a new methodology that automatically generates English mnemonic keywords to support the learning of basic Japanese vocabulary. A new phonetic algorithm, called JemSoundex, is also introduced for phonetically transliterating the Japanese and English languages for phonetic matching. The effective
fields and provides to the researchers the application form best matched to the researcher's current research field. We have developed recommendation system of Grant-in-Aid system for researchers by using JSPS (Japan Society for the Promotion of Science) keywords. The system can determine some rules associated between the
Search operations have become quite indispensable in recent days and loads of research are being organized to store and process the indices required for search operations in a simple and effective manner. Whenever indices are stored, the space it occupies and the ease of access are to be taken care of. This paper briefly deals with the existing system — the inverted index, and discusses the limitations...
In a real world, it is often in a group setting that sensitive information has to be stored in databases of a server. Although personal information does not need to be stored in a server, the secret information shared by group members is likely to be stored there. The shared sensitive information requires more security and privacy protection. To our best knowledge, there is no paper which deals with...
paper, algorithm is defined to improve relevancy of result based on webpage keyword ratio. In result analysis, result of proposed method is compared with deferent algorithms such as PageRank and Topic Distillation with Query Dependent Link Connections and Page Characteristics result.
In order to over the shortcoming of the incomprehensive of summarization, a new lexical-chain-based keywords extraction and automatic summarization algorithm from Chinese texts based on the unknown word recognition using co-occurrence of neighbor words is proposed in this paper, and an algorithm for constructing
In keyword search over relational databases (KSORD), retrieval of user's initial query is often unsatisfying. User has to reformulate his query and execute the new query, which costs much time and effort. In this paper, a method of automatically reformulating user queries by relevance feedback is introduced, which is
Keyword query applies to the database which accommodate structured data provide a search option over text attributes that uses a probability based ranking technique but query facing the issue of poor quality results. The keyword matches with multiple entities because the user does not provide exact data from which we
in the paper, a new model (MAK-Chord) is presented which is expanded from Chord. It generates fingerprints for each resource which include all the attribute keyword information and take into account the query frequency difference. It gives two different mappings between resources and nodes and effectively supports
Topic Detection is a sub-task of Topic Detection and Tracking, its main task is to find and organize topics that system didn't know. By analyzing hundreds of website news reports, we find that usually there exist some keywords in text, and early study didn't pay enough attention to this, we propose a topic detection
of text summarization is accurate identification of keywords from the given textual content. In this paper, the relative performance of three popular algorithms, namely TextRank, LexRank and Latent Semantic Analysis for keyword extraction were investigated by measuring their effectiveness in identifying keywords from
Keyword search is a user-friendly way to query XML data, such that users do not need to understand the complex syntax of structured query languages and the complex structural information of the underlying XML data. However, existing semantics suffer from limited expressiveness, thus users cannot obtain desired
In a time when volatile data is in constant growth, the importance of keyword extraction becomes particularly evident. Keywords can quickly identify, structure and reveal potentially worthwhile information. The quality of automatically extracted keywords reflects the individual characteristics of the various retrieval
Two keyword-extraction ways are usually used, one is simply using the information from exactly single word like word frequency and TF.IDF, the other is based on the relationship between words. The relationship is usually described as word similarity which derives from a corpus (WordNet, HowNet) or man-made thesaurus
propose a novel algorithm for keyword search in XML documents based on maximum repetitive unit. The basic idea of the algorithm is as follows. Firstly, extract the duplicate structures of XML documents as repetitive units. Then find out which units contain all the query keywords. The results returned are a number of
evaluate the collection of words and phrases to select set of keywords of the text. Next use the normal search engine to search the keywords set. Part of the search result will be used as seed links in focused crawler. Focused crawler's crawling policy is the best-first search policy, and this policy uses the similarity
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
integrate information from multiple interrelated pages to answer keyword queries meaningfully. Next-generation web search engines require link-awareness, or more generally, the capability of integrating correlative information items that are linked through hyperlinks. In this paper, we study the problems of identifying the
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