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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 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
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
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
Keyword search over databases, popularized by keyword search in WWW, allows ordinary users to access database information without the knowledge of structured query languages and database schemas. Most of the previous studies in this area use IR-style ranking, which fail to consider the importance of the query answers
Precision queries of keyword search developed quickly over relational databases, but it can't be better to process fuzzy queries for satisfying higher requests of users. Aiming at fuzzy queries of numerical attributes for keyword-based search over relational databases, we give a new kind of membership function (normal
With the fast development of location-based services and geo-tagging, spatial keyword queries that retrieve objects satisfying both spatial and keyword conditions are gaining in prevalence. A hybrid index that integrates a spatial index (e.g., the R-tree or its variations) with a keyword filter offers a promising
There may exist a specific relationship between the keywords if an XML multi-keywords search has more than one answers. Such relationship can be speculated by SLCA. This paper proposes a user-friendly Top-k keywords searching approach based on the relationship of keywords. The SLCA of a keyword search is first
This study at first used the text mining method to analyze the keywords of the Chinese news reports related to Macan's gambling industry from June to September 2012. The study got 19 major keywords at the first step. In order to comprehend the influence of each keyword in each document, the study applied the Fruit Fly
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
Domain Assets are the domain knowledge constructed according to the common requirements in the domain. In order to reuse the domain assets effectively, a domain assets search algorithm is proposed in this paper. Compared with the keyword search, this algorithm is based on semantic similarity, and the domain assets
A novel text association rule approach FHAR algorithm is presented. To overcome the defect of traditional keywords which does not take into account the semantic relation between keywords, FHAR algorithm in the paper is based on concept vector. The density of semantic field and the weight of meaning are used to
some keywords, the search engine will return all the web pages related to the keywords in a reasonable order with some sort algorithm. In this process, retrieval results sort algorithm plays a significant role of user satisfaction. This paper starts from the background of sort algorithm, and introduces the history and
system (Fexpert) for a research university. Data were undertaken from three sources: (1) researcher's personal profile, (2) graduate school profile and (3) research project profiles. The data were preprocessed and clustered according to each expert's keywords using K-Means algorithm. The proposed system can be used to find
the linear space. BZ-tree is constructed using Z curve. BZ-tree is a balanced multi-branch tree. BZ-tree has the characteristic of B+-tree and R-tree. In BZ-tree, Z values of the points are the keywords, and all points are stored in leaf nodes, ordered by Z values. Using the reduction of the dimensionality of BZ-tree
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
Based on the research and analysis of interactive text properties, the word frequency statistics and synonyms merger are imported to obtain the keywords of interactive text. The Sentence similarity is used to describe the degree of coupling between sentences. Then a novel topic partition algorithm based on average
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.
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