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This paper proposes a systematic full text search on document using a combined keyword and structural similarity of documents under consideration. The approach operates in two steps. The first step uses a set of designated keywords to acquire potential desired documents by means of an open source tool. The second step
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
pyramid refers to publications that belong to a most-specific research topic. In this paper, we present elGiza, a research-pyramid based search tool for VLDLs. elGiza is equipped with (i) a research-pyramid-based Content-Based Search-Keyword Suggester that helps user develop search terms to reduce search failures, and (ii) a
At Petra Christian University Library, book searching engine system is using a common keyword matching as search queries. So, the user must enter the correct keyword and this case often takes a long time for users to find book that needed. Therefore, required an application that's capable display the search results
Google Scholar is one of the major academic search engines but its ranking algorithm for academic articles is unknown. In recent studies we partly reverse-engineered the algorithm. This paper presents the results of our third study. While the first study provided a broad overview and the second study focused on researching the impact of citation counts, the current study focused on analyzing the correlation...
library of XML documents which need to be ranked so they satisfy both the keyword and the tree structure constraints. This is a position paper which proposes combining existing vector space models (for keyword match) with tree isomorphism codes (for structural match).
In this paper, an intelligent concept based search engine has been presented that can be used as a multilingual platform for different search queries. It retrieves those results pages also which don't have directly the keywords but contains the synonyms or related words. In response to a query for the word “car
Libraries offer valuable resources to library patrons. Unfortunately, formulating library queries that match the rigid keywords chosen by the Library of Congress in library records to retrieve relevant results can be difficult. In solving this problem, we have developed a library search engine, called EnLibS, which
profiles five kinds of academic resources from four features including resource type, disciplinary distribution, keyword distribution and LDA topic distribution. After fusing user behaviors and resource profiles, the users' preferences are modeled. Finally, the top-N recommendation is made according to user's interest value
This thesis studies a user interest model which can correspond with every user by collecting some personal information of users and optimize the retrieval results by search the original query expansion automatically. After putting the inquiry keyword into the search engine, the system will filter and sort the
retrieval system. Given a document, a keyquery is a set of few keywords for which the document achieves a high relevance score. Keyqueries can hence be viewed as a general and concise description of the returned retrieval results. The keyquery framework addresses important problems of static classification systems: overlarge
In Digital Library (DL) system, users interact with the system to search for books or research papers. Users can search through metadata or search for information in the pages by querying using keywords. In both cases, a huge amount of results are returned; however, the relevant ones to the user are not often amongst
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.