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Artificial Intelligence for Biomedical Informatics
Published in Ranjeet Kumar Rout, Saiyed Umer, Sabha Sheikh, Amrit Lal Sangal, Artificial Intelligence Technologies for Computational Biology, 2023
Shahid Azim, Samridhi Dev, Sushil Kumar, Aditi Sharan
In order to deal with vocabulary mismatch problem of IR, Query expansion(QE) has been used by the researchers to improve retrieval efficiency. In most general way, query expansion involves selection of some source for providing expansion terms, selecting the appropriate terms for expansion based on similarity between the original and the candidate expansion terms, reformulating the query by weighting/ reweighting of original and expanded terms. Three main source options for selecting QE terms: (i) manually extracted knowledge resources inclusive of thesauri, dictionaries, and ontologies; (ii) the documents needed in the retrieval process; (iii) external text clusters and resources. Based on these sources query expansion can be categorized as knowledge based or corpus based. In biomedical domain, because of the availability of biomedical knowledge bases like Mesh, UMLS, Gene-ontology etc., mostly knowledge based query expansion has been performed by the researchers.
Research Trends on the Web
Published in Akshi Kumar, Web Technology, 2018
Previous work in the area of CIR has focused on three main themes: user profile modeling, query expansion, and relevance. User profile modeling: Focuses on exploiting the sources of evidence that more precisely include approaches to build the user profile that allows learning the user’s context by implicitly inferring the information from the user’s behavior and from external or local context sources.Query expansion: The query expansion approaches attempt to expand the original search query by adding further, new, or related terms. These additional terms are inserted into an existing query, either by the user (interactive query expansion, IQE) or by the retrieval system (automatic query expansion, AQE), with the intent to increase the accuracy of the search.Relevance feedback: The notion of relevance feedback (RF) is to take into account the results that are initially returned in response to the input query and provide a means for automatically reformulating a query to more accurately reflect a user’s interests.
Video Modeling and Retrieval
Published in Ling Guan, Yifeng He, Sun-Yuan Kung, Multimedia Image and Video Processing, 2012
Zheng-Jun Zha, Jin Yuan, Yan-Tao Zheng, Tat-Seng Chua
Basic text-based concept selection is based on the exact text matching between query and detector description [32–34]. For example, Snoek et al. [34] represented each detector description by a term vector, where each element corresponds to a unique normalized word. The vector space model [40] was then employed to compute the similarity between detector description and query words. Based on the resultant similarity, the concepts were selected automatically. Although exact text matching can achieve highly accurate selection results, its largest limitation is that it is not able to discover the potentially related concepts that do not explicitly appear in the query. To tackle this problem, some sophisticated text-based selection approaches have been developed [36,37]. They performed data-driven query expansion using external text resources and selected the related concepts for the expanded query. One drawback of these approaches is that the query expansion might introduce noisy terms and thus result in inappropriate concept selection.
Machine Learning Based Product Classification for eCommerce
Published in Journal of Computer Information Systems, 2022
Search phrase variants demonstrate process how user adjust terms from personal vocabulary to the product names (descriptions) or product classification until intended of search meet results. Product classification is a part of the research fields known as ontology and taxonomy matching. In taxonomy matching, data are annotated for relationship (not for meaning). If you are going to find an object in certain category, a matching algorithm has to establish the meaning in external data or in context within the schema. In contrast, the ontologies logical systems of axioms work for data annotation according to functional meaning.21 When classification is created in the ontology manner similar items are grouped according to the functionality. During a search process, user focuses on matching expectations with functionalities, however only felt by her/himself. That is why, we can state that search phrases links hidden variables on user site with products features represented by text objects (product names). The query expansion mechanisms can help searchers refine their queries by recommending additional or alternative search terms based on grouping functionalities. User-side automated assistance can improve searcher performance (as measured by the number of relevant documents found) by approximately 20%.22 Thus, effectiveness product classification should provide at least two major enhancements: a) recall the right products on the screen in a way that the collection contains as many products as possible but all responding to the customer’s request, and b) limit the match (precision), to the resulting subset of objects representing customer request with the minimal number of a missed objects.18 Thus, we formulate a research hypothesis as follows: H1: Supplementing product names as textual data that represent real object’s features and functionalities with UGC as the additional vocabulary of product description can significantly improve classification accuracy.
HQEBSKG: Hybrid Query Expansion Based on Semantic Knowledgebase and Grouping
Published in IETE Journal of Research, 2022
Mohammad Reza Keyvanpour, Zahra Karimi Zandian, Zahra Abdolhosseini
Using the Internet for different purposes has turned into one of the daily activities of almost all people all over the world, who research, shop, use applications and do many other things via the Internet [1]. Therefore, search engines have been popular to retrieve useful information and relevant ones based on user queries. Although search engine technologies have grown and achieved a lot of success, their performance results are still not encouraging. Low precision and recall in returned results or a high volume of results irrelevant to the query are examples of their shortcomings [2]. In this field, query expansion (QE) is one of the most important solutions to improve them. Query expansion is the process of refining the user query by adding new terms or reweighting query terms that describe the user intention or a query that is more likely to retrieve only the relevant documents [3]. QE is studied in the field of computer science, particularly within the scope of natural language processing, information retrieval (IR) [4] and data mining. Information Retrieval focuses on finding documents whose content matches with a user query from a large document collection [5]. Data mining is a process that uses data analysis tools to uncover and find patterns and relationships among data from different perspectives that may lead to extracting new information from a large database and summarizes them into useful information [6–8]. Specifically, QE helps users to find their required information [9–11]. Some typical reasons for using query expansion are as follows: Increasing the quality of search results particularly in terms of precision, recall and relevance measures [2].Helping the users create a good query that provides what the user really needs [2,12].Disambiguating the problems due to natural language processing and using a single word to express a concept [13].