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Journal articles on the topic 'Query expansion'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 January 2023

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1

He, Ben, and Iadh Ounis. "Combining fields for query expansion and adaptive query expansion." Information Processing & Management 43, no. 5 (September 2007): 1294–307. http://dx.doi.org/10.1016/j.ipm.2006.11.002.

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2

Grootjen, F. A., and Th P. van der Weide. "Conceptual query expansion." Data & Knowledge Engineering 56, no. 2 (February 2006): 174–93. http://dx.doi.org/10.1016/j.datak.2005.03.006.

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3

Azad, Hiteshwar Kumar, Akshay Deepak, and Kumar Abhishek. "Query Expansion for Improving Web Search." Journal of Computational and Theoretical Nanoscience 17, no. 1 (January 1, 2020): 101–8. http://dx.doi.org/10.1166/jctn.2020.8635.

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The aim of this paper is to retrieve the most relevant expansion words for expanding the initial query of the user in order to enhance the outcomes of web search results. Query expansion plays a major role in reformulating a user’s initial query to a one more pertinent to the user’s intended meaning. The reformulated query is then used to obtain more appropriate outcomes from a large amount of information on the web. The proposed semantic query expansion technique uses Wikipedia and WordNet as data sources. Wikipedia is taken as a base for all query expansions because it is one of the most diversified and relevant databases available on the web. To further improve the proposed query expansion technique,WordNet—a lexical database—is used as the as another data source because the synonyms (synsets) of the query term provided by it can be quite useful for query expansion. The proposed expansion technique successfully combines the two data sources to retrieve the most relevant expansion terms from the data sources in response to the user’s original query. The proposed work has been divided into four phases: (1) extraction of relevant words from Wikipedia (2) extraction of relevant words from WordNet (3) merging of the expansion terms obtained from Wikipedia and WordNet, and (4) query formulation by combining the expansion terms using Boolean operators. This reformulated query is then fired on the web to find the desired result. The Experimental result shows a significant improvement in information retrieval using query expansion.

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4

Harman, Donna. "Towards Interactive Query Expansion." ACM SIGIR Forum 51, no. 2 (August 2, 2017): 79–89. http://dx.doi.org/10.1145/3130348.3130357.

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5

Biancalana, Claudio, Fabio Gasparetti, Alessandro Micarelli, and Giuseppe Sansonetti. "Social semantic query expansion." ACM Transactions on Intelligent Systems and Technology 4, no. 4 (September 2013): 1–43. http://dx.doi.org/10.1145/2508037.2508041.

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6

Srinivasan, Padmini. "Query expansion and MEDLINE." Information Processing & Management 32, no. 4 (July 1996): 431–43. http://dx.doi.org/10.1016/0306-4573(95)00076-3.

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7

Pang, Wei, and Junping Du. "Query Expansion and Query Fuzzy with Large-Scale Click-through Data for Microblog Retrieval." International Journal of Machine Learning and Computing 9, no. 3 (June 2019): 279–87. http://dx.doi.org/10.18178/ijmlc.2019.9.3.799.

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8

Lopes, Lynette, and Jayant Gadge. "Hybrid Approach for Query Expansion using Query Log." International Journal of Applied Information Systems 7, no. 6 (July 4, 2014): 30–35. http://dx.doi.org/10.5120/ijais14-451204.

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9

KASAMWATTANAROTE, Siriwat, Yusuke UCHIDA, and Shin'ichi SATOH. "Query Bootstrapping: A Visual Mining Based Query Expansion." IEICE Transactions on Information and Systems E99.D, no. 2 (2016): 454–66. http://dx.doi.org/10.1587/transinf.2015edp7193.

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10

Nie, Jian-Yun. "Query expansion and query translation as logical inference." Journal of the American Society for Information Science and Technology 54, no. 4 (2003): 335–46. http://dx.doi.org/10.1002/asi.10214.

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11

Leal Bando, Lorena, Falk Scholer, and Andrew Turpin. "Query-biased summary generation assisted by query expansion." Journal of the Association for Information Science and Technology 66, no. 5 (July 18, 2014): 961–79. http://dx.doi.org/10.1002/asi.23222.

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12

Otair, Mohammed, Ghassan Kanaan, and Raed Kanaan. "Optimizing an Arabic Query using Comprehensive Query Expansion Techniques." International Journal of Computer Applications 71, no. 17 (June 26, 2013): 42–49. http://dx.doi.org/10.5120/12454-9244.

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13

Carstens, Carola, and Dorothea Mildner. "Query Reformulation Behavior in an Interactive Query Expansion Environment." Datenbank-Spektrum 11, no. 3 (October 8, 2011): 161–72. http://dx.doi.org/10.1007/s13222-011-0069-z.

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14

Yin, Jie, and Wei Ran Xu. "Query Expansion Associated with Clustering." Applied Mechanics and Materials 441 (December 2013): 647–50. http://dx.doi.org/10.4028/www.scientific.net/amm.441.647.

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The aim of the system is to achieve query expansion. The method works combining clustering of hierarchical methods. Through the information proffered by a background document, doc_ID, concerning the initial query, a cluster containing doc_ID can be produced by hierarchical clustering. And the word co-occurrence information can be extracted from the candidate documents in this cluster. Compared with the content in doc_ID, the result of the experiment using the system shows an expected performance to develop expanded terms which are qualified to add to original query.

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15

Sharma, Dilip Kumar, Rajendra Pamula, and D. S. Chauhan. "Semantic approaches for query expansion." Evolutionary Intelligence 14, no. 2 (March 20, 2021): 1101–16. http://dx.doi.org/10.1007/s12065-020-00554-x.

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16

SINGH, ROSHNI, and LEENA R. RAGHA. "Advances in IR Query Expansion." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 5 (May 30, 2018): 103. http://dx.doi.org/10.23956/ijarcsse.v8i5.685.

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As the internet is becoming rich with huge information, retrieving the wanted information is a huge challenge. Due to huge variation in framing a query by the individuals, matching of the terms with the document to be searched are facing many challenges. Researchers have worked on problem of identifying the correct essence of the user query by adding additional useful terms. They are currently working on this kind of Query Expansion (QE). There are many proposals like Manually, Iterative and Automatic QE. In this work we compare these different techniques and analyze for their strength and weaknesses.

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17

Brandão, Wladmir Cardoso. "Exploiting entities for query expansion." ACM SIGIR Forum 48, no. 1 (June 26, 2014): 43. http://dx.doi.org/10.1145/2641383.2641393.

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18

Ghosal, Sayani, and Dr Devendra Kumar Tayal. "Comprehensive Survey: Automatic Query Expansion." International Journal of Computer Trends & Technology 67, no. 07 (July 25, 2019): 1–7. http://dx.doi.org/10.14445/22312803/ijctt-v67i7p101.

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19

Sarkas, Nikos, Nilesh Bansal, Gautam Das, and Nick Koudas. "Measure-driven keyword-query expansion." Proceedings of the VLDB Endowment 2, no. 1 (August 2009): 121–32. http://dx.doi.org/10.14778/1687627.1687642.

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20

Bandyopadhyay, Ayan, Kripabandhu Ghosh, Prasenjit Majumder, and Mandar Mitra. "Query expansion for microblog retrieval." International Journal of Web Science 1, no. 4 (2012): 368. http://dx.doi.org/10.1504/ijws.2012.052535.

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21

Mandala, Rila, Takenobu Tokunaga, and Hozumi Tanaka. "Query expansion using heterogeneous thesauri." Information Processing & Management 36, no. 3 (May 2000): 361–78. http://dx.doi.org/10.1016/s0306-4573(99)00068-0.

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22

Huang, Qing, Yangrui Yang, Xudong Wang, Hongyan Wan, Rui Wang, and Guoqing Wu. "Query Expansion via Intent Predicting." International Journal of Software Engineering and Knowledge Engineering 27, no. 09n10 (November 2017): 1591–601. http://dx.doi.org/10.1142/s0218194017400137.

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To make the code search (CS) become more effective, a novel query expansion with intents (QEI) is proposed, in which the intent refers to the common subsequent modifications of the search results. The intent is extracted from the modification history. Within the intent scope, the CS is speeded up based on the semantic and structural matches. The precision of the search results is also increased by expanding the query with the intent. Compared with CodeHow and Google CS, QEI outperforms them by 28.5% with a precision score of 0.846. (i.e. 84.6% of the first results are accepted directly by users).

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23

Pal, Dipasree, Mandar Mitra, and Kalyankumar Datta. "Improving query expansion using WordNet." Journal of the Association for Information Science and Technology 65, no. 12 (May 13, 2014): 2469–78. http://dx.doi.org/10.1002/asi.23143.

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24

Gao, Qian, and Young Im Cho. "A Multi-Agent Personalized Query Refinement Approach for Academic Paper Retrieval in Big Data Environment." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 7 (November 20, 2012): 874–80. http://dx.doi.org/10.20965/jaciii.2012.p0874.

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This paper proposes a multi-agent query refinement approach to realize personalized query expansion effective for academic paper retrieval in a Big Data environment. First, we use Hadoop as a platform to develop a formalized model to represent different types of large caches of data in order to analyze and process Big Data efficiently. Second, we use a client agent to verify user identities and monitor whether a device is ready to run a query-expanded task. We then use a query expansion agent to determine the domain that the initial query belongs to by applying a knowledgebased query expansion strategy and comprehensively considering users’ interests according to the intelligent devices they use by implementing a user-device-based query expansion strategy and a weighted query expansion strategy in order to obtain the optimized query expansion set. We compare our method with the conceptual retrieval method as well as other two lexical methods for query expansion, and we prove that our method has better average recall and average precision ratios.

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25

Zaiem, Salah, and Fatiha Sadat. "Sequence to Sequence Learning for Query Expansion." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 10075–76. http://dx.doi.org/10.1609/aaai.v33i01.330110075.

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As fas as we are aware, using Sequence to Sequence algorithms for query expansion has not been explored yet in Information Retrieval literature. We tried to fill this gap in the literature with a custom Query Expansion system trained and tested on open datasets. One specificity of our engine compared to classic ones is that it does not need the documents to expand the introduced query. We test our expansions on two different tasks : Information Retrieval and Answer preselection. Our method yielded a slight improvement in performance in both two tasks . Our main contributions are :• Starting from open datasets, we built a Query Expansion training set using sentence-embeddings-based Keyword Extraction.• We assess the ability of the Sequence to Sequence neural networks to capture expanding relations in the words embeddings’ space.We afterwards started a quantitative and qualitative analysis of the weights learned by our network. In the second part, I will discuss what is learned by a Recurrent Neural Network compared to what we know about human language learning.

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26

Chawla, Suruchi. "Semantic Query Expansion using Cluster Based Domain Ontologies." International Journal of Information Retrieval Research 2, no. 2 (April 2012): 13–28. http://dx.doi.org/10.4018/ijirr.2012040102.

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Information on the web has been growing at a very rapid pace and has become quite voluminous over the past few years. The users search query on the web could not retrieve sufficient relevant documents and is responsible for low precision of search results. To improve the precision of search results, an algorithm is proposed in this paper for semantic query expansion using domain ontology based on clustered web query sessions. Domain ontology is created for each cluster of query sessions. The input query of a user is used to select the most similar cluster. The domain ontology of the selected cluster is used to suggest the related concepts for query expansion and the expanded query is used for information retrieval to test its effectiveness. The experiment was conducted on the captured user query sessions on the web and results prove the efficacy of the proposed approach.

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27

Wang, Yingqi, Nianbin Wang, and Lianke Zhou. "Keyword Query Expansion Paradigm Based on Recommendation and Interpretation in Relational Databases." Scientific Programming 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7613026.

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Due to the ambiguity and impreciseness of keyword query in relational databases, the research on keyword query expansion has attracted wide attention. Existing query expansion methods expose users’ query intention to a certain extent, but most of them cannot balance the precision and recall. To address this problem, a novel two-step query expansion approach is proposed based on query recommendation and query interpretation. First, a probabilistic recommendation algorithm is put forward by constructing a term similarity matrix and Viterbi model. Second, by using the translation algorithm of triples and construction algorithm of query subgraphs, query keywords are translated to query subgraphs with structural and semantic information. Finally, experimental results on a real-world dataset demonstrate the effectiveness and rationality of the proposed method.

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28

Gou, Zhinan, and Yan Li. "A method of query expansion based on topic models and user profile for search in folksonomy." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 1701–11. http://dx.doi.org/10.3233/jifs-210508.

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With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

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29

Singh, Jagendra, and Aditi Sharan. "Context Window Based Co-occurrence Approach for Improving Feedback Based Query Expansion in Information Retrieval." International Journal of Information Retrieval Research 5, no. 4 (October 2015): 31–45. http://dx.doi.org/10.4018/ijirr.2015100103.

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Pseudo-relevance feedback (PRF) is a type of relevance feedback approach of query expansion that considers the top ranked retrieved documents as relevance feedback. In this paper the authors focus is to capture the limitation of co-occurrence and PRF based query expansion approach and the authors proposed a hybrid method to improve the performance of PRF based query expansion by combining query term co-occurrence and query terms contextual information based on corpus of top retrieved feedback documents in first pass. Firstly, the paper suggests top retrieved feedback documents based query term co-occurrence approach to select an optimal combination of query terms from a pool of terms obtained using PRF based query expansion. Second, contextual window based approach is used to select the query context related terms from top feedback documents. Third, comparisons were made among baseline, co-occurrence and contextual window based approaches using different performance evaluating metrics. The experiments were performed on benchmark data and the results show significant improvement over baseline approach.

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30

Esch, Maria, Jinbo Chen, Stephan Weise, Keywan Hassani-Pak, Uwe Scholz, and Matthias Lange. "A Query Suggestion Workflow for Life Science IR-Systems." Journal of Integrative Bioinformatics 11, no. 2 (June 1, 2014): 15–26. http://dx.doi.org/10.1515/jib-2014-237.

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Summary Information Retrieval (IR) plays a central role in the exploration and interpretation of integrated biological datasets that represent the heterogeneous ecosystem of life sciences. Here, keyword based query systems are popular user interfaces. In turn, to a large extend, the used query phrases determine the quality of the search result and the effort a scientist has to invest for query refinement. In this context, computer aided query expansion and suggestion is one of the most challenging tasks for life science information systems. Existing query front-ends support aspects like spelling correction, query refinement or query expansion. However, the majority of the front-ends only make limited use of enhanced IR algorithms to implement comprehensive and computer aided query refinement workflows. In this work, we present the design of a multi-stage query suggestion workflow and its implementation in the life science IR system LAILAPS. The presented workflow includes enhanced tokenisation, word breaking, spelling correction, query expansion and query suggestion ranking. A spelling correction benchmark with 5,401 queries and manually selected use cases for query expansion demonstrate the performance of the implemented workflow and its advantages compared with state-of-the-art systems.

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31

YANG, Xin-xin, Pei-feng LI, and Qiao-ming ZHU. "Name disambiguation based on query expansion." Journal of Computer Applications 32, no. 9 (May 13, 2013): 2488–90. http://dx.doi.org/10.3724/sp.j.1087.2012.02488.

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32

Zou, Qun, and Changquan Zhang. "Query expansion via learning change sequences." International Journal of Knowledge-based and Intelligent Engineering Systems 24, no. 2 (July 20, 2020): 95–105. http://dx.doi.org/10.3233/kes-200033.

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33

Yusuf, Nuhu, Noor Azah Samsudin, Norfaradilla Wahid, Aida Mustapha, Nazri Mohd Nawi, and Mohd Amin Mohd Yunus. "Arabic text semantic-based query expansion." International Journal of Data Mining, Modelling and Management 14, no. 1 (2022): 30. http://dx.doi.org/10.1504/ijdmmm.2022.10046102.

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34

Yusuf, Nuhu, Mohd Amin Mohd Yunus, Norfaradilla Wahid, Aida Mustapha, Nazri Mohd Nawi, and Noor Azah Samsudin. "Arabic text semantic-based query expansion." International Journal of Data Mining, Modelling and Management 14, no. 1 (2022): 30. http://dx.doi.org/10.1504/ijdmmm.2022.122037.

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35

Prabhakar, Dinesh Kumar, Sukomal Pal, and Chiranjeev Kumar. "Query Expansion for Tansliterated Text Retrieval." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 4 (January 7, 2021): 1–34. http://dx.doi.org/10.1145/3447649.

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With Web 2.0, there has been exponential growth in the number of Web users and the volume of Web content. Most of these users are not only consumers of the information but also generators of it. People express themselves here in colloquial languages, but using Roman script (transliteration). These texts are mostly informal and casual, and therefore seldom follow grammar rules. Also, there does not exist any prescribed set of spelling rules in transliterated text. This freedom leads to large-scale spelling variations, which is a major challenge in mixed script information processing. This article studies different existing phonetic algorithms to handle the issue of spelling variation, points out the limitations of them, and proposes a novel phonetic encoding approach with two different flavors in the light of Hindi transliteration. Experiments performed over Hindi song lyrics retrieval in mixed script domain with three different retrieval models show that proposed approaches outperform the existing techniques in a majority of the cases (sometimes statistically significantly) for a number of metrics like nDCG@1, nDCG@5, nDCG@10, MAP, MRR, and Recall.

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36

ROBERTSON, S. E. "ON TERM SELECTION FOR QUERY EXPANSION." Journal of Documentation 46, no. 4 (April 1990): 359–64. http://dx.doi.org/10.1108/eb026866.

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37

Hang Cui, Ji-Rong Wen, Jian-Yun Nie, and Wei-Ying Ma. "Query expansion by mining user logs." IEEE Transactions on Knowledge and Data Engineering 15, no. 4 (July 2003): 829–39. http://dx.doi.org/10.1109/tkde.2003.1209002.

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38

Okabe, Masayuki, and Seiji Yamada. "Semisupervised Query Expansion with Minimal Feedback." IEEE Transactions on Knowledge and Data Engineering 19, no. 11 (November 2007): 1585–89. http://dx.doi.org/10.1109/tkde.2007.190646.

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39

V, Arjun Atreya, Ashish Kankaria, Pushpak Bhattacharyya, and Ganesh Ramakrishnan. "Query Expansion in Resource-Scarce Languages." ACM Transactions on Asian and Low-Resource Language Information Processing 16, no. 2 (December 14, 2016): 1–17. http://dx.doi.org/10.1145/2997643.

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40

Liu, Ziyang, Sivaramakrishnan Natarajan, and Yi Chen. "Query expansion based on clustered results." Proceedings of the VLDB Endowment 4, no. 6 (March 2011): 350–61. http://dx.doi.org/10.14778/1978665.1978667.

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41

Jain, Goonjan, and Achal Bansal. "Common sense based automatic query expansion." Journal of Information and Optimization Sciences 41, no. 7 (August 13, 2020): 1579–87. http://dx.doi.org/10.1080/02522667.2020.1802130.

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42

Weerkamp, Wouter, Krisztian Balog, and Maarten de Rijke. "Exploiting External Collections for Query Expansion." ACM Transactions on the Web 6, no. 4 (November 2012): 1–29. http://dx.doi.org/10.1145/2382616.2382621.

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43

Zingla, Meriem Amina, Latiri Chiraz, and Yahya Slimani. "Short Query Expansion for Microblog Retrieval." Procedia Computer Science 96 (2016): 225–34. http://dx.doi.org/10.1016/j.procs.2016.08.135.

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44

Abdelali, Ahmed, Jim Cowie, and Hamdy S. Soliman. "Improving query precision using semantic expansion." Information Processing & Management 43, no. 3 (May 2007): 705–16. http://dx.doi.org/10.1016/j.ipm.2006.06.007.

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45

Colace, Francesco, Massimo De Santo, Luca Greco, and Paolo Napoletano. "Weighted Word Pairs for query expansion." Information Processing & Management 51, no. 1 (January 2015): 179–93. http://dx.doi.org/10.1016/j.ipm.2014.07.004.

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46

Xie, Hongtao, Yongdong Zhang, Jianlong Tan, Li Guo, and Jintao Li. "Contextual Query Expansion for Image Retrieval." IEEE Transactions on Multimedia 16, no. 4 (June 2014): 1104–14. http://dx.doi.org/10.1109/tmm.2014.2305909.

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47

Joshi, Sandeep, and Satpal Singh Kushwaha. "Query Expansion using Artificial Relevance Feedback." International Journal of Computer Applications 44, no. 7 (April 30, 2012): 41–45. http://dx.doi.org/10.5120/6279-8448.

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48

Muresan, Gheorghe. "An investigation of query expansion terms." Proceedings of the American Society for Information Science and Technology 43, no. 1 (October 10, 2007): 1–9. http://dx.doi.org/10.1002/meet.14504301313.

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49

WAN, Chang-Xuan, and Yuan LU. "Structural Query Expansion Based on Weighted Query Term for XML Documents." Journal of Software 19, no. 10 (October 20, 2008): 2611–19. http://dx.doi.org/10.3724/sp.j.1001.2008.02611.

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50

Singh, Jagendra, and Aditi Sharan. "Relevance Feedback Based Query Expansion Model Using Borda Count and Semantic Similarity Approach." Computational Intelligence and Neuroscience 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/568197.

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Pseudo-Relevance Feedback (PRF) is a well-known method of query expansion for improving the performance of information retrieval systems. All the terms of PRF documents are not important for expanding the user query. Therefore selection of proper expansion term is very important for improving system performance. Individual query expansion terms selection methods have been widely investigated for improving its performance. Every individual expansion term selection method has its own weaknesses and strengths. To overcome the weaknesses and to utilize the strengths of the individual method, we used multiple terms selection methods together. In this paper, first the possibility of improving the overall performance using individual query expansion terms selection methods has been explored. Second, Borda count rank aggregation approach is used for combining multiple query expansion terms selection methods. Third, the semantic similarity approach is used to select semantically similar terms with the query after applying Borda count ranks combining approach. Our experimental results demonstrated that our proposed approaches achieved a significant improvement over individual terms selection method and related state-of-the-art methods.

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