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Relevant bibliographies by topics / QUERY AUTOCOMPLETION

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Academic literature on the topic 'QUERY AUTOCOMPLETION'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "QUERY AUTOCOMPLETION"

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Xiao, Chuan, Jianbin Qin, Wei Wang, Yoshiharu Ishikawa, Koji Tsuda, and Kunihiko Sadakane. "Efficient error-tolerant query autocompletion." Proceedings of the VLDB Endowment 6, no. 6 (April 2013): 373–84. http://dx.doi.org/10.14778/2536336.2536339.

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Jiang, Danyang, Honghui Chen, and Fei Cai. "Exploiting Query’s Temporal Patterns for Query Autocompletion." Mathematical Problems in Engineering 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/7490879.

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Query autocompletion (QAC) is a common interactive feature of web search engines. It aims at assisting users to formulate queries and avoiding spelling mistakes by presenting them with a list of query completions as soon as they start typing in the search box. Existing QAC models mostly rank the query completions by their past popularity collected in the query logs. For some queries, their popularity exhibits relatively stable or periodic behavior while others may experience a sudden rise in their query popularity. Current time-sensitive QAC models focus on either periodicity or recency and are unable to respond swiftly to such sudden rise, resulting in a less optimal QAC performance. In this paper, we propose a hybrid QAC model that considers two temporal patterns of query’s popularity, that is, periodicity and burst trend. In detail, we first employ the Discrete Fourier Transform (DFT) to identify the periodicity of a query’s popularity, by which we forecast its future popularity. Then the burst trend of query’s popularity is detected and incorporated into the hybrid model with its cyclic behavior. Extensive experiments on a large, real-world query log dataset infer that modeling the temporal patterns of query popularity in the form of its periodicity and its burst trend can significantly improve the effectiveness of ranking query completions.

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HU, Sheng, Chuan XIAO, and Yoshiharu ISHIKAWA. "An Efficient Algorithm for Location-Aware Query Autocompletion." IEICE Transactions on Information and Systems E101.D, no. 1 (2018): 181–92. http://dx.doi.org/10.1587/transinf.2017edp7152.

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Yi, Peipei, Byron Choi, Sourav S. Bhowmick, and Jianliang Xu. "AutoG: a visual query autocompletion framework for graph databases." VLDB Journal 26, no. 3 (January 27, 2017): 347–72. http://dx.doi.org/10.1007/s00778-017-0454-9.

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Qin, Jianbin, Chuan Xiao, Sheng Hu, Jie Zhang, Wei Wang, Yoshiharu Ishikawa, Koji Tsuda, and Kunihiko Sadakane. "Efficient query autocompletion with edit distance-based error tolerance." VLDB Journal 29, no. 4 (December 14, 2019): 919–43. http://dx.doi.org/10.1007/s00778-019-00595-4.

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Abdelkrim, Latreche, Lehireche Ahmed, and Kadda Benyahia. "Interrogation Based on Semantic Annotations." International Journal of Web Portals 9, no. 2 (July 2017): 47–67. http://dx.doi.org/10.4018/ijwp.2017070103.

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Traditional information search approaches do not explicitly capture the meaning of a keyword query, but provide a good way for the user to express his or her information needs based on the keywords. In principle, semantic search aims to produce better results than traditional keyword search, but its progression has retarded because of to the complexity of the query languages. In this article, the authors present an approach to adapt keyword queries to querying the semantic web based on semantic annotations: the approach automatically construct structured formal queries from keywords. The authors propose a new process where they introduce a novel context-based query autocompletion feature to help the users to construct their keywords query by suggesting queries given prefixes. They also address the problem of context-based generating formal queries by exploiting user's query history, where previous queries can be used as contextual information for generating a new query. With the first tests, the authors' approach achieved encouraging results.

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Zarchi, R., A. Elgressy, S. Ben-Horin, and U. Kopylov. "P344 What our patients are looking for - the common fields of interest in IBD patients: A Google Trends Analysis." Journal of Crohn's and Colitis 16, Supplement_1 (January 1, 2022): i359. http://dx.doi.org/10.1093/ecco-jcc/jjab232.471.

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Abstract Background Inflammatory bowel diseases are varied in the way they present, progress and respond to treatment. IBD patients frequently complain about the lack of proper knowledge and support systems, this gap leads to an extensive online search for information. The aim of our study was to characterize the trends in IBD-related Google searches by exploring the Google Trends query tool Methods We retrieved worldwide Google Trends data related to Crohn’s Disease and Ulcerative Colitis over the past, 10 years (Jan, 2011 - Oct, 2021). The search terms selection was based on preferences and knowledge gaps identified by an online survey by the members of the of the israeli Crohn’s Disease and Ulcerative Colitis patients association, coupled with data from available literature and Google autocompletion data. We also compared the search volume of the two diseases using the “Topic” feature in google trends, which allows us to utilize google’s search terms aggregation by a specific topic. Google trends provide RSV (Relative Search Volume) over time, and over different regions. The results are normalized on a scale of, 0–100 (100 signifying the most highly-search item).We also compared the RSV for searches related to UC and CD. Results Out of the, 20 domains researched over, 370 months, the, 10 most searched domains in Crohn’s disease related searches was ’diet’(41.30%), ’cancer’(14.22%), ’weight’(7.52%), ’pregnancy’(4.44%), ’disability’(4.05%), ’COVID’(3.45%), ’alcohol’ (3.22%), ’vaccine’ (3.17%), ’stress’ (2.54%) and ’smoking’ (2.47%). The, 10 most searched domain in ulcerative colitis related searches was ’diet’ (41.72%), ’cancer’ (18.69%), ’weight’ (5.47%), ’pregnancy’(4.77%), ’smoking’(3.76%), ’alcohol’(3.63%), ’probiotics(3.27%), ’disability’(2.77%), ’stress’(2.62%) and ’covid’(2.12%). When focusing at the time period between March, 2020 and October, 2021, the most searched domain in crohn’s disease was ’COVID’(22.67%) followed by ’diet’(21.81%), ’cancer’(12.52%), ’vaccine’(10.70%), ’weight’(7.07%), ’disability’ (4.34%), ’alcohol’ (2.82%), ’pregnancy’ (2.67%), ’stress’ (2.22%) and ’biologics’ (2.17%). Over the same time span, the most searched domain in ulcerative colitis was ’diet’ (29.59%) followed by ’cancer’ (18.32%), ’COVID’ (11.84%), ’weight’ (6.32%), ’vaccine’ (5.75%), ’alcohol’ (4.07%), ’pregnancy’ (3.66%), ’disability’ (3.06%), ’smoking’ (2.61%) and ’probiotics’ (2.46%). Conclusion Patients have numerous interests related to their IBD disease. The most searched IBD-related item is diet, with COVID-19 leading since the break of the pandemic. Our results are a surrogate representation of the patient’s knowledge gaps and needs, and we suggest that IBD healthcare providers should focus their guidance on the issues identified by the patients as such.

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Yi, Peipei, Jianping Li, Byron Choi, Sourav S. Bhowmick, and Jianliang Xu. "FLAG: Towards Graph Query Autocompletion for Large Graphs." Data Science and Engineering, April 16, 2022. http://dx.doi.org/10.1007/s41019-022-00182-8.

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AbstractGraph query autocompletion (GQAC) takes a user’s graph query as input and generates top-k query suggestions as output, to help alleviate the verbose and error-prone graph query formulation process in a visual interface. To compose a target query with GQAC, the user may iteratively adopt suggestions or manually add edges to augment the existing query. The current state-of-the-art of GQAC, however, focuses on a large collection of small- or medium-sized graphs only. The subgraph features exploited by existing GQAC are either too small or too scarce in large graphs. In this paper, we present Flexible graph query autocompletion for LArge Graphs, called FLAG. We are the first to propose wildcard labels in the context of GQAC, which summarizes query structures that have different labels. FLAG allows augmenting users’ queries with subgraph increments with wildcard labels to form suggestions. To support wildcard-enabled suggestions, a new suggestion ranking function is proposed. We propose an efficient ranking algorithm and extend an index to further optimize the online suggestion ranking. We have conducted a user study and a set of large-scale simulations to verify both the effectiveness and efficiency of FLAG. The results show that the query suggestions saved roughly 50% of mouse clicks and FLAG returns suggestions in few seconds.

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YI, Peipei, Byron Choi, Zhiwei Zhang, Sourav S. Bhowmick, and Jianliang Xu. "GFocus: User Focus-based Graph Query Autocompletion." IEEE Transactions on Knowledge and Data Engineering, 2020, 1. http://dx.doi.org/10.1109/tkde.2020.3002934.

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"An Efficient Algorithm for Location-Aware Query Autocompletion." IEICE INFORMATION AND SYSTEMS SOCIETY JOURNAL 24, no. 3 (November 1, 2019): 14. http://dx.doi.org/10.1587/ieiceissjournal.24.3_14.

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Dissertations / Theses on the topic "QUERY AUTOCOMPLETION"

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Yi, Peipei. "Graph query autocompletion." HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/557.

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The prevalence of graph-structured data in modern real-world applications has led to a rejuvenation of research on graph data management and analytics. Several database query languages have been proposed for textually querying graph databases. Unfortunately, formulating a graph query using any of these query languages often demands considerable cognitive effort and requires "programming" skill at least similar to programming in SQL. Yet, in a wide spectrum of graph applications consumers need to query graph data but are not proficient query writers. Hence, it is important to devise intuitive techniques that can alleviate the burden of query formulation and thus increase the usability of graph databases. In this dissertation, we take the first step to study the graph query autocompletion problem. We provide techniques that take a user's graph query as input and generate top-k query suggestions as output, to help to alleviate the verbose and error-prone graph query formulation process in a visual environment. Firstly, we study visual query autocompletion for graph databases. Techniques for query autocompletion have been proposed for web search and XML search. However, a corresponding capability for graph query engine is in its infancy. We propose a novel framework for graph query autocompletion (called AutoG). The novelties of AutoG are as follows: First, we formalize query composition that specifies how query suggestions are formed. Second, we propose to increment a query with the logical units called c-prime features, that are (i) frequent subgraphs and (ii) constructed from smaller c-prime features in no more than c ways. Third, we propose algorithms to rank candidate suggestions. Fourth, we propose a novel index called feature DAG (FDAG) to further optimize the ranking. Secondly, we propose user focus-based graph query autocompletion. AutoG provides suggestions that are formed by adding subgraph increments to arbitrary places of an existing user query. However, humans can only interact with a small number of recent software artifacts in hand. Hence, many such suggestions could be irrelevant. We present the GFocus framework that exploits a novel notion of user focus of graph query formulation. Intuitively, the focus is the subgraph that a user is working on. We formulate locality principles to automatically identify and maintain the focus. We propose novel monotone submodular ranking functions for generating popular and comprehensive query suggestions only at the focus. We propose efficient algorithms and an index for ranking the suggestions. Thirdly, we propose graph query autocompletion for large graphs. Graph features that have been exploited in AutoG are either absent or rare in large graphs. To address this, we present Flexible graph query autocompletion for LArge Graphs, called FLAG. We propose wildcard label for query graph and query suggestions. In particular, FLAG allows augmenting users' queries using subgraph increments with wildcard labels, which summarize query suggestions that have similar increment structures but different labels. We propose an efficient ranking algorithm and a novel index, called Suggestion Summarization DAG (SSDAG), to optimize the online suggestion ranking. Detailed problem analysis and extensive experimental studies consistently demonstrate the effectiveness and robustness of our proposed techniques in a broad range of settings.

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KATIYAR, ANTRA. "A CONTEXT SENSITIVE AND PERSONALIZED QUERY AUTOCOMPLETION TECHNIQUE." Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15941.

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Query Autocompletion is a leading attribute of Search Engines which makes the user’s search experience better by predicting the query. QAC methods suggest query suggestions to users, after they enter some of the keystrokes in the search engine. This is done by predicting the query using past query logs and other trends. Current QAC methods use the Most Popular Completions as the suggestion results. Context and Personalized techniques are proposed already but they are used separately. The present methods being incorporated are the location and past searches sensitive QAC. In this proposed work of thesis, we will talk about a hybrid technique by combining both the context sensitive, trending and personalized suggestions. The improvements which are made in the base paper are that a new approach can be proposed by combining the three techniques to create a hybrid technique. It intends to incorporate three major research works: Time sensitive (based on time series and trends), Context Sensitive (based on recent searches done) and Personalized (based on gender, location and age-group) query auto completion. Thus an algorithm that considers all these parameters will be better at predicting the user query. The results predicted are better in reducing the user keystrokes during the search and also reduces the searching time, and also enhances the reliability of the search engine. Further improvements can be done by extracting the user’s browsing history to determine keywords, interests and other user-specific data for enhancing the result predictions.

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Book chapters on the topic "QUERY AUTOCOMPLETION"

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Smits, Grégory, Olivier Pivert, Hélène Jaudoin, and François Paulus. "An Autocompletion Mechanism for Enriched Keyword Queries to RDF Data Sources." In Flexible Query Answering Systems, 601–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40769-7_52.

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Li, Guozhong, Nathan Ng, Peipei Yi, Zhiwei Zhang, and Byron Choi. "Answering the Why-Not Questions of Graph Query Autocompletion." In Database Systems for Advanced Applications, 332–41. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91452-7_22.

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Kastrinakis, Dimitrios, and Yannis Tzitzikas. "Advancing Search Query Autocompletion Services with More and Better Suggestions." In Lecture Notes in Computer Science, 35–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13911-6_3.

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Wu, Hao, and Lizhu Zhou. "Form-Based Instant Search and Query Autocompletion on Relational Data." In Web-Age Information Management, 139–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32281-5_14.

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Miao, Yukai, Jianbin Qin, Sheng Hu, Yuyang Dong, Yoshiharu Ishikawa, and Makoto Onizuka. "NGNC: A Flexible and Efficient Framework for Error-Tolerant Query Autocompletion." In Communications in Computer and Information Science, 101–15. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61133-0_8.

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Conference papers on the topic "QUERY AUTOCOMPLETION"

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Ng, Nathan, Peipei Yi, Zhiwei Zhang, Byron Choi, Sourav S. Bhowmick, and Jianliang Xu. "FGreat: Focused Graph Query Autocompletion." In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 2019. http://dx.doi.org/10.1109/icde.2019.00213.

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Maxwell, David, Peter Bailey, and David Hawking. "Large-scale Generative Query Autocompletion." In ADCS 2017: The 22nd Australasian Document Computing Symposium. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3166072.3166083.

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Di Santo, Giovanni, Richard McCreadie, Craig Macdonald, and Iadh Ounis. "Comparing Approaches for Query Autocompletion." In SIGIR '15: The 38th International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2766462.2767829.

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Kang, Young Mo, Wenhao Liu, and Yingbo Zhou. "QueryBlazer: Efficient Query Autocompletion Framework." In WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3437963.3441725.

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Wang, Jin, and Chunbin Lin. "Fast Error-tolerant Location-aware Query Autocompletion." In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020. http://dx.doi.org/10.1109/icde48307.2020.00223.

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Block, Adam, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, and Inderjit S. Dhillon. "Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion." In SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3477495.3531958.

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