Relevant bibliographies by topics / Context of user query (COQ)

Academic literature on the topic 'Context of user query (COQ)'

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Published: 10 December 2022
Last updated: 19 February 2023

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Journal articles on the topic "Context of user query (COQ)"

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Xu, Zheng, Hai-Yan Chen, and Jie Yu. "Generating Personalized Web Search Using Semantic Context." Scientific World Journal 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/462782.

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The “one size fits the all” criticism of search engines is that when queries are submitted, the same results are returned to different users. In order to solve this problem, personalized search is proposed, since it can provide different search results based upon the preferences of users. However, existing methods concentrate more on the long-term and independent user profile, and thus reduce the effectiveness of personalized search. In this paper, the method captures the user context to provide accurate preferences of users for effectively personalized search. First, the short-term query context is generated to identify related concepts of the query. Second, the user context is generated based on the click through data of users. Finally, a forgetting factor is introduced to merge the independent user context in a user session, which maintains the evolution of user preferences. Experimental results fully confirm that our approach can successfully represent user context according to individual user information needs.
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Gajendragadkar, Uma, and Sarang Joshi. "Context Sensitive Search String Composition Algorithm using User Intention to Handle Ambiguous Keywords." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 1 (February 1, 2017): 432. http://dx.doi.org/10.11591/ijece.v7i1.pp432-450.

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<p>Finding the required URL among the first few result pages of a search engine is still a challenging task. This may require number of reformulations of the search string thus adversely affecting user's search time. Query ambiguity and polysemy are major reasons for not obtaining relevant results in the top few result pages. Efficient query composition and data organization are necessary for getting effective results. Context of the information need and the user intent may improve the autocomplete feature of existing search engines. This research proposes a Funnel Mesh-5 algorithm (FM5) to construct a search string taking into account context of information need and user intention with three main steps 1) Predict user intention with user profiles and the past searches via weighted mesh structure 2) Resolve ambiguity and polysemy of search strings with context and user intention 3) Generate a personalized disambiguated search string by query expansion encompassing user intention and predicted query. Experimental results for the proposed approach and a comparison with direct use of search engine are presented. A comparison of FM5 algorithm with K Nearest Neighbor algorithm for user intention identification is also presented. The proposed system provides better precision for search results for ambiguous search strings with improved identification of the user intention. Results are presented for English language dataset as well as Marathi (an Indian language) dataset of ambiguous search strings.</p><p> </p>
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Kumar, Sushil, and Naresh Chauhan. "A Context Model For Focused Web Search." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 3 (June 30, 2012): 155–62. http://dx.doi.org/10.24297/ijct.v2i3c.2715.

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In the existing web search systems, the information retrieval isperformed using a single query and mapping it to a set ofdocuments. From a single query, however, the search systemscan only have very limited clue about the user‟s informationneed. The user‟s context and his environment are ignored whilesearching the information resulting in irrelevant search results.These irrelevant search results increase the cognitive overheadof the user in filtering them out and finding useful information.Therefore, the search systems must incorporate contextinformation regarding user and his environment search thehighly relevant web pages. This paper prepares an Entity-Centric model for the context and proposes a framework forcontext-aware focused web search system that considers thevarious context features and returns highly relevant searchresults to the user.
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Yao, Jing, Zhicheng Dou, and Ji-Rong Wen. "Clarifying Ambiguous Keywords with Personal Word Embeddings for Personalized Search." ACM Transactions on Information Systems 40, no. 3 (July 31, 2022): 1–29. http://dx.doi.org/10.1145/3470564.

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Personalized search tailors document ranking lists for each individual user based on her interests and query intent to better satisfy the user’s information need. Many personalized search models have been proposed. They first build a user interest profile from the user’s search history, and then re-rank the documents based on the personalized matching scores between the created profile and candidate documents. In this article, we attempt to solve the personalized search problem from an alternative perspective of clarifying the user’s intention of the current query. We know that there are many ambiguous words in natural language such as “Apple.” People with different knowledge backgrounds and interests have personalized understandings of these words. Therefore, we propose a personalized search model with personal word embeddings for each individual user that mainly contain the word meanings that the user already knows and can reflect the user interests. To learn great personal word embeddings, we design a pre-training model that captures both the textual information of the query log and the information about user interests contained in the click-through data represented as a graph structure. With personal word embeddings, we obtain the personalized word and context-aware representations of the query and documents. Furthermore, we also employ the current session as the short-term search context to dynamically disambiguate the current query. Finally, we use a matching model to calculate the matching score between the personalized query and document representations for ranking. Experimental results on two large-scale query logs show that our designed model significantly outperforms state-of-the-art personalization models.
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Yu, Yang Xin. "Personalization Information Retrieval Based on Unigram Language Model." Applied Mechanics and Materials 321-324 (June 2013): 2269–73. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.2269.

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Personalization information retrieval is very useful in information retrieval system, the user profile can be used to represent the favorites or interests of user. Many methods to personalization have been studied in extending query with user profile. A proposed navel method which use the context of long-term user profile with multiple domain to extend query model under the unigram language model framework, uses the new query model to retrieve and get more interesting results for users. Combined with psudo relevance feedback model, the proposed method get better performance. Experimental results show that the proposed method in this paper is effective.
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Chen, Wanyu, Zepeng Hao, Taihua Shao, and Honghui Chen. "Personalized query suggestion based on user behavior." International Journal of Modern Physics C 29, no. 04 (April 2018): 1850036. http://dx.doi.org/10.1142/s0129183118500365.

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Query suggestions help users refine their queries after they input an initial query. Previous work mainly concentrated on similarity-based and context-based query suggestion approaches. However, models that focus on adapting to a specific user (personalization) can help to improve the probability of the user being satisfied. In this paper, we propose a personalized query suggestion model based on users’ search behavior (UB model), where we inject relevance between queries and users’ search behavior into a basic probabilistic model. For the relevance between queries, we consider their semantical similarity and co-occurrence which indicates the behavior information from other users in web search. Regarding the current user’s preference to a query, we combine the user’s short-term and long-term search behavior in a linear fashion and deal with the data sparse problem with Bayesian probabilistic matrix factorization (BPMF). In particular, we also investigate the impact of different personalization strategies (the combination of the user’s short-term and long-term search behavior) on the performance of query suggestion reranking. We quantify the improvement of our proposed UB model against a state-of-the-art baseline using the public AOL query logs and show that it beats the baseline in terms of metrics used in query suggestion reranking. The experimental results show that: (i) for personalized ranking, users’ behavioral information helps to improve query suggestion effectiveness; and (ii) given a query, merging information inferred from the short-term and long-term search behavior of a particular user can result in a better performance than both plain approaches.
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Li, Chenjie, Juseung Lee, Zhengjie Miao, Boris Glavic, and Sudeepa Roy. "CaJaDE." Proceedings of the VLDB Endowment 15, no. 12 (August 2022): 3594–97. http://dx.doi.org/10.14778/3554821.3554852.

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In this work, we demonstrate CaJaDE (Context-Aware Join-Augmented Deep Explanations), a system that explains query results by augmenting provenance with contextual information from other related tables in the database. Given two query results whose difference the user wants to understand, we enumerate possible ways of joining the provenance (i.e., contributing input tuples) of these two query results with tuples from other relevant tables in the database that were not used in the query. We use patterns to concisely explain the difference between the augmented provenance of the two query results. CaJaDE, through a comprehensive UI, enables the user to formulate questions and explore explanations interactively.
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Li, Yi Min. "Querying Deep Web Based on User Query Schema." Applied Mechanics and Materials 220-223 (November 2012): 2916–19. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2916.

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In the context of data integration on the web, traditional data integration system lacks scalability, flexibility, is no longer adequate. A novel data integration architecture, UQSIQ, is proposed, which achieves web-scale data integration. Key components in the system are introduced. UQSIQ maps user query schema to a suitable domain, selects web databases, querys and then ranks results. Key techniques, domain mapping and user query schema matching, to handle the scale and heterogeneity of structured web data are described.
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Xiong, Wei, Michael Recce, and Brook Wu. "Intent-Based User Segmentation with Query Enhancement." International Journal of Information Retrieval Research 3, no. 4 (October 2013): 1–17. http://dx.doi.org/10.4018/ijirr.2013100101.

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With the rapid advancement of the internet, accurate prediction of user's online intent underlying their search queries has received increasing attention from online advertising community. This paper aims to address the major challenges with user queries in the context of behavioral targeting advertising by proposing a query enhancement mechanism that augments user's queries by leveraging a user query log. The empirical evaluation demonstrates that the authors' methodology for query enhancement achieves greater improvement than the baseline models in both intent-based user classification and user segmentation. Different from traditional user segmentation methods, which take little semantics of user behaviors into consideration, the authors propose a novel user segmentation strategy by incorporating the query enhancement mechanism with a topic model to mine the relationships between users and their behaviors in order to segment users in a semantic manner. Comparing with a classical clustering algorithm, K-means, the experimental results indicate that the proposed user segmentation strategy helps improve behavioral targeting effectiveness significantly. This paper also proposes an alternative to define user's search intent for the evaluation purpose, in the case that the dataset is sanitized. This approach automatically labels users in a click graph, which are then used in training an intent-based user classifier.
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Voskarides, Nikos. "Supporting search engines with knowledge and context." ACM SIGIR Forum 55, no. 2 (December 2021): 1–2. http://dx.doi.org/10.1145/3527546.3527573.

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Search engines leverage knowledge to improve information access. Such knowledge comes in different forms: unstructured knowledge (e.g., textual documents) and structured knowledge (e.g., relationships between real-world objects and topics). In order to effectively leverage knowledge, search engines should account for context, i.e., information about the user and query. In this thesis, we aim to support search engines in leveraging knowledge while accounting for context. In the first part of this thesis, we study how to make structured knowledge more accessible to the user when the search engine proactively provides such knowledge as context to enrich search results. As a first task, we study how to retrieve descriptions of knowledge facts from a text corpus. Next, we study how to automatically generate knowledge fact descriptions. And finally, we study how to contextualize knowledge facts, that is, to automatically find facts related to a query fact. In the second part of this thesis, we study how to improve interactive knowledge gathering. We focus on conversational search, where the user interacts with the search engine to gather knowledge over large unstructured knowledge repositories. We study multi-turn passage retrieval as an instance of conversational search and focus on query resolution, that is, add missing context from the conversation history to the current turn. We model query resolution as a term classification task and propose a method to address it. In the final part of this thesis, we focus on search engine support for professional writers in the news domain. We study how to support such writers create event-narratives by exploring knowledge from a corpus of news articles. We propose a dataset construction procedure for this task that relies on existing news articles to simulate incomplete narratives and relevant articles. We study the performance of multiple rankers, lexical and semantic, and provide insights into the characteristics of this task. Awarded by : University of Amsterdam, Amsterdam, The Netherlands on 5 February 2021. Supervised by : Maarten de Rijke. Available at : https://hdl.handle.net/11245.1/78187b29-2403-4711-800a-0f92fcb9b15c.
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Dissertations / Theses on the topic "Context of user query (COQ)"

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Lipani, Aldo. "Query rewriting in information retrieval: automatic context extraction from local user documents to improve query results." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4528/.

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The central objective of research in Information Retrieval (IR) is to discover new techniques to retrieve relevant information in order to satisfy an Information Need. The Information Need is satisfied when relevant information can be provided to the user. In IR, relevance is a fundamental concept which has changed over time, from popular to personal, i.e., what was considered relevant before was information for the whole population, but what is considered relevant now is specific information for each user. Hence, there is a need to connect the behavior of the system to the condition of a particular person and his social context; thereby an interdisciplinary sector called Human-Centered Computing was born. For the modern search engine, the information extracted for the individual user is crucial. According to the Personalized Search (PS), two different techniques are necessary to personalize a search: contextualization (interconnected conditions that occur in an activity), and individualization (characteristics that distinguish an individual). This movement of focus to the individual's need undermines the rigid linearity of the classical model overtaken the ``berry picking'' model which explains that the terms change thanks to the informational feedback received from the search activity introducing the concept of evolution of search terms. The development of Information Foraging theory, which observed the correlations between animal foraging and human information foraging, also contributed to this transformation through attempts to optimize the cost-benefit ratio. This thesis arose from the need to satisfy human individuality when searching for information, and it develops a synergistic collaboration between the frontiers of technological innovation and the recent advances in IR. The search method developed exploits what is relevant for the user by changing radically the way in which an Information Need is expressed, because now it is expressed through the generation of the query and its own context. As a matter of fact the method was born under the pretense to improve the quality of search by rewriting the query based on the contexts automatically generated from a local knowledge base. Furthermore, the idea of optimizing each IR system has led to develop it as a middleware of interaction between the user and the IR system. Thereby the system has just two possible actions: rewriting the query, and reordering the result. Equivalent actions to the approach was described from the PS that generally exploits information derived from analysis of user behavior, while the proposed approach exploits knowledge provided by the user. The thesis went further to generate a novel method for an assessment procedure, according to the "Cranfield paradigm", in order to evaluate this type of IR systems. The results achieved are interesting considering both the effectiveness achieved and the innovative approach undertaken together with the several applications inspired using a local knowledge base.
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Asfari, Ounas. "Personalized Access to Contextual Information by using an Assistant for Query Reformulation." Thesis, Paris 11, 2011. http://www.theses.fr/2011PA112126.

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Les travaux présentés dans cette thèse rentrent dans le cadre de la Recherche d'Information (RI) et s'intéressent à une des questions de recherche actuellement en vogue dans ce domaine: la prise en compte du contexte de l'utilisateur pendant sa quête de l'information pertinente. Nous proposons une approche originale de reformulation automatique de requêtes basée sur le profil utilisateur et sa tâche actuelle. Plus précisément, notre approche tient compte deux éléments du contexte, les centres d'intérêts de l'utilisateur (son profil) et la tâche qu'il réalise, pour suggérer des requêtes appropriées à son contexte. Nous proposons, en particulier, toute une démarche originale permettant de bien interpréter et réécrire la requête initiale en fonction des activités réalisées dans la tâche courante de l'utilisateur.Nous considérons qu'une tâche est jalonnée par des activités, nous proposons alors d'interpréter le besoin de l'utilisateur, représenté initialement par la requête, selon ses activités actuelles dans la tâche (et son profil) et de suggérer des reformulations de requêtes appropriées à ces activités.Une implémentation de cette approche est faite, et elle est suivie d’une étude expérimentale. Nous proposons également une procédure d'évaluation qui tient compte l'évaluation des termes d'expansion, et l'évaluation des résultats retournés en utilisant les requêtes reformulées, appelés SRQ State Reformulated Query. Donc, trois facteurs d’évaluation sont proposés sur lesquels nous nous appuierons pour l'analyse et l'évaluation des résultats. L’objective est de quantifier l'amélioration apportée par notre système dans certains contextes par rapport aux autres systèmes. Nous prouvons que notre approche qui prend en compte la tâche actuelle de l'utilisateur est effectivement plus performante que les approches basées, soit uniquement sur la requête initiale, ou encore celle basée sur la requête reformulée en considérant uniquement le profil de l'utilisateur
Access to relevant information adapted to the needs and the context of the user is areal challenge in Web Search, owing to the increases of heterogeneous resources andthe varied data on the web. There are always certain needs behind the user query,these queries are often ambiguous and shortened, and thus we need to handle thesequeries intelligently to satisfy the user’s needs. For improving user query processing,we present a context-based hybrid method for query expansion that automaticallygenerates new reformulated queries in order to guide the information retrieval systemto provide context-based personalized results depending on the user profile andhis/her context. Here, we consider the user context as the actual state of the task thatthe user is undertaking when the information retrieval process takes place. Thus StateReformulated Queries (SRQ) are generated according to the task states and the userprofile which is constructed by considering related concepts from existing concepts ina domain ontology. Using a task model, we will show that it is possible to determinethe user’s current task automatically. We present an experimental study in order toquantify the improvement provided by our system compared to the direct querying ofa search engine without reformulation, or compared to the personalized reformulationbased on a user profile only. The Preliminary results have proved the relevance of ourapproach in certain contexts
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(14030507), Deepani B. Guruge. "Effective document clustering system for search engines." Thesis, 2008. https://figshare.com/articles/thesis/Effective_document_clustering_system_for_search_engines/21433218.

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People use web search engines to fill a wide variety of navigational, informational and transactional needs. However, current major search engines on the web retrieve a large number of documents of which only a small fraction are relevant to the user query. The user then has to manually search for relevant documents by traversing a topic hierarchy, into which a collection is categorised. As more information becomes available, it becomes a time consuming task to search for required relevant information.

This research develops an effective tool, the web document clustering (WDC) system, to cluster, and then rank, the output data obtained from queries submitted to a search engine, into three pre-defined fuzzy clusters. Namely closely related, related and not related. Documents in closely related and related documents are ranked based on their context.

The WDC output has been compared against document clustering results from the Google, Vivisimo and Dogpile systems as these where considered the best at the fourth Search Engine Awards [24]. Test data was from standard document sets, such as the TREC-8 [118] data files and the Iris database [38], or 3 from test text retrieval tasks, "Latex", "Genetic Algorithms" and "Evolutionary Algorithms". Our proposed system had as good as, or better results, than that obtained by these other systems. We have shown that the proposed system can effectively and efficiently locate closely related, related and not related, documents among the retrieved document set for queries submitted to a search engine.

We developed a methodology to supply the user with a list of keywords filtered from the initial search result set to further refine the search. Again we tested our clustering results against the Google, Vivisimo and Dogpile systems. In all cases we have found that our WDC performs as well as, or better than these systems.

The contributions of this research are:

  1. A post-retrieval fuzzy document clustering algorithm that groups documents into closely related, related and not related clusters. This algorithm uses modified fuzzy c-means (FCM) algorithm to cluter documents into predefined intelligent fuzzy clusters and this approach has not been used before.
  2. The fuzzy WDC system satisfies the user's information need as far as possible by allowing the user to reformulate the initial query. The system prepares an initial word list by selecting a few characteristics terms of high frequency from the first twenty documents in the initial search engine output. The user is then able to use these terms to input a secondary query. The WDC system then creates a second word list, or the context of the user query (COQ), from the closely related documents to provide training data to refine the search. Documents containing words with high frequency from the training list, based on a pre-defined threshold value, are then presented to the user to refine the search by reformulating the query. In this way the context of the user query is built, enabling the user to learn from the keyword list. This approach is not available in current search engine technology.
  3. A number of modifications were made to the FCM algorithm to improve its performance in web document clustering. A factor swkq is introduced into the membership function as a measure of the amount of overlaping between the components of the feature vector and the cluster prototype. As the FCM algorithm is greatly affected by the values used to initialise the components of cluster prototypes a machine learning approach, using an Evolutionary Algorithm, was used to resolve the initialisation problem.
  4. Experimental results indicate that the WDC system outperformed Google, Dogpile and the Vivisimo search engines. The post-retrieval fuzzy web document clustering algorithm designed in this research improves the precision of web searches and it also contributes to the knowledge of document retrieval using fuzzy logic.
  5. A relational data model was used to automatically store data output from the search engine off-line. This takes the processing of data of the Internet off-line, saving resources and making better use of the local CPU.
  6. This algorithm uses Latent Semantic Indexing (LSI) to rank documents in the closely related and related clusters. Using LSI to rank document is wellknown, however, we are the first to apply it in the context of ranking closely related documents by using COQ to form the term x document matrix in LSI, to obtain better ranking results.
  7. Adjustments based on document size are proposed for dealing with problems associated with varying document size in the retrieved documents and the effect this has on cluster analysis.
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Book chapters on the topic "Context of user query (COQ)"

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Park, Hyun Kyu, In Ho Cho, Sook Young Ji, and Joong Seek Lee. "An Empirical Study on Web Search Behavior through the Investigation of a User-Clustered Query Session." In Modeling and Using Context, 233–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24279-3_25.

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Maeda, Haruhisa, Sachio Saiki, and Masahide Nakamura. "User Context Query Service Supporting Home Person-Centered Care for Elderly People." In Intelligent Human Systems Integration, 112–18. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-73888-8_19.

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Aknouche, Rachid, Ounas Asfari, Fadila Bentayeb, and Omar Boussaid. "Integrating Query Context and User Context in an Information Retrieval Model Based on Expanded Language Modeling." In Lecture Notes in Computer Science, 244–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32498-7_19.

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Storey, Veda C., Vijayan Sugumaran, and Andrew Burton-Jones. "The Role of User Profiles in Context-Aware Query Processing for the Semantic Web." In Natural Language Processing and Information Systems, 51–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27779-8_5.

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Singh, Jagendra, and Aditi Sharan. "Context Window Based Co-Occurrence Approach for Improving Feedback Based Query Expansion in Information Retrieval." In Information Retrieval and Management, 1597–613. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5191-1.ch072.

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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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Xiong, Wei, and Y. F. Brook Wu. "User Query Enhancement for Behavioral Targeting." In Ontologies and Big Data Considerations for Effective Intelligence, 413–33. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2058-0.ch009.

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Ad targeting has been receiving more and more attention in the online publishing world, where advertisers want their ads to be seen by potential consumers at the right time. This chapter aims to address the major challenges with user queries in the context of behavioral targeting advertising by proposing a user intent representation strategy and a query enhancement mechanism. The authors focus on investigating the intent based user classification performance and the effectiveness of user segmentation under a topic model that helps explore semantic relation between user queries in behavioral targeting. In addition, the authors propose an alternative to define user's search intent for the evaluation purpose, in the case that the dataset is sanitized.
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Koutrika, Georgia. "Database Query Personalization." In Encyclopedia of Database Technologies and Applications, 147–52. IGI Global, 2005. http://dx.doi.org/10.4018/978-1-59140-560-3.ch025.

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Traditional database and information retrieval systems have followed a query-based information access paradigm (i.e., information is returned to the user on the basis of a query issued). As a result, users issuing the same query are provided with the same answer. With the advent of the World Wide Web and hand-held electronic devices such as palmtops and cellular phones, information access entered a new era. Increasing amounts of information become available to a growing mass of untrained lay users through various access media. A user searching Web-resident information may have to reformulate queries issued several times and sift through many results until a satisfactory, if any, answer is obtained. As purely query-driven approaches may be inappropriate in this context, the need for a shift towards a more user-centered information access paradigm arises. To this end, different approaches aim to the personalization of the overall user experience at different levels: content selection, content presentation, and user interaction.
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Hanh, Huu Hoang, Manh Nguyen Tho, and Min Tjoa A. "A Semantic Web-Based Approach for Context-Aware User Query Formulation and Information Retrieval." In Web Engineering Advancements and Trends, 1–23. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-719-5.ch001.

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Formulating unambiguous queries in the Semantic Web applications is a challenging task for users. This article presents a new approach in guiding users to formulate clear requests based on their common nature of querying for information. The approach known as the front-end ap proach gives users an overview about the system data through a vir tual data component which stores the extracted metadata of the data storage sources in the form of an ontology. This approach reduces the ambiguities in users’ requests at a very early stage and allows the query process to effectively perform in fulfilling users’ demands in a context-aware manner. Furthermore, the approach provides a powerful query engine, called context-aware query ing, that recommends the appropriate query patterns according to the user’s querying context.
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Müller, Henning, and Jayashree Kalpathy-Cramer. "Putting the Content Into Context." In New Technologies for Advancing Healthcare and Clinical Practices, 105–15. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-780-7.ch006.

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Digital management of medical images is becoming increasingly important as the number of images being created in medical settings everyday is growing rapidly. Content-based image retrieval or techniques based on the query-by-example paradigm have been studied extensively in computer vision. However, the global, low level visual features automatically extracted by these algorithms do not always correspond to high level concepts that a user has in his mind for searching. The role of image retrieval in diagnostic medicine can be quite complex, making it difficult for the user to express his/her information needs appropriately. Image retrieval in medicine needs to evolve from purely visual retrieval to a more holistic, case-based approach that incorporates various multimedia data sources. These include multiple images, free text, structured data, as well as external knowledge sources and ontologies.
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Hurson, A., and Bo Yang. "Multimedia Content Representation Technologies." In Multimedia Technologies, 580–89. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-953-3.ch042.

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In recent years, the rapid expansion of multimedia applications, partly due to the exponential growth of the Internet, has proliferated over the daily life of Internet users. Consequently, research on multimedia technologies is of increasing importance in computer society. In contrast with traditional text-based systems, multimedia applications usually incorporate much more powerful descriptions of human thought – video, audio and images (Auffret, Foote, Li & Shahraray, 1999). Moreover, the large collections of data in multimedia systems make it possible to resolve more complex data operations, such as imprecise query or content-based retrieval. For instance, image database systems may accept an example picture and return the most similar images of the example (Cox, Miller & Minka, 2000, Huang, Chang & Huang, 2003). However, the conveniences of multimedia applications come at the expense of new challenges to the existing data management schemes
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Conference papers on the topic "Context of user query (COQ)"

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Freitas, Marcelo, Jimmy Silva, Davi Bandeira, Antonio Mendonca, Damires Souza, and Ana Carolina Salgado. "A User Context Management Approach for Query Personalization Settings." In 2012 IEEE Sixth International Conference on Semantic Computing (ICSC). IEEE, 2012. http://dx.doi.org/10.1109/icsc.2012.31.

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"Log Analysis of Academic Digital Library: User Query Patterns." In iConference 2014 Proceedings: Breaking Down Walls. Culture - Context - Computing. iSchools, 2014. http://dx.doi.org/10.9776/14346.

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Feng, Lizhou, Wanli Zuo, and Youwei Wang. "Novel Query Expansion Method based on User Interest Context and Ontology." In 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/isrme-15.2015.82.

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Shibata, Ryoichi, Shoya Matsumori, Yosuke Fukuchi, Tomoyuki Maekawa, Mitsuhiko Kimoto, and Michita Imai. "Utilizing Core-Query for Context-Sensitive Ad Generation Based on Dialogue." In IUI '22: 27th International Conference on Intelligent User Interfaces. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3490099.3511116.

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Zhang, Binbin, and Rahul Rai. "Materials Follow Form and Function: Probabilistic Factor Graph Approach for Automatic Material Assignments to 3D Objects." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34064.

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There are strong co-relations between material assignment, shape, and functionality of a part in overall product/assembly. However, these strong co-relations are rarely exploited for automated material assignment. We present a probabilistic graphical model-based approach to automatically assign materials to the parts (components) of a 3D object (assembly). The presented model performs material assignment by identifying the relations between shape, functionality, and materials of parts in the existing database objects. By learning the context dependent correlation without supervision from a set of objects and their segmented parts, the learned model can be used to assign proper real materials to the parts of a query object. Our primary contributions are: a) the real materials definition and assignment and b) assigning materials based on the functionality and form of the parts in the object. The performance of proposed computational approach is demonstrated by results of material assignment on various query objects without pre-specified material definitions.
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dos Santos, Veronica, and Sérgio Lifschitz. "A semantic search approach for hyper relational knowledge graphs." In Anais Estendidos do Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbbd_estendido.2021.18171.

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Information Retrieval Systems usually employ syntactic search techniques to match a set of keywords with the indexed content to retrieve results. But pure keyword-based matching lacks on capturing user's search intention and context and suffers of natural language ambiguity and vocabulary mismatch. Considering this scenario, the hypothesis raised is that the use of embeddings in a semantic search approach will make search results more meaningfully. Embeddings allow to minimize problems arising from terminology and context mismatch. This work proposes a semantic similarity function to support semantic search based on hyper relational knowledge graphs. This function uses embeddings in order to find the most similar nodes that satisfy a user query.
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Wu, Sixing, Minghui Wang, Dawei Zhang, Yang Zhou, Ying Li, and Zhonghai Wu. "Knowledge-Aware Dialogue Generation via Hierarchical Infobox Accessing and Infobox-Dialogue Interaction Graph Network." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/546.

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Due to limited knowledge carried by queries, traditional dialogue systems often face the dilemma of generating boring responses, leading to poor user experience. To alleviate this issue, this paper proposes a novel infobox knowledge-aware dialogue generation approach, HITA-Graph, with three unique features. First, open-domain infobox tables that describe entities with relevant attributes are adopted as the knowledge source. An order-irrelevance Hierarchical Infobox Table Encoder is proposed to represent an infobox table at three levels of granularity. In addition, an Infobox-Dialogue Interaction Graph Network is built to effectively integrate the infobox context and the dialogue context into a unified infobox representation. Second, a Hierarchical Infobox Attribute Attention mechanism is developed to access the encoded infobox knowledge at different levels of granularity. Last but not least, a Dynamic Mode Fusion strategy is designed to allow the Decoder to select a vocabulary word or copy a word from the given infobox/query. We extract infobox tables from Chinese Wikipedia and construct an infobox knowledge base. Extensive evaluation on an open-released Chinese corpus demonstrates the superior performance of our approach against several representative methods.
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Rangarajan, Arvind, Pradeep Radhakrishnan, Abha Moitra, Andrew Crapo, and Dean Robinson. "Manufacturability Analysis and Design Feedback System Developed Using Semantic Framework." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12028.

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Early manufacturability feedback is critical for reducing product cost and lead-time. This paper describes a new architecture and platform for authoring and applying manufacturability rules for design. The key step is to define a domain-specific ontology by creating a higher-level semantic language that describes design and manufacturing concepts relevant to specific manufacturing processes. This language has two primary uses; express design in the context of manufacturing and relate manufacturing constraints on design as declarative rules. OWL and Jena (a reasoning engine) are used in the background to reason about specific designs and provide manufacturability feedback in a client-server model. The use of Semantic Web technology makes it easier to augment manufacturability feedback with a query system for the designer that utilizes the same rule knowledge base to answer what-if scenarios. This is implemented using SPARQL and using the CAD design context and so enhances the user experience. This novel approach makes it easier for the domain experts to write or verify rules and the designers to validate concepts before changing the CAD model. This helps in maintaining the independence between the CAD platform and core enterprise knowledge. A pilot study in the sheet metal domain is implemented to demonstrate the steps necessary for complete early manufacturability analysis software and highlights the benefits of this approach.
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Asfoor, Hasan Mahdi, and Dalal Abadi Alharbi. "Unleash the Potential of Upstream Data Using Search, AI and Computer Vision." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211841-ms.

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Abstract Data is at the heart of Digital Transformation whether it is generated by legacy systems, a word processor, or state-of-the-art sensors. This makes it harder for Upstream professionals to find the right information for operational needs and making decisions. In this work, we describe an approach that utilizes Enterprise Search, AI and Computer Vision to construct a single efficient search layer, called USEARCH, over multiple data repositories. This enables Upstream professionals to perform searches using simple business language and get the information they need wherever it resides. Our approach to build USEARCH consists of five steps. First, establishing a search infrastructure. Second, indexing content and metadata of documents to make them easily searchable from a single layer regardless of the source repository. Third, utilizing Artificial Intelligence to intelligently tag information within data and documents such as wells, reservoirs and business process labels. Fourth, applying Computer Vision techniques to extract tabular information from documents. Fifth, developing an intuitive user interface to simplify finding data. It can intelligently set the business context based on user domain such as Exploration, Drilling or Petroleum Engineering. We applied our approach on over 3 million documents of different types such as drilling reports, reservoir studies, well test analysis, biostratigraphy and geological maps and stored on multiple repositories. This results in a massive improvement. Upstream professionals can now find data through a single layer and thus eliminate the overhead of switching searches between repositories. In addition, multiple search queries that used to take minutes have now been replaces by a single query that takes a few seconds and even milliseconds in some cases. Furthermore, search results are displayed in a business context with direct links to data at the source repositories. The application of Computer Vision allows any tabular data within documents to be exported to databases for fast analysis. Our approach connects all different data repositories and provides user with an intuitive user interface to find data. This unleashes the potential of Upstream data and empowers Upstream analysts to spend more time using data than finding it.
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de Resende Neves, Francisa Joana, and Ana Lídia Virtudes. "GIS model for management of expansion areas: the case of Belmonte." In International Conference Virtual City and Territory. Barcelona: Centre de Política de Sòl i Valoracions, 2009. http://dx.doi.org/10.5821/ctv.7536.

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Um dos mais importantes desempenhos dos Municípios em Portugal passa, em grande escala, pela gestão territorial, tarefa que tem vindo a enfrentar alguns obstáculos quanto à sua eficácia. Estes obstáculos têm relevância acrescida devido a lacunas na gestão de áreas de expansão urbana, tradicionalmente dispersas que correspondem aos espaços contíguos às zonas urbanas consolidadas,historicamente compactas para onde estas se poderão expandir. Ora, é precisamente nas áreas de expansão urbana, caracterizadas pelas baixas densidades, quer populacional quer habitacional, onde freqüentemente permanecem atividades de cariz dominante não urbano, de índole agrícola, que são entendidas como locais de reserva para o crescimento urbano futuro, que se registra uma maior dinâmica nas alterações de ocupação e utilização do território, em resultado da forte iniciativa privada. Esta iniciativa é freqüentemente morosa na sua execução e ineficaz quanto à continuidade com a envolvente, devido à deficiente informação disponibilizada pelo Município quanto às regras urbanísticas vigentes, ou devido ao deficiente entendimento dessas mesmas regras por parte dos agentes privados. Conseqüentemente, poder-se-á referir que a inexistência de um modelo de gestão territorial, organizado e simples que possa disponibilizar de forma eficaz a informação quanto às regras de ocupação e utilização do solo vigente, quer na óptica dos munícipes na sua iniciativa privada de intervir no território, quer na óptica do Município na sua tarefa técnica de fazer cumprir essas mesmas regras, tem vindo a agravar esta problemática, designadamente, dificultando a monetarização do processo. Verifica-se, assim, a necessidade de evoluir no modo de disponibilizar ao cidadão toda a informação relativa às possibilidades de ocupação e utilização do solo, incluindo as faculdades de edificar e de urbanizar, como as condicionantes que limitam o direito de propriedade e o uso do solo, provenientes dos conteúdos conceptuais dos instrumentos de gestão territorial de âmbito municipal. Atualmente, os vários instrumentos utilizados na gestão do território, a nível municipal, têm vindo a ser acompanhados com alguns softwares em desenvolvimento, como os Sistemas de Informação Geográfica, capazes de expor com maior facilidade e eficácia uma quantidade cada vez maior e mais complexa de informação, num processo moroso. Com este tipo de aplicações é possível, por exemplo, sobrepor a um Ortofotomapa de uma determinada parcela do território, a informação proveniente do elemento chave da gestão territorial de âmbito municipal, o Plano Diretor Municipal, das suas cartas de condicionantes, mapas da rede hidrográfica entre outras. Este artigo apresenta um modelo de gestão territorial aplicado às áreas de expansão urbana do conselho de Belmonte, localizado no interior do país junto à fronteira com a Espanha, recorrendo a uma base dedados e aos Sistemas de Informação Geográfica. Este modelo pretende servir de apoio à iniciativa privada bem como ao Município ao qual competirá assegurar a sua monitorização. *** ENG: One of the most important jobs of the Municipal Town Hall (MTH) is, largely, to manage the territory, which is something which has its difficulties along the way. These obstacles are even more relevant considering there are gaps in the management of areas where there is urban expansion, traditionally scattered; diversec ty; which correspond to the spaces adjacent to the consolidated urban areas, historically compact city into which these will be able to expand. It is precisely in the areas of urbane expansion, characterized by low densities, both in terms of population and housing, where frequently activities of a non-urban nature, of agricultural nature for example, which are considered as areas of future urban growth, which register a greater dynamic in the alterations of occupation and utilization of the territory, as a result of a strong private initiative. This initiative is frequently slow in its execution and ineffective with regards to continuity with the surroundings, due to the defective available information by the Local authority (MTH) with regard to the town planning rules in force, or due to the defective understanding of the same rules on the part of the private agents. As a result, we could say that the inexistence of any kind of territorial management model, organised and simple, and which could efficiently provide information regarding the rules of occupation and utilization, whether it be in the sight of the municipality in its private enterprise in the territory, or whether it be in the sight of the municipality in its technical role to enforce the keeping of these same rules, has come to aggravate this very issue, namely, making more difficult the monitoring of the process. In this we can confirm the necessity to evolve in the manner of making available to the citizen all the town planning information relative the possibilities of occupation and utilization of the land, including the facilities to build and urbanize as well as the conditioning factors which limit and rights of the owner in the occupation and utilization of the land, originating from the conceptual contents of the instruments of territorial management of the municipality. At present, the various instruments used in the management of territory in Portugal, on a municipal level,have come to be accompanied by various software currently being developed, such as Geographical Information Systems (GIS), able to expose with greater ease and efficiency a growing quantity and greater complexity of information, in a slow process. With these types of applications it is possible, for example, to superimpose an Ortophotomap of a determined piece of the territory, the information originating from the key element of the territorial management of municipal extent, the Municipal Director Plan, of its letters of restrictions, maps of the hydrographical network amongst other such things. This article presents a model of territorial management applied to the areas of expansion of the district of Belmonte, located in the interior of the country close to the border with Spain, resorting to a data base and to the Geographical Information Systems. This model intends to serve as a support to the private enterprise as well as to the Local authority to which it will compete to secure its monitoring.
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Reports on the topic "Context of user query (COQ)"

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Borgwardt, Stefan, and Veronika Thost. Temporal Query Answering in EL. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.214.

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Context-aware systems use data about their environment for adaptation at runtime, e.g., for optimization of power consumption or user experience. Ontology-based data access (OBDA) can be used to support the interpretation of the usually large amounts of data. OBDA augments query answering in databases by dropping the closed-world assumption (i.e., the data is not assumed to be complete any more) and by including domain knowledge provided by an ontology. We focus on a recently proposed temporalized query language that allows to combine conjunctive queries with the operators of the well-known propositional temporal logic LTL. In particular, we investigate temporalized OBDA w.r.t. ontologies in the DL EL, which allows for efficient reasoning and has been successfully applied in practice. We study both data and combined complexity of the query entailment problem.
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