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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Meng, Jian Liang, and Da Wei Li. "Improve and Optimize Query Recommendation System by MST Algorithm and its MapReduce Implementation." Applied Mechanics and Materials 701-702 (December 2014): 50–53. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.50.
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Query recommendation as an important tool to enhance the user search efficiency has gradually become a hotspot. In the context of big data, using the MapReduce programming model, combined with distributed minimum spanning tree algorithm, a parallel query recommended method based on MapReduce was proposed in this paper. The final results show that the efficiency of query recommendation was greatly improved through parallel computing.
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Chen, Jia, Jiaxin Mao, Yiqun Liu, Ziyi Ye, Weizhi Ma, Chao Wang, Min Zhang, and Shaoping Ma. "A Hybrid Framework for Session Context Modeling." ACM Transactions on Information Systems 39, no. 3 (May 6, 2021): 1–35. http://dx.doi.org/10.1145/3448127.
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Understanding user intent is essential for various retrieval tasks. By leveraging contextual information within sessions, e.g., query history and user click behaviors, search systems can capture user intent more accurately and thus perform better. However, most existing systems only consider intra-session contexts and may suffer from the problem of lacking contextual information, because short search sessions account for a large proportion in practical scenarios. We believe that in these scenarios, considering more contexts, e.g., cross-session dependencies, may help alleviate the problem and contribute to better performance. Therefore, we propose a novel Hybrid framework for Session Context Modeling (HSCM), which realizes session-level multi-task learning based on the self-attention mechanism. To alleviate the problem of lacking contextual information within current sessions, HSCM exploits the cross-session contexts by sampling user interactions under similar search intents in the historical sessions and further aggregating them into the local contexts. Besides, application of the self-attention mechanism rather than RNN-based frameworks in modeling session-level sequences also helps (1) better capture interactions within sessions, (2) represent the session contexts in parallelization. Experimental results on two practical search datasets show that HSCM not only outperforms strong baseline solutions such as HiNT, CARS, and BERTserini in document ranking, but also performs significantly better than most existing query suggestion methods. According to the results in an additional experiment, we have also found that HSCM is superior to most ranking models in click prediction.
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Kumar, P. Nandha, and M. Hemalathar. "DOMAIN ORIENTED ONTOLOGY BASED SEMANTIC WEB SEARCH METHODOLOGY USING SPARQL QUERY." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 9 (March 11, 2014): 3875–85. http://dx.doi.org/10.24297/ijct.v12i9.2827.
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Semantic web facilitates the use of automated processing of descriptions on the web and exchange and representation of information is done in a meaningful way. But a conventional search engine, the context and semantics of the user query is not analyzed fully and the data is not well structured so it does not provide the relevant content needed by the user. Hence to overcome this problem, semantic web search has become an essential part in today’s world. In this proposed method the user given query is analyzed semantically and the web data is stored in an ontology which is well structured and conventional search is performed using SPARQL query viewer plug-in. Finally, ranking algorithm is used to rank the extracted links for the given query. The results obtained are accurate enough to satisfy the request made by the user. The level of accuracy is enhanced since the ontology is made consistent and query is analyzed semantically to retrieve the correct result. The domain specific evaluation time obtained shows promising results.
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Vuong, Tung, Salvatore Andolina, Giulio Jacucci, and Tuukka Ruotsalo. "Spoken Conversational Context Improves Query Auto-completion in Web Search." ACM Transactions on Information Systems 39, no. 3 (May 6, 2021): 1–32. http://dx.doi.org/10.1145/3447875.
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Web searches often originate from conversations in which people engage before they perform a search. Therefore, conversations can be a valuable source of context with which to support the search process. We investigate whether spoken input from conversations can be used as a context to improve query auto-completion. We model the temporal dynamics of the spoken conversational context preceding queries and use these models to re-rank the query auto-completion suggestions. Data were collected from a controlled experiment and comprised conversations among 12 participant pairs conversing about movies or traveling. Search query logs during the conversations were recorded and temporally associated with the conversations. We compared the effects of spoken conversational input in four conditions: a control condition without contextualization; an experimental condition with the model using search query logs; an experimental condition with the model using spoken conversational input; and an experimental condition with the model using both search query logs and spoken conversational input. We show the advantage of combining the spoken conversational context with the Web-search context for improved retrieval performance. Our results suggest that spoken conversations provide a rich context for supporting information searches beyond current user-modeling approaches.
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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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Giunchiglia, Fausto, Khandaker Tabin Hasan, Rezwan Ahmed, and Sheikh Shaugat Abdullah. "Context Enabled Query and Minimalist Metadata Visualization: A Context Bound Approach for User and Content." International Journal of Computer Applications 69, no. 27 (May 31, 2013): 34–40. http://dx.doi.org/10.5120/12147-8500.
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Menaceur, Sadek, Makhlouf Derdour, and Abdelkrim Bouramoul. "Using Query Expansion Techniques and Content-Based Filtering for Personalizing Analysis in Big Data." International Journal of Information Technology and Web Engineering 15, no. 2 (April 2020): 77–101. http://dx.doi.org/10.4018/ijitwe.2020040104.
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The recent debates on personalizing analyses in a Big Data context are one of the most solicited challenges for business intelligence (BI) administrators. The high-volume, the high-variety, and the high-velocity of Big Data have produced difficulty in storing, processing, and analyzing data in traditional systems. These 3Vs (volume, velocity, and variety) created many new challenges and make them difficult to extract the specific needs of the users. In addition, the user may be faced with the problem of disorientation; he does not know what information really corresponds to his needs. The information personalization systems aim to overcome these problems of disorientation by using a user profile. The effectiveness of the personalization system in a Big Data context is to demonstrate by the relevance and accuracy of the content of the results obtained, according to the needs of the user and the context of the research. Nevertheless, most of the recent research focused on the relational data warehouse personalizing and ignored the integration of the user context into the analysis of OLAP cubes, which is the first concerned to execute the user's multidimensional queries. To deal with this, the authors propose in this article a dynamic personalizing approach in Big Data context using OLAP cubes, based on the Content-Based Filtering, and the Query Expansion techniques. The first step in the proposal consists of processing the user queries by an enrichment technique in order to integrate the user profile and his searching context to reduce the searching space in the OLAP cube, and use the expansion technique to extend the scope of the analysis in the OLAP cube. The retrieved results are: “as relevant as possible” compared to the user's initial request. Afterward, they use information filtering techniques such as content-based filtering to personalize the analysis in the reduced data cube according to the term frequency and cosine similarity. Finally, they present a case study and experiences results to evaluate and validate their approach.
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Gaur, Manas, Kalpa Gunaratna, Vijay Srinivasan, and Hongxia Jin. "ISEEQ: Information Seeking Question Generation Using Dynamic Meta-Information Retrieval and Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 10672–80. http://dx.doi.org/10.1609/aaai.v36i10.21312.
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Conversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and satisfy the users' needs. If realized, such a CIS system has far-reaching benefits in the real world; for example, CIS systems can assist clinicians in pre-screening or triaging patients in healthcare. A key open sub-problem in CIS that remains unaddressed in the literature is generating Information Seeking Questions (ISQs) based on a short initial query from the end-user. To address this open problem, we propose Information SEEking Question generator (ISEEQ), a novel approach for generating ISQs from just a short user query, given a large text corpus relevant to the user query. Firstly, ISEEQ uses a knowledge graph to enrich the user query. Secondly, ISEEQ uses the knowledge-enriched query to retrieve relevant context passages to ask coherent ISQs adhering to a conceptual flow. Thirdly, ISEEQ introduces a new deep generative-adversarial reinforcement learning-based approach for generating ISQs. We show that ISEEQ can generate high-quality ISQs to promote the development of CIS agents. ISEEQ significantly outperforms comparable baselines on five ISQ evaluation metrics across four datasets having user queries from diverse domains. Further, we argue that ISEEQ is transferable across domains for generating ISQs, as it shows the acceptable performance when trained and tested on different pairs of domains. A qualitative human evaluation confirms that ISEEQ generated ISQs are comparable in quality to human-generated questions, and it outperformed the best comparable baseline.
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Chenaina, Tarek, Sameh Neji, and Abdullah Shoeb. "Query Sense Discovery Approach to Realize the User's Search Intent." International Journal of Information Retrieval Research 12, no. 1 (January 2022): 1–18. http://dx.doi.org/10.4018/ijirr.289609.
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The main goal of information retrieval is getting the most relevant documents to a user’s query. So, a search engine must not only understand the meaning of each keyword in the query but also their relative senses in the context of the query. Discovering the query meaning is a comprehensive and evolutionary process; the precise meaning of the query is established as developing the association between concepts. The meaning determination process is modeled by a dynamic system operating in the semantic space of WordNet. To capture the meaning of a user query, the original query is reformulating into candidate queries by combining the concepts and their synonyms. A semantic score characterizing the overall meaning of such queries is calculated, the one with the highest score was used to perform the search. The results confirm that the proposed "Query Sense Discovery" approach provides a significant improvement in several performance measures.
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Vuong, Tung, Salvatore Andolina, Giulio Jacucci, and Tuukka Ruotsalo. "Does More Context Help? Effects of Context Window and Application Source on Retrieval Performance." ACM Transactions on Information Systems 40, no. 2 (April 30, 2022): 1–40. http://dx.doi.org/10.1145/3474055.
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We study the effect of contextual information obtained from a user’s digital trace on Web search performance. Contextual information is modeled using Dirichlet–Hawkes processes (DHP) and used in augmenting Web search queries. The context is captured by monitoring all naturally occurring user behavior using continuous 24/7 recordings of the screen and associating the context with the queries issued by the users. We report a field study in which 13 participants installed a screen recording and digital activity monitoring system on their laptops for 14 days, resulting in data on all Web search queries and the associated context data. A query augmentation (QAug) model was built to expand the original query with semantically related terms. The effects of context window and source were determined by training context models with temporally varying context windows and varying application sources. The context models were then utilized to re-rank the QAug model. We evaluate the context models by using the Web document rankings of the original query as a control condition compared against various experimental conditions: (1) a search context condition in which the context was sourced from search history; (2) a non-search context condition in which the context was sourced from all interactions excluding search history; (3) a comprehensive context condition in which the context was sourced from both search and non-search histories; and (4) an application-specific condition in which the context was sourced from interaction histories captured on a specific application type. Our results indicated that incorporating more contextual information significantly improved Web search rankings as measured by the positions of the documents on which users clicked in the search result pages. The effects and importance of different context windows and application sources, along with different query types are analyzed, and their impact on Web search performance is discussed.
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EITER, THOMAS, MICHAEL FINK, and HANS TOMPITS. "A knowledge-based approach for selecting information sources." Theory and Practice of Logic Programming 7, no. 3 (May 2007): 249–300. http://dx.doi.org/10.1017/s1471068406002754.
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AbstractThrough the Internet and the World-Wide Web, a vast number of information sources has become available, which offer information on various subjects by different providers, often in heterogeneous formats. This calls for tools and methods for building an advanced information-processing infrastructure. One issue in this area is the selection of suitable information sources in query answering. In this paper, we present a knowledge-based approach to this problem, in the setting where one among a set of information sources (prototypically, data repositories) should be selected for evaluating a user query. We use extended logic programs (ELPs) to represent rich descriptions of the information sources, an underlying domain theory, and user queries in a formal query language (here, XML-QL, but other languages can be handled as well). Moreover, we use ELPs for declarative query analysis and generation of a query description. Central to our approach are declarativesource-selection programs, for which we define syntax and semantics. Due to the structured nature of the considered data items, the semantics of such programs must carefully respect implicit context information in source-selection rules, and furthermore combine it with possible user preferences. A prototype implementation of our approach has been realized exploiting the DLV KR system and its PLP front-end for prioritized ELPs. We describe a representative example involving specific movie databases, and report about experimental results.
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Kiran, Mandava Kranthi, and K. Thammi Reddy. "SodhanaRef: a reference management software built using hybrid semantic measure." International Journal of Engineering & Technology 7, no. 2.14 (April 9, 2018): 495. http://dx.doi.org/10.14419/ijet.v7i2.9544.
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Reference management softwares are widely used by the researchers to maintain their collection of scholarly literature that exist in PDF format. Though widely used most of the reference management softwares have no sophisticated Information retrieval except few which offer advanced search that includes search for title, author etc., These softwares in the present day market do not give importance to the semantic similarity or relatedness concept, query expansion and finding the context within the query to find the concept behind the user mentioned query.With SodhanaRef, a solution is offered to deal with the above-mentioned issues by building reference management software using a mix up of corpus-based and knowledge-based semantic measures. Based on the evolution done on about 200 various scholarly literatures in the PDF form, SodhanaRef shows a good performance over Mendeley when compared between these two reference management softwares for title search. The other evaluations for finding the semantic similarity between the user mentioned query and the existing titles in the title search and for identifying the concept behind the query along with identifying the concept of a research publication have shown good results with an average precision between 0.8 to 1 for each query.
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Qiu, Minghui, Xinjing Huang, Cen Chen, Feng Ji, Chen Qu, Wei Wei, Jun Huang, and Yin Zhang. "Reinforced History Backtracking for Conversational Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 15 (May 18, 2021): 13718–26. http://dx.doi.org/10.1609/aaai.v35i15.17617.
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To model the context history in multi-turn conversations has become a critical step towards a better understanding of the user query in question answering systems. To utilize the context history, most existing studies treat the whole context as input, which will inevitably face the following two challenges. First, modeling a long history can be costly as it requires more computation resources. Second, the long context history consists of a lot of irrelevant information that makes it difficult to model appropriate information relevant to the user query. To alleviate these problems, we propose a reinforcement learning based method to capture and backtrack the related conversation history to boost model performance in this paper. Our method seeks to automatically backtrack the history information with the implicit feedback from the model performance. We further consider both immediate and delayed rewards to guide the reinforced backtracking policy. Extensive experiments on a large conversational question answering dataset show that the proposed method can help to alleviate the problems arising from longer context history. Meanwhile, experiments show that the method yields better performance than other strong baselines, and the actions made by the method are insightful.
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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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Singh, Vikram. "Predicting Search Intent Based on In-Search Context for Exploratory Search." International Journal of Advanced Pervasive and Ubiquitous Computing 11, no. 3 (July 2019): 53–75. http://dx.doi.org/10.4018/ijapuc.2019070104.
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Modern information systems are expected to assist users with diverse goals, via exploiting the topical dimension (‘what' the user is searching for) of information needs. However, the intent dimension (‘why' the user is searching) has preferred relatively lesser for the same intention. Traditionally, the intent is an ‘immediate reason, purpose, or goal' that motivates the user search, and captured in search contexts (Pre-search, In-search, Pro-Search), an ideal information system would be able to use. This article proposes a novel intent estimation strategy; based on the intuition that captured intent, and proactively extracts likely results. The captured ‘Pre-search' context adapts query term proximities within matched results beside document-term statistics and pseudo-relevance feedback with user-relevance feedback for In-search. The assessment asserts the superior performance of the proposed strategy over the equivalent on tradeoffs, e.g., novelty, diversity (coverage, topicality), retrieval (precision, recall, F-measure) and exploitation vs exploration.
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Takama, Yasufumi, Takuya Tezuka, Hiroki Shibata, and Lieu-Hen Chen. "Estimation of Search Intents from Query to Context Search Engine." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 3 (May 20, 2020): 316–25. http://dx.doi.org/10.20965/jaciii.2020.p0316.
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This paper estimates users’ search intents when using the context search engine (CSE) by analyzing submitted queries. Recently, due to the increase in the amount of information on the Web and the diversification of information needs, the gap between user’s information needs and a basic search function provided by existing web search engines becomes larger. As a solution to this problem, the CSE that limits its tasks to answer questions about temporal trends has been proposed. It provides three primitive search functions, which users can use in accordance with their purposes. Furthermore, if the system can estimate users’ search intents, it can provide more user-friendly services that contribute the improvement of search efficiency. Aiming at estimating users’ search intents only from submitted queries, this paper analyzes the characteristics of queries in terms of typical search intents when using CSE, and defines classification rules. To show the potential use of the estimated search intents, this paper introduces a learning to rank into CSE. Experimental results show that MAP (mean average precision) is improved by learning rank models separately for different search intents.
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Park, Minsoo, and Tae-Seok Lee. "A longitudinal study of information needs and search behaviors in science and technology." Electronic Library 34, no. 1 (February 1, 2016): 83–98. http://dx.doi.org/10.1108/el-04-2014-0058.
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Purpose This study aims at a longitudinal understanding of the user–system interactions from the context of science and technology at a query level. Design/methodology/approach The authors quantitatively analyzed log data sets culled from more than 24,820,416 queries submitted by users of a national scientific and technical information system, collected in 2008-2011. Findings In the fields of science and technology, the user search behaviors and patterns have remained stable. User queries are short and simple. In all, 80 per cent of the queries are made up of one-three terms. The length of query on a scholarly information system in the fields of science and technology is different from that of Web search. The former is longer than the latter. Search topics have shifted fast. “FUEL BATTERY”, “NANO”, “OLED”, “CAR”, “ROBOT” and “SMARTPHONE” were high-ranked queries from 2008 to 2011. It was found that the time to determine whether the users will stay on the site took about 10 seconds on average from the time of visit. If the users viewed the results of a list generated by the search query and took any action, such as detailed view, export or full-text download, most of them stayed more than 10 minutes on average. Originality/value Longitudinal user research using a query analysis helps to understand the information needs and behavioral patterns of users on information systems related to a specific field and those based on the Web. It also brings insights into the past, present and future events of a field. In other words, it plays a role as a mirror that reflects the flow of time. In the long run, it will be an historic asset. In the future, user studies using a query analysis need to be carried out from various (e.g. social, cultural or other academic disciplines) long-term perspectives on a continuous basis.
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Deepthi, Godavarthi, and A. Mary Sowjanya. "Query-Based Retrieval Using Universal Sentence Encoder." Revue d'Intelligence Artificielle 35, no. 4 (August 31, 2021): 301–6. http://dx.doi.org/10.18280/ria.350404.
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In Natural language processing, various tasks can be implemented with the features provided by word embeddings. But for obtaining embeddings for larger chunks like sentences, the efforts applied through word embeddings will not be sufficient. To resolve such issues sentence embeddings can be used. In sentence embeddings, complete sentences along with their semantic information are represented as vectors so that the machine finds it easy to understand the context. In this paper, we propose a Question Answering System (QAS) based on sentence embeddings. Our goal is to obtain the text from the provided context for a user-query by extracting the sentence in which the correct answer is present. Traditionally, infersent models have been used on SQUAD for building QAS. In recent times, Universal Sentence Encoder with USECNN and USETrans have been developed. In this paper, we have used another variant of the Universal sentence encoder, i.e. Deep averaging network in order to obtain pre-trained sentence embeddings. The results on the SQUAD-2.0 dataset indicate our approach (USE with DAN) performs well compared to Facebook’s infersent embedding.
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Hoang, Hahn H., Tho M. Nguyen, and A. M. Tjoa. "A Semantic Web-based Approach for Context-Aware User Query formulation and Information Retrieval." International Journal of Information Technology and Web Engineering 3, no. 1 (January 2008): 1–23. http://dx.doi.org/10.4018/jitwe.2008010101.
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Kacprzyk, Janusz, and Sławomir Zadrożny. "A novel approach to hierarchical contextual bipolar queries: A winnow operator approach." Control and Cybernetics 51, no. 2 (June 1, 2022): 267–83. http://dx.doi.org/10.2478/candc-2022-0017.
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Abstract We propose a new approach to the bipolar database queries, which involve a necessary (required) and optional (desired) conditions, connected with a non-conventional aggregation operator “and possibly”, combined with a context, exemplified by “find houses which are cheap and – with respect to other houses in town – possibly close to a railroad station”. We use our winnow operator based interpretation of the bipolar queries. We assume that the query, posed by the human user, involves terms, which do not directly relate to attributes, and which are then to be decoded using a concept of a query hierarchy, leading to the queries, which involve terms directly related to attribute values. The original query is considered to be of level 0, at the bottom of the precisiation hierarchy, then its required and optional parts are assumed to be bipolar queries themselves, both accounting for context. The precisiation proceeds further, to level 1 queries, level 2, etc. A real estate related example is provided as illustration.
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Govindan, Abinaya, Gyan Ranjan, and Amit Verma. "Question Answering Module Leveraging Heterogeneous Datasets." International Journal on Natural Language Computing 10, no. 6 (December 31, 2021): 1–15. http://dx.doi.org/10.5121/ijnlc.2021.10601.
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Question Answering has been a well-researched NLP area over recent years. It has become necessary for users to be able to query through the variety of information available - be it structured or unstructured. In this paper, we propose a Question Answering module which a) can consume a variety of data formats - a heterogeneous data pipeline, which ingests data from product manuals, technical data forums, internal discussion forums, groups, etc. b) addresses practical challenges faced in real-life situations by pointing to the exact segment of the manual or chat threads which can solve a user query c) provides segments of texts when deemed relevant, based on user query and business context. Our solution provides a comprehensive and detailed pipeline that is composed of elaborate data ingestion, data parsing, indexing, and querying modules. Our solution is capable of handling a plethora of data sources such as text, images, tables, community forums, and flow charts. Our studies performed on a variety of business-specific datasets represent the necessity of custom pipelines like the proposed one to solve several real-world document question-answering.
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Giaveno, Sara, Anna Osello, Davide Garufi, and Diego Santamaria Razo. "Embodied Carbon and Embodied Energy Scenarios in the Built Environment. Computational Design Meets EPDs." Sustainability 13, no. 21 (October 29, 2021): 11974. http://dx.doi.org/10.3390/su132111974.
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This article aims to study the political, environmental and economic factors in contemporary society that influence new approaches and decision making in design in terms of carbon emissions and energy employment. These issues are increasingly influencing political decision making and public policy throughout every aspect of society, including the design practice. Managing this kind of complexity means adopting new forms of collaboration, methodologies and tools, knowledge and technology sharing. The article aims to narrate a PhD research experience grounded in academy–industry collaboration and aimed at creating a digital methodology for impact evaluation and investment planning. In particular, the digital methodology focuses on responding to international public policy for the sustainable growth of cities, in terms of footprint and energy demand, by including a holistic view of the design process made possible by the use of life-cycle assessment (LCA) procedures. To simplify the calculation, the methodology focuses on the Environmental Product Declaration (EPD) data rather than the entire LCA. The EPD is a document that describes the environmental impacts linked to the production of a specific quantity of product or service. The objective was not to create another evaluation method but to employ the EPD results in combination with parametric and computational procedures. The integration of those procedures by using visual programming and scripting allowed the calculation of Embodied Carbon and Embodied Energy and created a user-friendly interface to query the results. The output obtained included automatic and dynamic diagrams able to identify impact scenarios in terms of CO2 emissions and MJ of embodied energy after the conceptual design stage. The strategic use of the charts lies in their potential to simulate impact conditions and, therefore, in the chance to create sustainable transformation scenarios in the early stages of design. At this point, the influence on choices is at its highest, and the costs are low. Moreover, the methodology represents a platform of collaboration that potentially increases the level of interaction between the actors of the construction process with the consequent improvement in design quality. In conclusion, building the design methodology and testing its performance within a specific sociotechnical context was important in critically evaluating certain topics, for example, the recent European strategies on new technology to reach sustainable objectives, the role of digital tools in proposing solutions towards contemporary social issues, the birth of new forms of partnership and collaboration and the new possibilities coming from digital evaluation approaches.
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Crampton, Jason, Gregory Z. Gutin, and Diptapriyo Majumdar. "Towards Better Understanding of User Authorization Query Problem via Multi-variable Complexity Analysis." ACM Transactions on Privacy and Security 24, no. 3 (August 31, 2021): 1–22. http://dx.doi.org/10.1145/3450768.
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User authorization queries in the context of role-based access control have attracted considerable interest in the past 15 years. Such queries are used to determine whether it is possible to allocate a set of roles to a user that enables the user to complete a task, in the sense that all the permissions required to complete the task are assigned to the roles in that set. Answering such a query, in general, must take into account a number of factors, including, but not limited to, the roles to which the user is assigned and constraints on the sets of roles that can be activated. Answering such a query is known to be NP-hard. The presence of multiple parameters and the need to find efficient and exact solutions to the problem suggest that a multi-variate approach will enable us to better understand the complexity of the user authorization query problem (UAQ). In this article, we establish a number of complexity results for UAQ. Specifically, we show the problem remains hard even when quite restrictive conditions are imposed on the structure of the problem. Our fixed-parameter tractable (FPT) results show that we have to use either a parameter with potentially quite large values or quite a restricted version of UAQ. Moreover, our second FPT algorithm is complex and requires sophisticated, state-of-the-art techniques. In short, our results show that it is unlikely that all variants of UAQ that arise in practice can be solved reasonably quickly in general.
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Yang, Zhihui, Zuozhi Wang, Yicong Huang, Yao Lu, Chen Li, and X. Sean Wang. "Optimizing machine learning inference queries with correlative proxy models." Proceedings of the VLDB Endowment 15, no. 10 (June 2022): 2032–44. http://dx.doi.org/10.14778/3547305.3547310.
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We consider accelerating machine learning (ML) inference queries on unstructured datasets. Expensive operators such as feature extractors and classifiers are deployed as user-defined functions (UDFs), which are not penetrable with classic query optimization techniques such as predicate push-down. Recent optimization schemes (e.g., Probabilistic Predicates or PP) assume independence among the query predicates, build a proxy model for each predicate offline, and rewrite a new query by injecting these cheap proxy models in the front of the expensive ML UDFs. In such a manner, unlikely inputs that do not satisfy query predicates are filtered early to bypass the ML UDFs. We show that enforcing the independence assumption in this context may result in sub-optimal plans. In this paper, we propose CORE, a query optimizer that better exploits the predicate correlations and accelerates ML inference queries. Our solution builds the proxy models online for a new query and leverages a branch-and-bound search process to reduce the building costs. Results on three real-world text, image and video datasets show that CORE improves the query throughput by up to 63% compared to PP and up to 80% compared to running the queries as it is.
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Junkkari, Marko, Johanna Vainio, Kati Iltanen, Paavo Arvola, Heidi Kari, and Jaana Kekäläinen. "Path Expressions in SQL." Journal of Database Management 27, no. 3 (July 2016): 1–22. http://dx.doi.org/10.4018/jdm.2016070101.
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This article focuses on testing a path-oriented querying approach to hierarchical data in relational databases. The authors execute a user study to compare the path-oriented approach and traditional SQL from two perspectives: correctness of queries and time spent in querying. They also analyze what kinds of errors are typical in path-oriented SQL. Path-oriented query languages are popular in the context of object-orientation and XML. However, relational databases are the most common paradigm for storing data and SQL is most common for manipulating data. When querying hierarchical data in SQL, the user must specify join conditions explicitly between hierarchy levels. Path-oriented SQL is a new alternative for expressing hierarchical queries in relational databases. In the authors' study, the users spent significantly less time in writing path-oriented SQL queries and made fewer errors in query formulation.
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Qiu, Tao, Peiliang Xie, Xiufeng Xia, Chuanyu Zong, and Xiaoxu Song. "Aggregated Boolean Query Processing for Document Retrieval in Edge Computing." Electronics 11, no. 12 (June 19, 2022): 1908. http://dx.doi.org/10.3390/electronics11121908.
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Search engines use significant hardware and energy resources to process billions of user queries per day, where Boolean query processing for document retrieval is an essential ingredient. Considering the huge number of users and large scale of the network, traditional query processing mechanisms may not be applicable since they mostly depend on a centralized retrieval method. To remedy this issue, this paper proposes a processing technique for aggregated Boolean queries in the context of edge computing, where each sub-region of the network corresponds to an edge network regulated by an edge server, and the Boolean queries are evaluated in a distributed fashion on the edge servers. This decentralized query processing technique has demonstrated its efficiency and applicability for the document retrieval problem. Experimental results on two real-world datasets show that this technique achieves high query performance and outperforms the traditional centralized methods by 2–3 times.
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Feng, Jiangfan, and Yanhong Liu. "Intelligent Context-Aware and Adaptive Interface for Mobile LBS." Computational Intelligence and Neuroscience 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/489793.
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Context-aware user interface plays an important role in many human-computer Interaction tasks of location based services. Although spatial models for context-aware systems have been studied extensively, how to locate specific spatial information for users is still not well resolved, which is important in the mobile environment where location based services users are impeded by device limitations. Better context-aware human-computer interaction models of mobile location based services are needed not just to predict performance outcomes, such as whether people will be able to find the information needed to complete a human-computer interaction task, but to understand human processes that interact in spatial query, which will in turn inform the detailed design of better user interfaces in mobile location based services. In this study, a context-aware adaptive model for mobile location based services interface is proposed, which contains three major sections: purpose, adjustment, and adaptation. Based on this model we try to describe the process of user operation and interface adaptation clearly through the dynamic interaction between users and the interface. Then we show how the model applies users’ demands in a complicated environment and suggested the feasibility by the experimental results.
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Singh, Vikram, and Chandradeep Kumar. "Improving Hamming-Distance Computation for Adaptive Similarity Search Approach." International Journal of Intelligent Information Technologies 18, no. 2 (April 2022): 1–17. http://dx.doi.org/10.4018/ijiit.296270.
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In the modern context, the similarity is determined by content preserving stimuli, retrieval of relevant ‘nearest neighbor’s objects and the way similar objects are pursued. Current similarity search in hamming-space based strategies finds all the data objects within a threshold hamming-distance for a user query. Though, the number of computations for distance and candidate generation are key concerns from the many years. The hamming-space paradigm extends the range of alternatives for an optimized search experience. A novel ‘counting based similarity search strategy is proposed, with an improved hamming-space, e.g. optimized candidate generation and verification function. The strategy adapts towards the lesser set of user query dimensions and subsequently constraints the hamming-space computations with each data objects, driven by generated statistics. The extensive evaluation asserts that the proposed ‘counting based approach can be combined with any pigeonhole principle-based similarity search to further improve its performance.
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Tudhope, Douglas, Ceri Binding, Dorothee Blocks, and Daniel Cunliffe. "Query expansion via conceptual distance in thesaurus indexed collections." Journal of Documentation 62, no. 4 (July 1, 2006): 509–33. http://dx.doi.org/10.1108/00220410610673873.
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PurposeThe purpose of this paper is to explore query expansion via conceptual distance in thesaurus indexed collectionsDesign/methodology/approachAn extract of the National Museum of Science and Industry's collections database, indexed with the Getty Art and Architecture Thesaurus (AAT), was the dataset for the research. The system architecture and algorithms for semantic closeness and the matching function are outlined. Standalone and web interfaces are described and formative qualitative user studies are discussed. One user session is discussed in detail, together with a scenario based on a related public inquiry. Findings are set in context of the literature on thesaurus‐based query expansion. This paper discusses the potential of query expansion techniques using the semantic relationships in a faceted thesaurus.FindingsThesaurus‐assisted retrieval systems have potential for multi‐concept descriptors, permitting very precise queries and indexing. However, indexer and searcher may differ in terminology judgments and there may not be any exactly matching results. The integration of semantic closeness in the matching function permits ranked results for multi‐concept queries in thesaurus‐indexed applications. An in‐memory representation of the thesaurus semantic network allows a combination of automatic and interactive control of expansion and control of expansion on individual query terms.Originality/valueThe application of semantic expansion to browsing may be useful in interface options where thesaurus structure is hidden.
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BALDUCCINI, MARCELLO, and EMILY C. LEBLANC. "Action-Centered Information Retrieval." Theory and Practice of Logic Programming 20, no. 2 (August 9, 2019): 249–72. http://dx.doi.org/10.1017/s1471068419000097.
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AbstractInformation retrieval (IR) aims at retrieving documents that are most relevant to a query provided by a user. Traditional techniques rely mostly on syntactic methods. In some cases, however, links at a deeper semantic level must be considered. In this paper, we explore a type of IR task in which documents describe sequences of events, and queries are about the state of the world after such events. In this context, successfully matching documents and query requires considering the events’ possibly implicit uncertain effects and side effects. We begin by analyzing the problem, then propose an action language-based formalization, and finally automate the corresponding IR task using answer set programming.
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Sen, Procheta, Debasis Ganguly, and Gareth J. F. Jones. "I Know What You Need: Investigating Document Retrieval Effectiveness with Partial Session Contexts." ACM Transactions on Information Systems 40, no. 3 (July 31, 2022): 1–30. http://dx.doi.org/10.1145/3488667.
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Reducing user effort in finding relevant information is one of the key objectives of search systems. Existing approaches have been shown to effectively exploit the context from the current search session of users for automatically suggesting queries to reduce their search efforts. However, these approaches do not accomplish the end goal of a search system—that of retrieving a set of potentially relevant documents for the evolving information need during a search session. This article takes the problem of query prediction one step further by investigating the problem of contextual recommendation within a search session. More specifically, given the partial context information of a session in the form of a small number of queries, we investigate how a search system can effectively predict the documents that a user would have been presented with had he continued the search session by submitting subsequent queries. To address the problem, we propose a model of contextual recommendation that seeks to capture the underlying semantics of information need transitions of a current user’s search context. This model leverages information from a number of past interactions of other users with similar interactions from an existing search log. To identify similar interactions, as a novel contribution, we propose an embedding approach that jointly learns representations of both individual query terms and also those of queries (in their entirety) from a search log data by leveraging session-level containment relationships. Our experiments conducted on a large query log, namely the AOL, demonstrate that using a joint embedding of queries and their terms within our proposed framework of document retrieval outperforms a number of text-only and sequence modeling based baselines.
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Alloui, Tarek, Imane Boussebough, and Allaoua Chaoui. "A Particle Swarm Optimization Algorithm for Web Information Retrieval." International Journal of Intelligent Information Technologies 11, no. 3 (July 2015): 15–29. http://dx.doi.org/10.4018/ijiit.2015070102.
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The Web has become the largest source of information worldwide and the information, in its various forms, is growing exponentially. So obtaining relevant and up-to-date information has become hard and tedious. This situation led to the emergence of search engines which index today billions of pages. However, they are generic services and they try to aim the largest number of users without considering their information needs in the search process. Moreover, users use generally few words to formulate their queries giving incomplete specifications of their information needs. So dealing this problem within Web context using traditional approaches is vain. This paper presents a novel particle swarm optimization approach for Web information retrieval. It uses relevance feedback to reformulate user query and thus improve the number of relevant results. In the authors' experimental results, they obtained a significant improvement of relevant results using their proposed approach comparing to what is obtained using only the user query into a search engine.
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Samany, Najmeh Neysani, Mahmoud Reza Delavar, Nicholas Chrisman, and Mohammad Reza Malek. "Modelling Spatio-Temporal Relevancy in Urban Context-Aware Pervasive Systems Using Voronoi Continuous Range Query and Multi-Interval Algebra." Mobile Information Systems 9, no. 3 (2013): 189–208. http://dx.doi.org/10.1155/2013/284904.
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Space and time are two dominant factors in context-aware pervasive systems which determine whether an entity is related to the moving user or not. This paper specifically addresses the use of spatio-temporal relations for detecting spatio-temporally relevant contexts to the user. The main contribution of this work is that the proposed model is sensitive to the velocity and direction of the user and applies customized Multi Interval Algebra (MIA) with Voronoi Continuous Range Query (VCRQ) to introduce spatio-temporally relevant contexts according to their arrangement in space. In this implementation the Spatio-Temporal Relevancy Model for Context-Aware Systems (STRMCAS) helps the tourist to find his/her preferred areas that are spatio-temporally relevant. The experimental results in a scenario of tourist navigation are evaluated with respect to the accuracy of the model, performance time and satisfaction of users in 30 iterations of the algorithm. The evaluation process demonstrated the efficiency of the model in real-world applications.
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Allot, Alexis, Qingyu Chen, Sun Kim, Roberto Vera Alvarez, Donald C. Comeau, W. John Wilbur, and Zhiyong Lu. "LitSense: making sense of biomedical literature at sentence level." Nucleic Acids Research 47, W1 (April 25, 2019): W594—W599. http://dx.doi.org/10.1093/nar/gkz289.
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AbstractLiterature search is a routine practice for scientific studies as new discoveries build on knowledge from the past. Current tools (e.g. PubMed, PubMed Central), however, generally require significant effort in query formulation and optimization (especially in searching the full-length articles) and do not allow direct retrieval of specific statements, which is key for tasks such as comparing/validating new findings with previous knowledge and performing evidence attribution in biocuration. Thus, we introduce LitSense, which is the first web-based system that specializes in sentence retrieval for biomedical literature. LitSense provides unified access to PubMed and PMC content with over a half-billion sentences in total. Given a query, LitSense returns best-matching sentences using both a traditional term-weighting approach that up-weights sentences that contain more of the rare terms in the user query as well as a novel neural embedding approach that enables the retrieval of semantically relevant results without explicit keyword match. LitSense provides a user-friendly interface that assists its users to quickly browse the returned sentences in context and/or further filter search results by section or publication date. LitSense also employs PubTator to highlight biomedical entities (e.g. gene/proteins) in the sentences for better result visualization. LitSense is freely available at https://www.ncbi.nlm.nih.gov/research/litsense.
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Kim, Jintae, Shinhyeok Oh, Oh-Woog Kwon, and Harksoo Kim. "Multi-Turn Chatbot Based on Query-Context Attentions and Dual Wasserstein Generative Adversarial Networks." Applied Sciences 9, no. 18 (September 18, 2019): 3908. http://dx.doi.org/10.3390/app9183908.
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To generate proper responses to user queries, multi-turn chatbot models should selectively consider dialogue histories. However, previous chatbot models have simply concatenated or averaged vector representations of all previous utterances without considering contextual importance. To mitigate this problem, we propose a multi-turn chatbot model in which previous utterances participate in response generation using different weights. The proposed model calculates the contextual importance of previous utterances by using an attention mechanism. In addition, we propose a training method that uses two types of Wasserstein generative adversarial networks to improve the quality of responses. In experiments with the DailyDialog dataset, the proposed model outperformed the previous state-of-the-art models based on various performance measures.
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Rogushina, J. V. "Classification of means and methods of the Web semantic retrieval." PROBLEMS IN PROGRAMMING, no. 1 (2017): 030–50. http://dx.doi.org/10.15407/pp2017.01.030.
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Problems associated with the improve ment of information retrieval for open environment are considered and the need for it’s semantization is grounded. Thecurrent state and prospects of development of semantic search engines that are focused on the Web information resources processing are analysed, the criteria for the classification of such systems are reviewed. In this analysis the significant attention is paid to the semantic search use of ontologies that contain knowledge about the subject area and the search users. The sources of ontological knowledge and methods of their processing for the improvement of the search procedures are considered. Examples of semantic search systems that use structured query languages (eg, SPARQL), lists of keywords and queries in natural language are proposed. Such criteria for the classification of semantic search engines like architecture, coupling, transparency, user context, modification requests, ontology structure, etc. are considered. Different ways of support of semantic and otology based modification of user queries that improve the completeness and accuracy of the search are analyzed. On base of analysis of the properties of existing semantic search engines in terms of these criteria, the areas for further improvement of these systems are selected: the development of metasearch systems, semantic modification of user requests, the determination of an user-acceptable transparency level of the search procedures, flexibility of domain knowledge management tools, increasing productivity and scalability. In addition, the development of means of semantic Web search needs in use of some external knowledge base which contains knowledge about the domain of user information needs, and in providing the users with the ability to independent selection of knowledge that is used in the search process. There is necessary to take into account the history of user interaction with the retrieval system and the search context for personalization of the query results and their ordering in accordance with the user information needs. All these aspects were taken into account in the design and implementation of semantic search engine "MAIPS" that is based on an ontological model of users and resources cooperation into the Web.
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Yan, Wei, Li Yan, and Z. M. Ma. "Automated Ranking of Relaxing Query Results Based on XML Structure and Content Preferences." International Journal of Systems and Service-Oriented Engineering 2, no. 1 (January 2011): 21–39. http://dx.doi.org/10.4018/jssoe.2011010102.
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This paper proposes a contextual preference query method of XML structural relaxation and content scoring to resolve the problem of empty or too many answers returned by XML. This paper proposes a XML contextual preference (XCP) model, where all the possible relaxing queries are determined by the users’ preferences. The XCP model allows users to express their interests on XML tree nodes, and then users assign interest scores to their interesting nodes for providing the best answers. A preference query results ranking method is proposed based on the XCP model, which includes: a Clusters_Merging algorithm to merge clusters based on the similarity of the context states, a Finding_Orders algorithm to find representative orders of the clusters, and a Top-k ranking algorithm to deal with the many answers problem. Results of preliminary user studies demonstrate that the method can provide users with most relevant and ranked query results. The efficiency and effectiveness of the approach are also demonstrated by experimental results.
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Bazzanella, Barbara, Heiko Stoermer, and Paolo Bouquet. "Entity Type Disambiguation in User Queries." Journal of Information & Knowledge Management 10, no. 03 (September 2011): 209–24. http://dx.doi.org/10.1142/s0219649211002948.
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Searching for information about individual entities such as persons, locations, events, is an important activity in Internet search today, and is in its core a very semantic-oriented task. Several ways for accessing such information exist, but for locating entity-specific information, search engines are the most commonly used approach. In this context, keyword queries are the primary means of retrieving information about a specific entity. We believe that an important first step of performing such a task is to understand what type of entity the user is looking for. We call this process Entity Type Disambiguation. In this paper, we present a Naive Bayesian Model for entity type disambiguation that explores our assumption that an entity type can be inferred from the attributes a user specifies in a search query. The model has been applied to queries provided by a large sample of participants in an experiment performing an entity search task. The beneficial impact of this approach for the development of new search systems is discussed.
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Wang, Jiamiao, Ling Chen, Lei Li, and Xindong Wu. "BiTTM: A Core Biterms-Based Topic Model for Targeted Analysis." Applied Sciences 11, no. 21 (October 29, 2021): 10162. http://dx.doi.org/10.3390/app112110162.
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While most of the existing topic models perform a full analysis on a set of documents to discover all topics, it is noticed recently that in many situations users are interested in fine-grained topics related to some specific aspects only. As a result, targeted analysis (or focused analysis) has been proposed to address this problem. Given a corpus of documents from a broad area, targeted analysis discovers only topics related with user-interested aspects that are expressed by a set of user-provided query keywords. Existing approaches for targeted analysis suffer from problems such as topic loss and topic suppression because of their inherent assumptions and strategies. Moreover, existing approaches are not designed to address computation efficiency, while targeted analysis is supposed to provide responses to user queries as soon as possible. In this paper, we propose a core BiTerms-based Topic Model (BiTTM). By modelling topics from core biterms that are potentially relevant to the target query, on one hand, BiTTM captures the context information across documents to alleviate the problem of topic loss or suppression; on the other hand, our proposed model enables the efficient modelling of topics related to specific aspects. Our experiments on nine real-world datasets demonstrate BiTTM outperforms existing approaches in terms of both effectiveness and efficiency.
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VALENCIA, MARIA, CODRINA LAUTH, and ERNESTINA MENASALVAS. "EMERGING USER INTENTIONS: MATCHING USER QUERIES WITH TOPIC EVOLUTION IN NEWS TEXT STREAMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 17, supp01 (August 2009): 59–80. http://dx.doi.org/10.1142/s0218488509006030.
Full textAbstract:
Trend detection analysis from unstructured data poses a huge challenge to current advanced, web-enabled knowledge-based systems (KBS). Consolidated studies in topic and trend detection from text streams have concentrated so far mainly on identifying and visualizing dynamically evolving text patterns. From the knowledge modeling perspective identifying and defining new, relevant features that are able to synchronize the emergent user intentions to the dynamicity of the system's structure is a need. Additionally the advanced KBS have to remain highly sensitive to the content change, marked by evolution of trends in topics extracted from text streams. In this paper, we are describing a three-layered approach called the "user-system-content method" that is helping us to identify the most relevant knowledge mapping features derived from the USER, SYSTEM and CONTENT perspectives into an overall "context model", that will enable the advanced KBS to automatically streamline the query enrichment process in a much more user-centered, dynamical and flexible way. After a general introduction to our three-layered approach, we will describe into detail the necessary process steps for the implementation of our method and will present a case study for its integration on a real multimedia web-content portal using news streams as major source of unstructured information.