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Learning Semantic Query Suggestions [chapter]

Edgar Meij, Marc Bron, Laura Hollink, Bouke Huurnink, Maarten de Rijke
2009 Lecture Notes in Computer Science  
We study the problem of semantic query suggestion, a special type of query transformation based on identifying semantic concepts contained in user queries.  ...  We evaluate the utility of different machine learning algorithms, features, and feature types in identifying semantic concepts using a manually developed test bed and show significant improvements over  ...  Conclusion and Future Work We have introduced the task of semantic query suggestion and presented a method that uses supervised machine learning methods to learn which concepts are used in a query.  ... 
doi:10.1007/978-3-642-04930-9_27 fatcat:oc3vakbki5hw5pktxyh5ouqi2u

Learning latent semantic relations from clickthrough data for query suggestion

Hao Ma, Haixuan Yang, Irwin King, Michael R. Lyu
2008 Proceeding of the 17th ACM conference on Information and knowledge mining - CIKM '08  
In this paper, aiming at providing semantically relevant queries for users, we develop a novel, effective and efficient two-level query suggestion model by mining clickthrough data, in the form of two  ...  For a given query raised by a specific user, the Query Suggestion technique aims to recommend relevant queries which potentially suit the information needs of that user.  ...  All of the results show that our latent semantic query suggestion algorithm has a promising future.  ... 
doi:10.1145/1458082.1458177 dblp:conf/cikm/MaYKL08 fatcat:zyxafooy4zgtdbzhwdvm5uugam

Suggesting Relevant Questions for a Query Using Statistical Natural Language Processing Technique [article]

Shriniwas Nayak, Anuj Kanetkar, Hrushabh Hirudkar, Archana Ghotkar, Sheetal Sonawane, Onkar Litake
2022 arXiv   pre-print
Suggesting similar questions for a user query has many applications ranging from reducing search time of users on e-commerce websites, training of employees in companies to holistic learning for students  ...  In this article, a self-learning combined approach is proposed for determining textual similarity that introduces a robust weighted syntactic and semantic similarity index for determining similar questions  ...  This article aims at bridging this gap by suggesting a method that self learns the optimal combination of syntactic and semantic methods [16] .  ... 
arXiv:2204.12069v1 fatcat:djjq4eqdlvaxfgj7fubjfmu3qa

Towards Generalizable Semantic Product Search by Text Similarity Pre-training on Search Click Logs [article]

Zheng Liu, Wei Zhang, Yan Chen, Weiyi Sun, Tianchuan Du, Benjamin Schroeder
2022 arXiv   pre-print
Recently, semantic search has been successfully applied to e-commerce product search and the learned semantic space(s) for query and product encoding are expected to generalize to unseen queries or products  ...  Proper domain-specific fine-tuning with clickstream data can lead to better model generalization, based on a bucketed analysis of a publicly available manual annotated query-product pair da  ...  We would also love to thank the suggestions and comments from reviewers to improve this paper.  ... 
arXiv:2204.05231v2 fatcat:xnlwslur2fhddeelzxxwupkcli

The Influence of Database Structure Representation on Database System Learning and Use

Robert L. Leitheiser, Salvatore T. March
1996 Journal of Management Information Systems  
The evidence for rejecting hypothesis 4 suggests that entity semantics make query language learning and use more difficult than it is with table semantics.  ...  Hypothesis 4 states that differences in representation semantics do not influence query language learning and use.  ... 
doi:10.1080/07421222.1996.11518106 fatcat:whmqkdgcrrfzhbh54uv6cowpy4

Toward a Deep Neural Approach for Knowledge-Based IR [article]

Gia-Hung Nguyen, Lynda Tamine, Laure Soulier, Nathalie Bricon-Souf
2016 arXiv   pre-print
This paper tackles the problem of the semantic gap between a document and a query within an ad-hoc information retrieval task.  ...  This latter issue is tackled by recent works dealing with deep representation learn ing of texts.  ...  of a KB sub-graph, as suggested in [2] .  ... 
arXiv:1606.07211v1 fatcat:jdypcyno3zcwphnoclk44dsfxi

MediaMill: Video Search using a Thesaurus of 500 Machine Learned Concepts

Cees G. M. Snoek, Marcel Worring, Bouke Huurnink, Jan C. van Gemert, Koen E. A. van de Sande, Dennis Koelma, Ork de Rooij
2006 International Conference on Semantics and Digital Media Technologies  
The result set can be browsed easily to obtain the final result for the query.  ...  The core of the system is a thesaurus of 500 automatically detected semantic concepts.  ...  Thus, in video retrieval a recent trend is to learn a lexicon of semantic concepts from multimedia examples and to employ these as entry points in querying the collection.  ... 
dblp:conf/samt/SnoekWHGSKR06 fatcat:wnfmm2r4lreedokeom4eo7jqk4

Taking Advantage of LOM Semantics for Supporting Lesson Authoring [chapter]

Olivier Motelet, Nelson A. Baloian
2005 Lecture Notes in Computer Science  
This article suggests an original approach which uses the structure of a lesson in order to automatically generate LOM-semantic-based queries whereas the user continues to formulate easy-to-write queries  ...  without semantic specifications.  ...  LOM-Semantic-based Retrieval of Learning Object Google and other indexing engines typically provide interfaces for simple queries with a semantic based on logical operators.  ... 
doi:10.1007/11575863_139 fatcat:jtudbm7yffapzfufxatp3e4k6y

SAFIR: a Semantic-Aware Neural Framework for IR

Maristella Agosti, Stefano Marchesin, Gianmaria Silvello
2021 Italian Information Retrieval Workshop  
The semantic mismatch between query and document terms -i.e., the semantic gap -is a long-standing problem in Information Retrieval (IR).  ...  SAFIR jointly learns word, concept, and document representations from scratch. The learned representations encode both polysemy and synonymy to address the semantic gap.  ...  The semantic matching component uses the learned representations to perform semantic matching between knowledge-enhanced query and documents.  ... 
dblp:conf/iir/Agosti0S21 fatcat:xufauywjtfds3o77mkyoppguu4

Getting Started with Neural Models for Semantic Matching in Web Search [article]

Kezban Dilek Onal, Ismail Sengor Altingovde, Pinar Karagoz, Maarten de Rijke
2016 arXiv   pre-print
tasks: query suggestion, ad retrieval, and document retrieval.  ...  We conclude with an assessment of the state-of-the-art and suggestions for future work.  ...  Query suggestion Next we turn to neural models for semantic matching for query suggestions.  ... 
arXiv:1611.03305v1 fatcat:agdgj7allbczxcyteuomswn574

Academic Administration and Management Scenarios on the Semantic Web

Feng Tao, S.A. Khoja, H. Davis, A. Gravell
2008 2008 Eighth IEEE International Conference on Advanced Learning Technologies  
managing learning resources in order to provide a value-added semantics layer where semantic annotation, query and reasoning can be carried out to support management requirements in Ed-Scene scenarios  ...  This paper describes scenarios developed as part of the Ed-Scene project which aims to provide intelligent services to the academic stakeholders (teachers, students, administrators, employers) by semantically  ...  SPARQL is a query language designed for querying semantic web triples.  ... 
doi:10.1109/icalt.2008.92 dblp:conf/icalt/TaoKDG08 fatcat:ol5d55bozze7bboxwqv7clktsy

Data-Science Recommendation System using Semantic Technology

2019 International Journal of Engineering and Advanced Technology  
Data science recommendation system using semantic web data-science ontology and service-oriented architecture is proposed in our work to recommend students the appropriate resources for their queries.  ...  Students at the starting of the learning phase don't know all the technological and algorithmic aspects related to data science.  ...  This approach can suggest more specific and elaborate content than machine learning approach.  ... 
doi:10.35940/ijeat.a9375.109119 fatcat:gj5lbvh4qvc6xls6oxqijjicyy

Query Understanding for Natural Language Enterprise Search [article]

Francisco Borges, Georgios Balikas, Marc Brette, Guillaume Kempf, Arvind Srikantan, Matthieu Landos, Darya Brazouskaya, Qianqian Shi
2020 arXiv   pre-print
Among several submodules of the system we detail the role of a Deep Learning Named Entity Recognizer. The paper concludes with discussion over the lessons learned while developing this product.  ...  The engine tries to understand the meaning of the queries and to map the query words to the symbols it supports like Persons, Organizations, Time Expressions etc..  ...  Query suggestions are built to be interpretable, but sometimes deep learning can be unpredictable and fails at tagging suggested queries.  ... 
arXiv:2012.06238v1 fatcat:t5ck2jmaqngexgmgoylpoqt3ua

Are all negatives created equal in contrastive instance discrimination? [article]

Tiffany Tianhui Cai, Jonathan Frankle, David J. Schwab, Ari S. Morcos
2020 arXiv   pre-print
Finally, we studied the properties of negatives that affect their hardness, and found that hard negatives were more semantically similar to the query, and that some negatives were more consistently easy  ...  ., 2020), we divided negatives by their difficulty for a given query and studied which difficulty ranges were most important for learning useful representations.  ...  These negatives are predominately in the same ImageNet class as the query, suggesting that semantically identical (but superficially dissimilar) negatives are unhelpful or detrimental to contrastive learning  ... 
arXiv:2010.06682v2 fatcat:5w5twrltafeu3jhwpz5onlwthm

Page 33 of Computational Linguistics Vol. 14, Issue 3 [page]

1988 Computational Linguistics  
) for the erroneous proposition Classification(DR.SMITH, _classval:&CLASSVALUES) appearing in the semantic representation of the stu- dent’s query, resulting in a suggested revised semantic representation  ...  The important point is that all of the revised semantic representations resulting from these suggestions represent queries that are apropos to the plan that IS is constructing. 4.3.2 SELECTING THE APPROPRIATE  ... 
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