Industry to move by UCF (with photo of Florida Governor Bob Graham announcing that Westinghouse will soon be building across from UCF); Colbourn foresees crunch; Llewellyn named dean of Arts and Sciences; Future UCF Newsfronts: Development director named; Ham radio club offers students new waves; UCF Summer blood drive on; Bo\u27s jersey ripped off; Residents upset by apartment conditions; Where to go when Aunt Millie visits (with photos); Future Comment: New chief outlines Future editorial policies; Apartments are wrecked; SAGA service lacking; Professors predict Clark won\u27t be tried for trip; Future Sights and Sounds: Cartoonist broadcasts music preference.https://stars.library.ucf.edu/centralfloridafuture/1401/thumbnail.jp

de Rijke, M.

Meij, E.

Reinanda, R.

English

University of Central Florida (UCF): STARS (Showcase of Text, Archives, Research & Scholarship)

Central Florida Future, Vol. 13 No. 01, July 2, 1980

International Migration, Integration and Social Cohesion online publications

UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl)UvA-DARE (Digital Academic Repository)Mining, ranking and recommending entity aspectsabstractReinanda, R.; Meij, E.; de Rijke, M.Publication date2015Document VersionFinal published versionLink to publicationCitation for published version (APA):Reinanda, R., Meij, E., & de Rijke, M. (2015). Mining, ranking and recommending entityaspects: abstract. 17. Abstract from 14th Dutch-Belgian Information Retrieval Workshop,Amsterdam, Netherlands. https://ilps.science.uva.nl/wp-content/uploads/sites/8/2015/11/DIR2015-proceedings.pdfGeneral rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s)and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an opencontent license (like Creative Commons).Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, pleaselet the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the materialinaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letterto: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. Youwill be contacted as soon as possible.Download date:26 Jul 2022ProceedingsProceedings of the 14th Dutch-Belgian Information Retrieval WorkshopMining, Ranking and Recommending Entity Aspects(Abstract)Ridho Reinanda†r.reinanda@uva.nlEdgar Meij‡emeij@yahoo-inc.comMaarten de Rijke†derijke@uva.nl† University of Amsterdam, Amsterdam, The Netherlands‡ Yahoo Labs, London, United KingdomABSTRACTWith the proliferation of mobile devices, an increasing amount ofavailable structured data, and the development of advanced searchresult pages, modern-day web search is increasingly geared to-wards entity-oriented search [1, 7, 9]. A first step and commonstrategy to address such information needs is to identify entitieswithin queries, commonly known as entity linking [6]. Semanticinformation that is gleaned from the linked entities (such as en-tity types, attributes, or related entities) is used in various ways bymodern search engines, e.g., for presenting an entity card, showingactionable links, and/or recommending related entities [2, 4, 5].Entities are not typically searched for on their own, however, butoften combined with other entities, types, attributes/properties, re-lationships, or keywords [9]. Such query completions in the contextof an entity are commonly referred to as entity-oriented intents orentity aspects [8, 11]. In this paper we study the problem of miningand ranking entity aspects in the context of web search. In particu-lar, we study four related tasks in this paper: (1) identifying entityaspects, (2) estimating the importance of aspects with respect toan entity, (3) ranking entity aspects with respect to a current queryand/or user session, and (4) leveraging entity aspects for query rec-ommendation.The first step in identifying entity aspects involves extractingcommon queries in the context of an entity and grouping thembased on their similarity. We perform this process offline and in-vestigate three matching strategies for clustering queries into entityaspects: lexical, semantic, and click-based. Gathering such entityaspects can already be valuable on its own since they can be usedto, e.g., discover bursty or consistent entity intents or to determineentity type-specific aspects [8].In the next step we rank the obtained entity aspects for each en-tity in a query-independent fashion using three distinct strategies.This provides us with a mechanism to retrieve the most relevantaspects for a given entity on its own, which, in turn, can be usedto, e.g., summarize the most pertinent information needs around anentity or to help the presentation of entity-oriented search resultssuch as customized entity cards on SERPs [1].The third task that we consider is aspect recommendation. Givenan entity and a certain aspect as input, recommend related aspects.This task is motivated by the increasing proliferation of entity-oriented interface elements for web search that can be improvedby, e.g., (re)ranking particular items on these elements. Recom-mending aspects for an entity can also help users discover new andserendipitous information with respect to an entity. We considertwo approaches to recommend aspects: semantic and behavioral.In the semantic approach, relatedness is estimated from a semanticrepresentation of aspects. The behavioral approach is based on the“flow” of aspect transitions in actual user sessions, modeled usingan adapted version of the query-flow graph [3].We perform large-scale experiments on both a publicly avail-able and a commercial search engine’s query log to evaluate ourproposed methods for mining, ranking, and recommending entityaspects, as well as for recommending queries. We perform con-trastive experiments using various similarity measures and rankingstrategies. We evaluate the quality of the extracted entity aspects bymanually evaluating the generated clusters. Since manually evalu-ating aspect rankings for entities is not straightforward, we resortto automatic evaluation based on adjacent query pairs. A similarautomatic evaluation strategy is also employed to evaluate aspectrecommendations.We find that entropy-based methods achieve the best performancecompared to maximum likelihood and language modeling on thetask of entity aspect ranking. Concerning aspect recommendationwe find that combining aspect transitions within a session and se-mantic relatedness give the best performance. Furthermore, weshow that the entity aspects can be effectively utilized for queryrecommendation.This work was presented at SIGIR 2015 [10].References[1] N. Balasubramanian and S. Cucerzan. Topic pages: An alternative tothe ten blue links. In IEEE-ICSC 2010, 2010.[2] R. Blanco, B. B. Cambazoglu, P. Mika, and N. Torzec. Entityrecommendations in web search. In ISWC ’13, 2013.[3] P. Boldi, F. Bonchi, C. Castillo, D. Donato, A. Gionis, and S. Vigna.The query-flow graph: Model and applications. In CIKM ’08, 2008.[4] L. Hollink, P. Mika, and R. Blanco. Web usage mining with semanticanalysis. In WWW ’13, 2013.[5] T. Lin, P. Pantel, M. Gamon, A. Kannan, and A. Fuxman. Activeobjects: Actions for entity-centric search. In WWW ’12, 2012.[6] E. Meij, W. Weerkamp, and M. de Rijke. Adding semantics tomicroblog posts. In WSDM 2012, 2012.[7] P. Pantel and A. Fuxman. Jigs and lures: Associating web querieswith structured entities. In ACL ’11, 2011.[8] P. Pantel, T. Lin, and M. Gamon. Mining entity types from querylogs via user intent modeling. In ACL’12, 2012.[9] J. Pound, P. Mika, and H. Zaragoza. Ad-hoc object retrieval in theweb of data. In WWW ’10, pages 771–780, 2010.[10] R. Reinanda, E. Meij, and M. de Rijke. Mining, ranking andrecommending entity aspects. In SIGIR 2015: 38th internationalACM SIGIR conference on Research and development in informationretrieval. ACM, August 2015.[11] X. Yin and S. Shah. Building taxonomy of web search intents forname entity queries. In WWW ’10, 2010.17

Mining, ranking and recommending entity aspects:abstract

UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl)UvA-DARE (Digital Academic Repository)Mining, ranking and recommending entity aspectsabstractReinanda, R.; Meij, E.; de Rijke, M.Publication date2015Document VersionFinal published versionLink to publicationCitation for published version (APA):Reinanda, R., Meij, E., & de Rijke, M. (2015). Mining, ranking and recommending entityaspects: abstract. 17. Abstract from 14th Dutch-Belgian Information Retrieval Workshop,Amsterdam, Netherlands. https://ilps.science.uva.nl/wp-content/uploads/sites/8/2015/11/DIR2015-proceedings.pdfGeneral rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s)and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an opencontent license (like Creative Commons).Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, pleaselet the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the materialinaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letterto: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. Youwill be contacted as soon as possible.Download date:09 Mar 2023ProceedingsProceedings of the 14th Dutch-Belgian Information Retrieval WorkshopMining, Ranking and Recommending Entity Aspects(Abstract)Ridho Reinanda†r.reinanda@uva.nlEdgar Meij‡emeij@yahoo-inc.comMaarten de Rijke†derijke@uva.nl† University of Amsterdam, Amsterdam, The Netherlands‡ Yahoo Labs, London, United KingdomABSTRACTWith the proliferation of mobile devices, an increasing amount ofavailable structured data, and the development of advanced searchresult pages, modern-day web search is increasingly geared to-wards entity-oriented search [1, 7, 9]. A first step and commonstrategy to address such information needs is to identify entitieswithin queries, commonly known as entity linking [6]. Semanticinformation that is gleaned from the linked entities (such as en-tity types, attributes, or related entities) is used in various ways bymodern search engines, e.g., for presenting an entity card, showingactionable links, and/or recommending related entities [2, 4, 5].Entities are not typically searched for on their own, however, butoften combined with other entities, types, attributes/properties, re-lationships, or keywords [9]. Such query completions in the contextof an entity are commonly referred to as entity-oriented intents orentity aspects [8, 11]. In this paper we study the problem of miningand ranking entity aspects in the context of web search. In particu-lar, we study four related tasks in this paper: (1) identifying entityaspects, (2) estimating the importance of aspects with respect toan entity, (3) ranking entity aspects with respect to a current queryand/or user session, and (4) leveraging entity aspects for query rec-ommendation.The first step in identifying entity aspects involves extractingcommon queries in the context of an entity and grouping thembased on their similarity. We perform this process offline and in-vestigate three matching strategies for clustering queries into entityaspects: lexical, semantic, and click-based. Gathering such entityaspects can already be valuable on its own since they can be usedto, e.g., discover bursty or consistent entity intents or to determineentity type-specific aspects [8].In the next step we rank the obtained entity aspects for each en-tity in a query-independent fashion using three distinct strategies.This provides us with a mechanism to retrieve the most relevantaspects for a given entity on its own, which, in turn, can be usedto, e.g., summarize the most pertinent information needs around anentity or to help the presentation of entity-oriented search resultssuch as customized entity cards on SERPs [1].The third task that we consider is aspect recommendation. Givenan entity and a certain aspect as input, recommend related aspects.This task is motivated by the increasing proliferation of entity-oriented interface elements for web search that can be improvedby, e.g., (re)ranking particular items on these elements. Recom-mending aspects for an entity can also help users discover new andserendipitous information with respect to an entity. We considertwo approaches to recommend aspects: semantic and behavioral.In the semantic approach, relatedness is estimated from a semanticrepresentation of aspects. The behavioral approach is based on the“flow” of aspect transitions in actual user sessions, modeled usingan adapted version of the query-flow graph [3].We perform large-scale experiments on both a publicly avail-able and a commercial search engine’s query log to evaluate ourproposed methods for mining, ranking, and recommending entityaspects, as well as for recommending queries. We perform con-trastive experiments using various similarity measures and rankingstrategies. We evaluate the quality of the extracted entity aspects bymanually evaluating the generated clusters. Since manually evalu-ating aspect rankings for entities is not straightforward, we resortto automatic evaluation based on adjacent query pairs. A similarautomatic evaluation strategy is also employed to evaluate aspectrecommendations.We find that entropy-based methods achieve the best performancecompared to maximum likelihood and language modeling on thetask of entity aspect ranking. Concerning aspect recommendationwe find that combining aspect transitions within a session and se-mantic relatedness give the best performance. Furthermore, weshow that the entity aspects can be effectively utilized for queryrecommendation.This work was presented at SIGIR 2015 [10].References[1] N. Balasubramanian and S. Cucerzan. Topic pages: An alternative tothe ten blue links. In IEEE-ICSC 2010, 2010.[2] R. Blanco, B. B. Cambazoglu, P. Mika, and N. Torzec. Entityrecommendations in web search. In ISWC ’13, 2013.[3] P. Boldi, F. Bonchi, C. Castillo, D. Donato, A. Gionis, and S. Vigna.The query-flow graph: Model and applications. In CIKM ’08, 2008.[4] L. Hollink, P. Mika, and R. Blanco. Web usage mining with semanticanalysis. In WWW ’13, 2013.[5] T. Lin, P. Pantel, M. Gamon, A. Kannan, and A. Fuxman. Activeobjects: Actions for entity-centric search. In WWW ’12, 2012.[6] E. Meij, W. Weerkamp, and M. de Rijke. Adding semantics tomicroblog posts. In WSDM 2012, 2012.[7] P. Pantel and A. Fuxman. Jigs and lures: Associating web querieswith structured entities. In ACL ’11, 2011.[8] P. Pantel, T. Lin, and M. Gamon. Mining entity types from querylogs via user intent modeling. In ACL’12, 2012.[9] J. Pound, P. Mika, and H. Zaragoza. Ad-hoc object retrieval in theweb of data. In WWW ’10, pages 771–780, 2010.[10] R. Reinanda, E. Meij, and M. de Rijke. Mining, ranking andrecommending entity aspects. In SIGIR 2015: 38th internationalACM SIGIR conference on Research and development in informationretrieval. ACM, August 2015.[11] X. Yin and S. Shah. Building taxonomy of web search intents forname entity queries. In WWW ’10, 2010.17

NARCIS 

Mining, ranking and recommending entity aspects: abstract

UvA-DARE

https://pure.uva.nl/ws/files/43432755/Mining_ranking_and_recommending_entity_aspects.pdf

Central Florida Future, Vol. 13 No. 01, July 2, 1980

Abstract

Similar works

Full text

Available Versions

University of Central Florida (UCF): STARS (Showcase of Text, Archives, Research & Scholarship)

International Migration, Integration and Social Cohesion online publications

International Migration, Integration and Social Cohesion online publications

NARCIS

UvA-DARE