research-article

Explanatory semantic relatedness and explicit spatialization for exploratory search

Authors:

Samuel H. Carton,

Mahmood Quaderi,

Johannes Schöning,

Doug DowneyAuthors Info & Claims

SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

Pages 415 - 424

https://doi.org/10.1145/2348283.2348341

Published: 12 August 2012 Publication History

Abstract

Exploratory search, in which a user investigates complex concepts, is cumbersome with today's search engines. We present a new exploratory search approach that generates interactive visualizations of query concepts using thematic cartography (e.g. choropleth maps, heat maps). We show how the approach can be applied broadly across both geographic and non-geographic contexts through explicit spatialization, a novel method that leverages any figure or diagram -- from a periodic table, to a parliamentary seating chart, to a world map -- as a spatial search environment. We enable this capability by introducing explanatory semantic relatedness measures. These measures extend frequently-used semantic relatedness measures to not only estimate the degree of relatedness between two concepts, but also generate human-readable explanations for their estimates by mining Wikipedia's text, hyperlinks, and category structure. We implement our approach in a system called Atlasify, evaluate its key components, and present several use cases.

References

[1]

Bergstrom, T. and Karahalios, K. 2009. Conversation clusters: grouping conversation topics through human-computer dialog. CHI '09.

Digital Library

[2]

Bertin, J. and Berg, W.J. 1989. Semiology of Graphics. University of Wisconsin Press.

Digital Library

[3]

Bozzon, A., Brambilla, M., Ceri, S. and Fraternali, P. 2010. Liquid query: multi-domain exploratory search on the web. WWW '10.

Digital Library

[4]

Budanitsky, A. and Hirst, G. 2006. Evaluating WordNet-based Measures of Lexical Semantic Relatedness. Computational Linguistics. 32, 1 (2006), 13--47.

Digital Library

[5]

Card, S.K., Mackinlay, J.D. and Shneiderman, B. 1999. Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann.

Digital Library

[6]

Dodge, M., Kitchin, R. and Perkins, C. 2011. Introduction to The Map Reader. The Map Reader: Theories of Mapping Practice and Cartographic Representation. Wiley.

[7]

Eisenstein, J., O'Connor, B., Smith, N.A. and Xing, Eric P. 2010. A Latent Variable Model for Geographic Lexical Variation. EMNLP '10.

Digital Library

[8]

Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G. and Ruppin, E. 2002. Placing Search in Context: The Concept Revisited. ACM Transactions on Information Systems. 20, 1 (2002), 116--131.

Digital Library

[9]

Gabrilovich, E. and Markovitch, S. 2007. Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis. IJCAI '07.

Digital Library

[10]

Gabrilovich, E. and Markovitch, S. 2009. Wikipedia-based Semantic Interpretation for Natural Language Processing. JAIR. 34, (2009), 443--498.

Digital Library

[11]

Goodchild, M.F., Yuan, M. and Cova, T.J. 2007. Towards a general theory of geographic representation in GIS. IJGIS. 21, 3 (2007), 239--260.

Digital Library

[12]

Google Correlate: http://correlate.googlelabs.com/. Accessed: 2011-07--28.

[13]

Hearst, M.A. 2009. Search User Interfaces. Cambridge University Press.

Digital Library

[14]

Hecht, B. and Gergle, D. 2010. The Tower of Babel Meets Web 2.0: User-Generated Content and Its Applications in a Multilingual Context. CHI '10.

Digital Library

[15]

Hecht, B. and Raubal, M. 2008. GeoSR: Geographically explore semantic relations in world knowledge. AGILE '08.

[16]

Hecht, B. and Schöning, J. 2008. Mapping the Zeitgeist. GIScience '08 (Extended Abstracts).

[17]

Hornbæk, K. and Frøkjær, E. 1999. Do Thematic Maps Improve Information Retrieval? INTERACT '99 (1999), 1--8.

[18]

Joachims, T. 2006. Training Linear SVMs in Linear Time. KDD '06.

Digital Library

[19]

Jones, C.B., Purves, R.S., Clough, P.D. and Joho, H. 2008. Modelling Vague Places with Knowledge from the Web. IJGIS. 22, 10 (2008), 1045--1065.

Digital Library

[20]

MacEachren, A.M. 1982. The Role of Complexity and Symbolization Method in Thematic Maps. Annals of the Assoc. of American Geographers. 72, 4 (1982), 495--513.

[21]

Miller, G.A. and Charles, W.G. 1991. Contextual correlates of semantic similarity. Language and Cognitive Processes. 6, 1 (1991), 1--28.

[22]

Milne, D. and Witten, I.H. 2008. An Effective, Low-Cost Measure of Semantic Relatedness Obtained from Wikipedia Links. WIKI-AI '08.

[23]

Milne, D. and Witten, I.H. 2008. Learning to link with Wikipedia. CIKM '08.

Digital Library

[24]

Montello, D.R. 2002. Cognitive Map-Design Research in the Twentieth Century: Theoretical and Empirical Approaches. CAGIS. 29, 3 (2002), 283--304.

[25]

Pedersen, T., Pakhomov, S.V.S., Patwardhan, S. and Chute, C.G. 2006. Measures of semantic similarity and relatedness in the biomedical domain. Journal of Biomedical Informatics. 40, 3 (2006), 288--299.

Digital Library

[26]

Ponzetto, S.P. and Strube, M. 2007. Knowledge Derived From Wikipedia For Computing Semantic Relatedness. JAIR. 30, (2007), 181--212.

Digital Library

[27]

Popescu, A. and Grefenstette, G. 2010. Mining User Home Location and Gender from Flickr Tags. ICWSM '10.

[28]

Popescu, A. and Grefenstette, G. 2010. Spatiotemporal mapping of Wikipedia concepts. JCDL '10 (2010), 129--138.

Digital Library

[29]

Potthast, M., Stein, B. and Anderka, M. 2008. A Wikipedia-based multilingual retrieval model. ECIR '08.

Digital Library

[30]

Radinsky, K., Agichtein, E., Gabrilovich, E. and Markovitch, S. 2011. A Word at a Time: Computing Word Relatedness using Temporal Semantic Analysis. WWW '11.

Digital Library

[31]

Rajaraman, A. 2009. Kosmix: High-Performance Topic Exploration using the Deep Web. Proceedings of the VLDB Endowment. 2, 2009 (2009), 1524--1529.

Digital Library

[32]

Risch, J.S., Rex, D.B., Dowson, S.T., Walters, T.B., May, R.A. and Moon, B.D. 1997. The STARLIGHT information visualization system. INFOVIS '97.

Digital Library

[33]

Rose, D.E. and Levinson, D. 2004. Understanding user goals in web search. WWW '04.

Digital Library

[34]

Sheesley, B. 2009. Data Probing and Info Window Design on Web-based Maps. Axis Maps Blog. http://www.axismaps .com/blog/2009/07/data-probing-and-info-window-design-on-web-based-maps/. Accessed: 2012-06-04.

[35]

Shneiderman, B. 1996. The eyes have it: a task by data type taxonomy for information visualizations. IEEE Symposium on Visual Languages '96 (Sep. 1996), 336 --343.

Digital Library

[36]

Skupin, A. and Fabrikant, S.I. 2003. Spatialization Methods: A Cartographic Research Agenda for Non-geographic Information Visualization. CAGIS. 30, 2 (2003), 95--115.

[37]

Slocum, T.A., McMaster, R.B., Kessler, F.C. and Howard, H.H. 2009. Thematic Cartography and Geovisualization. Prentice Hall.

[38]

Strube, M. and Ponzetto, S.P. 2006. WikiRelate! Computing Semantic Relatedness Using Wikipedia. AAAI '06.

Digital Library

[39]

White, R., Muresan, G. and Marchionini, G. 2006. Evaluating Exploratory Search Systems. SIGIR '06 Workshop on Evaluating Exploratory Search (2006).

[40]

White, R., Roth, R. and Marchionini, G. 2009. Exploratory search: beyond the query-response paradigm. Morgan & Claypool.

Digital Library

[41]

Zesch, T. and Gurevych, I. 2006. Automatically creating datasets for measures of semantic relatedness. ACL-Workshop on Linguistic Distances.

Digital Library

[42]

Zesch, T. and Gurevych, I. 2010. The More the Better? Assessing the Influence of Wikipedia's Growth on Semantic Relatedness Measures. LREC '10.

[43]

Zesch, T. and Gurevych, I. 2009. Wisdom of crowds versus wisdom of linguists -- measuring the semantic relatedness of words. Natural Language Engineering. 16, 1 (2009), 25--59.

Digital Library

[44]

Zesch, T., Gurevych, I. and Mühlhäuser, M. 2007. Analyzing and accessing Wikipedia as a lexical semantic resource. Data Structures for Linguistic Resources and Applications.

Cited By

Formica ATaglino F(2023)Semantic relatedness in DBpedia: A comparative and experimental assessmentInformation Sciences10.1016/j.ins.2022.11.025621(474-505)Online publication date: Apr-2023
https://doi.org/10.1016/j.ins.2022.11.025
Kaur HDowney DSingh ACheng EWeld DBragg J(2022)FeedLens: Polymorphic Lenses for Personalizing Exploratory Search over Knowledge GraphsProceedings of the 35th Annual ACM Symposium on User Interface Software and Technology10.1145/3526113.3545631(1-15)Online publication date: 29-Oct-2022
https://dl.acm.org/doi/10.1145/3526113.3545631
Cashman DXu SDas SHeimerl FLiu CHumayoun SGleicher MEndert AChang R(2021)CAVA: A Visual Analytics System for Exploratory Columnar Data Augmentation Using Knowledge GraphsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.303044327:2(1731-1741)Online publication date: Feb-2021
https://doi.org/10.1109/TVCG.2020.3030443
Show More Cited By

Index Terms

Explanatory semantic relatedness and explicit spatialization for exploratory search
1. Human-centered computing

Recommendations

Evaluating semantic similarity and relatedness over the semantic grouping of clinical term pairs

Display Omitted Objective: develop a method to quantify the similarity and relatedness of biomedical and clinical term pairs.Semantic similarity and relatedness measures exploit information extrapolated from the Unified Medical Language System.Evaluates ...
A link-based visual search engine for Wikipedia
JCDL '11: Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries

This paper introduces HMpara, a new search engine that aims to make Wikipedia easier to explore. It works on top of the encyclopedia's existing link structure, abstracting away from document content and allowing users to navigate the resource at a ...
Exploratory search with semantic transformations using collaborative knowledge bases
WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining

Sometimes we search for simple facts. Other times we search for relationships between concepts. While existing information retrieval systems work well for simple searches, they are less satisfying for complex inquiries because of the ill-structured ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

August 2012

1236 pages

ISBN:9781450314725

DOI:10.1145/2348283

General Chair:
William Hersh
Oregon Health & Science University, USA
,
Program Chairs:
Jamie Callan
Carnegie Mellon University, USA
,
Yoelle Maarek
Yahoo! Research, Israel
,
Mark Sanderson
Royal Melbourne Institute of Technology, Australia

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 August 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGIR '12

Sponsor:

SIGIR

SIGIR '12: The 35th International ACM SIGIR conference on research and development in Information Retrieval

August 12 - 16, 2012

Oregon, Portland, USA

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

29
Total Citations
View Citations
1,081
Total Downloads

Downloads (Last 12 months)21
Downloads (Last 6 weeks)2

Reflects downloads up to 13 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Formica ATaglino F(2023)Semantic relatedness in DBpedia: A comparative and experimental assessmentInformation Sciences10.1016/j.ins.2022.11.025621(474-505)Online publication date: Apr-2023
https://doi.org/10.1016/j.ins.2022.11.025
Kaur HDowney DSingh ACheng EWeld DBragg J(2022)FeedLens: Polymorphic Lenses for Personalizing Exploratory Search over Knowledge GraphsProceedings of the 35th Annual ACM Symposium on User Interface Software and Technology10.1145/3526113.3545631(1-15)Online publication date: 29-Oct-2022
https://dl.acm.org/doi/10.1145/3526113.3545631
Cashman DXu SDas SHeimerl FLiu CHumayoun SGleicher MEndert AChang R(2021)CAVA: A Visual Analytics System for Exploratory Columnar Data Augmentation Using Knowledge GraphsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.303044327:2(1731-1741)Online publication date: Feb-2021
https://doi.org/10.1109/TVCG.2020.3030443
Savino GSturdee MRundé SLohmeier CHecht BPrandi CNunes NSchöning J(2020)MapRecorder: analysing real-world usage of mobile map applicationsBehaviour & Information Technology10.1080/0144929X.2020.171473340:7(646-662)Online publication date: 12-Feb-2020
https://doi.org/10.1080/0144929X.2020.1714733
Sen SSwoap ALi QDippenaar INgo MPujol SGold RBoatman BHecht BJackson B(2019)Toward Universal Spatialization Through Wikipedia-Based Semantic EnhancementACM Transactions on Interactive Intelligent Systems10.1145/32137699:2-3(1-29)Online publication date: 9-Apr-2019
https://dl.acm.org/doi/10.1145/3213769
Sadr HSoleimandarabi MPedram MTeshnelab M(2019)Unified Topic-Based Semantic Models: A Study in Computing the Semantic Relatedness of Geographic Terms2019 5th International Conference on Web Research (ICWR)10.1109/ICWR.2019.8765257(134-140)Online publication date: Apr-2019
https://doi.org/10.1109/ICWR.2019.8765257
Hadj Taieb MZesch TBen Aouicha M(2019)A survey of semantic relatedness evaluation datasets and proceduresArtificial Intelligence Review10.1007/s10462-019-09796-3Online publication date: 23-Dec-2019
https://doi.org/10.1007/s10462-019-09796-3
Chen MMeng CHuang GZaniolo C(2019)Neural Article Pair Modeling for Wikipedia Sub-article MatchingMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-10997-4_1(3-19)Online publication date: 18-Jan-2019
https://doi.org/10.1007/978-3-030-10997-4_1
Saif AOmar NAb Aziz MZainodin USalim N(2018)Semantic concept model using Wikipedia semantic featuresJournal of Information Science10.1177/016555151770623144:4(526-551)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1177/0165551517706231
Kumar VBakhshi SKennedy LShamma DBielikova MHerder ECena FDesmarais M(2017)The Force WithinProceedings of the 25th Conference on User Modeling, Adaptation and Personalization10.1145/3079628.3079694(294-297)Online publication date: 9-Jul-2017
https://dl.acm.org/doi/10.1145/3079628.3079694
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents