More Web Proxy on the site http://driver.im/

research-article

How Users Explore Ontologies on the Web: A Study of NCBO's BioPortal Usage Logs

Authors:

Lisette Esín-Noboa,

Markus Strohmaier,

Mark A. MusenAuthors Info & Claims

WWW '17: Proceedings of the 26th International Conference on World Wide Web

Pages 775 - 784

https://doi.org/10.1145/3038912.3052606

Published: 03 April 2017 Publication History

Abstract

Ontologies in the biomedical domain are numerous, highly specialized and very expensive to develop. Thus, a crucial prerequisite for ontology adoption and reuse is effective support for exploring and finding existing ontologies. Towards that goal, the National Center for Biomedical Ontology (NCBO) has developed BioPortal---an online repository containing more than 500 biomedical ontologies. In 2016, BioPortal represents one of the largest portals for exploration of semantic biomedical vocabularies and terminologies, which is used by many researchers and practitioners. While usage of this portal is high, we know very little about how exactly users search and explore ontologies and what kind of usage patterns or user groups exist in the first place. Deeper insights into user behavior on such portals can provide valuable information to devise strategies for a better support of users in exploring and finding existing ontologies, and thereby enable better ontology reuse. To that end, we study and group users according to their browsing behavior on BioPortal and use data mining techniques to characterize and compare exploration strategies across ontologies. In particular, we were able to identify seven distinct browsing types, all relying on different functionality provided by BioPortal. For example, Search Explorers extensively use the search functionality while Ontology Tree Explorers mainly rely on the class hierarchy for exploring ontologies. Further, we show that specific characteristics of ontologies influence the way users explore and interact with the website. Our results may guide the development of more user-oriented systems for ontology exploration on the Web.

References

[1]

O. Bodenreider. The unified medical language system (umls): integrating biomedical terminology. Nucleic acids research, 32(suppl 1):D267--D270, 2004.

[2]

J. Borges and M. Levene. Evaluating variable-length markov chain models for analysis of user web navigation sessions. IEEE Trans. on Knowl. and Data Eng., 19(4):441--452, Apr. 2007.

Digital Library

[3]

F. Chierichetti, R. Kumar, P. Raghavan, and T. Sarlos. Are web users really markovian? In Proceedings of the 21st international conference on World Wide Web, pages 609--618. ACM, 2012.

Digital Library

[4]

M. Deshpande and G. Karypis. Selective markov models for predicting web page accesses. ACM Trans. Internet Technol., 4(2):163--184, May 2004.

Digital Library

[5]

L. Espín Noboa, F. Lemmerich, P. Singer, and M. Strohmaier. Discovering and characterizing mobility patterns in urban spaces: A study of manhattan taxi data. In Proceedings of the 25th International Conference Companion on World Wide Web, pages 537--542, 2016.

Digital Library

[6]

S. M. Falconer, T. Tudorache, and N. F. Noy. An analysis of collaborative patterns in large-scale ontology development projects. In M. A. Musen and O. Corcho, editors, K-CAP, pages 25--32. ACM, 2011.

Digital Library

[7]

B. Fortuna, D. Mladenic, and M. Grobelnik. User modeling combining access logs, page content and semantics. arXiv preprint arXiv:1103.5002, 2011.

[8]

A. Gomez, M. Fernandez, and O. Corcho. Ontological engineering. 2 nd Edition, Springer-Verlag, 2004.

[9]

Z. Hellman and A. Gal. Structural Conflict Avoidance in Collaborative Ontology Engineering, pages 216--225. Springer Netherlands, Dordrecht, 2005.

[10]

J. Hoxha, M. Junghans, and S. Agarwal. Enabling semantic analysis of user browsing patterns in the web of data. arXiv preprint arXiv:1204.2713, 2012.

[11]

S. Jupp, T. Burdett, J. Malone, C. Leroy, M. Pearce, J. McMurry, and H. Parkinson. A new ontology lookup service at embl-ebi.

[12]

R. Kumar and A. Tomkins. A characterization of online browsing behavior. In Proceedings of the 19th international conference on World wide web, pages 561--570. ACM, 2010.

Digital Library

[13]

C. Lakshminarayan, R. Kosuru, and M. Hsu. Modeling complex clickstream data by stochastic models: Theory and methods. In Proceedings of the 25th International Conference Companion on World Wide Web, pages 879--884, 2016.

Digital Library

[14]

J. Lehmann, C. Müller-Birn, D. Laniado, M. Lalmas, and A. Kaltenbrunner. Reader preferences and behavior on wikipedia. In Proceedings of the 25th ACM conference on Hypertext and social media, pages 88--97. ACM, 2014.

Digital Library

[15]

R. Lempel and S. Moran. The stochastic approach for link-structure analysis (salsa) and the tkc effect. Comput. Netw., 33(1--6):387--401, June 2000.

Digital Library

[16]

M. A. Musen, N. F. Noy, N. H. Shah, P. L. Whetzel, C. G. Chute, M.-A. Story, B. Smith, et al. The national center for biomedical ontology. Journal of the American Medical Informatics Association, 19(2):190--195, 2012.

[17]

N. F. Noy, D. L. McGuinness, et al. Ontology development 101: A guide to creating your first ontology, 2001.

[18]

N. F. Noy, N. H. Shah, P. L. Whetzel, B. Dai, M. Dorf, N. Griffith, C. Jonquet, D. L. Rubin, M.-A. Storey, C. G. Chute, et al. Bioportal: ontologies and integrated data resources at the click of a mouse. Nucleic acids research, page gkp440, 2009.

[19]

C. Pesquita and F. M. Couto. Predicting the extension of biomedical ontologies. PLoS Comput Biol, 8(9):e1002630, 09 2012.

[20]

P. L. T. Pirolli and J. E. Pitkow. Distributions of surfers' paths through the world wide web: Empirical characterizations. World Wide Web, 2(1--2):29--45, 1999.

Digital Library

[21]

M. Salvadores, P. R. Alexander, M. A. Musen, and N. F. Noy. Bioportal as a dataset of linked biomedical ontologies and terminologies in rdf. Semantic web, 4(3):277--284, 2013.

[22]

R. Sen and M. Hansen. Predicting a web user's next access based on log data. Journal of Computational Graphics and Statistics, 12(1):143--155, 2003.

[23]

E. Simperl and M. Luczak-Rösch. Collaborative ontology engineering: a survey. The Knowledge Engineering Review, 29(01):101--131, 2014.

[24]

P. Singer, D. Helic, A. Hotho, and M. Strohmaier. Hyptrails: A bayesian approach for comparing hypotheses about human trails on the web. In Proceedings of the 24th International Conference on World Wide Web, WWW '15, pages 1003--1013, 2015.

Digital Library

[25]

B. Smith, M. Ashburner, C. Rosse, J. Bard, W. Bug, W. Ceusters, L. J. Goldberg, K. Eilbeck, A. Ireland, C. J. Mungall, et al. The obo foundry: coordinated evolution of ontologies to support biomedical data integration. Nature biotechnology, 25(11):1251--1255, 2007.

[26]

M. Strohmaier, S. Walk, J. Pöschko, D. Lamprecht, T. Tudorache, C. Nyulas, M. A. Musen, and N. F. Noy. How ontologies are made: Studying the hidden social dynamics behind collaborative ontology engineering projects. Web Semantics: Science, Services and Agents on the World Wide Web, 20(0), 2013.

[27]

S. Van Laere, R. Buyl, and M. Nyssen. A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology Engineering. In On the Move to Meaningful Internet Systems: OTM 2014 Conferences, pages 657--673. Springer, 2014.

[28]

S. Walk, P. Singer, L. E. Noboa, T. Tudorache, M. A. Musen, and M. Strohmaier. Understanding how users edit ontologies: Comparing hypotheses about four real-world projects. In The Semantic Web - ISWC 2015 - 14th International Semantic Web Conference, pages 551--568, 2015.

Digital Library

[29]

S. Walk, P. Singer, and M. Strohmaier. Sequential action patterns in collaborative ontology-engineering projects: A case-study in the biomedical domain. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, Shanghai, China, November 3-7, 2014, pages 1349--1358, 2014.

Digital Library

[30]

S. Walk, P. Singer, M. Strohmaier, D. Helic, N. F. Noy, and M. A. Musen. How to apply markov chains for modeling sequential edit patterns in collaborative ontology-engineering projects. International Journal of Human-Computer Studies, 84:51--66, 2015.

Digital Library

[31]

S. Walk, P. Singer, M. Strohmaier, T. Tudorache, M. A. Musen, and N. F. Noy. Discovering beaten paths in collaborative ontology-engineering projects using markov chains. Journal of Biomedical Informatics, 51:254--271, 2014.

Digital Library

[32]

H. Wang, T. Tudorache, D. Dou, N. F. Noy, and M. A. Musen. Analysis of user editing patterns in ontology development projects. In On the Move to Meaningful Internet Systems: OTM 2013 Conferences, pages 470--487. Springer, 2013.

[33]

P. L. Whetzel, N. F. Noy, N. H. Shah, P. R. Alexander, C. Nyulas, T. Tudorache, and M. A. Musen. Bioportal: enhanced functionality via new web services from the national center for biomedical ontology to access and use ontologies in software applications. Nucleic acids research, 39(suppl 2):W541--W545, 2011.

Cited By

Fabre RAzeroual OSchöpfel JBellot PEgret D(2023)A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYPFuture Internet10.3390/fi1504014715:4(147)Online publication date: 10-Apr-2023
https://doi.org/10.3390/fi15040147
Mazimwe AHammouda IGidudu A(2021)Implementation of FAIR Principles for Ontologies in the Disaster Domain: A Systematic Literature ReviewISPRS International Journal of Geo-Information10.3390/ijgi1005032410:5(324)Online publication date: 11-May-2021
https://doi.org/10.3390/ijgi10050324
Ali NGadallah AHefny HNovikov B(2020)An Integrated Framework for Web Data Preprocessing Towards Modeling User Behavior2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)10.1109/FarEastCon50210.2020.9271467(1-8)Online publication date: 6-Oct-2020
https://doi.org/10.1109/FarEastCon50210.2020.9271467
Show More Cited By

Index Terms

How Users Explore Ontologies on the Web: A Study of NCBO's BioPortal Usage Logs
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation theory
      1. Network science
2. Information systems
  1. World Wide Web
    1. Web mining
      1. Traffic analysis
      2. Web log analysis

Recommendations

Multilingual Mapping Reconciliation between English-French Biomedical Ontologies
WIMS '16: Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics

Even if multilingual ontologies are now more common, for historical reasons, in the biomedical domain, many ontologies or terminologies have been translated from one natural language to another resulting in two potentially aligned ontologies but with ...
BioPortal as a dataset of linked biomedical ontologies and terminologies in RDF

BioPortal is a repository of biomedical ontologies --the largest such repository, with more than 300 ontologies to date. This set includes ontologies that were developed in OWL, OBO and other formats, as well as a large number of medical terminologies ...
Semantic enrichment for medical ontologies

The Unified Medical Language System (UMLS) contains two separate but interconnected knowledge structures, the Semantic Network (upper level) and the Metathesaurus (lower level). In this paper, we have attempted to work out better how the use of such a ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '17: Proceedings of the 26th International Conference on World Wide Web

April 2017

1678 pages

ISBN:9781450349130

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

Copyright © 2017 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 03 April 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Institutes of Health
U.S. National Institute of General Medical Sciences

Conference

WWW '17

Sponsor:

IW3C2

WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

Acceptance Rates

WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,068 of 6,946 submissions, 15%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
162
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)1

Reflects downloads up to 12 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Fabre RAzeroual OSchöpfel JBellot PEgret D(2023)A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYPFuture Internet10.3390/fi1504014715:4(147)Online publication date: 10-Apr-2023
https://doi.org/10.3390/fi15040147
Mazimwe AHammouda IGidudu A(2021)Implementation of FAIR Principles for Ontologies in the Disaster Domain: A Systematic Literature ReviewISPRS International Journal of Geo-Information10.3390/ijgi1005032410:5(324)Online publication date: 11-May-2021
https://doi.org/10.3390/ijgi10050324
Ali NGadallah AHefny HNovikov B(2020)An Integrated Framework for Web Data Preprocessing Towards Modeling User Behavior2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)10.1109/FarEastCon50210.2020.9271467(1-8)Online publication date: 6-Oct-2020
https://doi.org/10.1109/FarEastCon50210.2020.9271467
Espín-Noboa LLemmerich FWalk SStrohmaier MMusen M(2019)HopRank: How Semantic Structure Influences Teleportation in PageRank (A Case Study on BioPortal)The World Wide Web Conference10.1145/3308558.3313487(2708-2714)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313487
Ivanova VLambrix PLohmann SPesquita C(2019)Visualization and interaction for ontologies and linked data—EditorialWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2018.10.00155:C(145-149)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1016/j.websem.2018.10.001
Kamdar MWalk STudorache TMusen M(2018)Analyzing user interactions with biomedical ontologiesWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2017.12.00249:C(16-30)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.websem.2017.12.002
Kanza SStolz AHepp MSimperl E(2018)What Does an Ontology Engineering Community Look Like? A Systematic Analysis of the schema.org CommunityThe Semantic Web10.1007/978-3-319-93417-4_22(335-350)Online publication date: 3-Jun-2018
https://doi.org/10.1007/978-3-319-93417-4_22
Kamdar MWalk STudorache TMusen M(2017)BiOnIC: A Catalog of User Interactions with Biomedical OntologiesThe Semantic Web – ISWC 201710.1007/978-3-319-68204-4_13(130-138)Online publication date: 4-Oct-2017
https://doi.org/10.1007/978-3-319-68204-4_13
(2016)Congestion control in disruption-tolerant networksAd Hoc Networks10.1016/j.adhoc.2016.02.00444:C(1-18)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.adhoc.2016.02.004
Tian JLiang XWang G(2016)Deployment and reallocation in mobile survivability-heterogeneous wireless sensor networks for barrier coverageAd Hoc Networks10.1016/j.adhoc.2015.08.00536:P1(321-331)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.adhoc.2015.08.005
Show More Cited By

View Options

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