Eva Zangerle
Martin Pichl
ABSTRACT
The Wikidata platform is a crowdsourced, structured knowledgebase aiming to provide integrated, free and language-agnostic facts which are---amongst others---used by Wikipedias. Users who actively enter, review and revise data on Wikidata are assisted by a property suggesting system which provides users with properties that might also be applicable to a given item. We argue that evaluating and subsequently improving this recommendation mechanism and hence, assisting users, can directly contribute to an even more integrated, consistent and extensive knowledge base serving a huge variety of applications. However, the quality and usefulness of such recommendations has not been evaluated yet. In this work, we provide the first evaluation of different approaches aiming to provide users with property recommendations in the process of curating information on Wikidata. We compare the approach currently facilitated on Wikidata with two state-of-the-art recommendation approaches stemming from the field of RDF recommender systems and collaborative information systems. Further, we also evaluate hybrid recommender systems combining these approaches. Our evaluations show that the current recommendation algorithm works well in regards to recall and precision, reaching a recall@7 of 79.71% and a precision@7 of 27.97%. We also find that generally, incorporating contextual as well as classifying information into the computation of property recommendations can further improve its performance significantly.
- Z. Abedjan and F. Naumann. Improving rdf data through association rule mining. Datenbank-Spektrum, 13(2):111--120, 2013.Google Scholar
Cross Ref
- Z. Abedjan and F. Naumann. The Semantic Web: ESWC 2014 Satellite Events: ESWC 2014 Satellite Events, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, chapter Amending RDF Entities with New Facts, pages 131--143. Springer International Publishing, Cham, 2014.Google Scholar
- R. Agrawal, T. Imieliński, and A. Swami. Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22(2):207--216, 1993. Google ScholarDigital Library
- S. Burgstaller-Muehlbacher, A. Waagmeester, E. Mitraka, J. Turner, T. E. Putman, J. Leong, P. Pavlidis, L. Schriml, B. M. Good, and A. I. Su. Wikidata as a semantic framework for the gene wiki initiative. bioRxiv, 2015.Google Scholar
- P. Cremonesi, R. Turrin, E. Lentini, and M. Matteucci. An evaluation methodology for collaborative recommender systems. In Automated solutions for Cross Media Content and Multi-channel Distribution, 2008. AXMEDIS'08. International Conference on, pages 224--231. IEEE, 2008. Google ScholarDigital Library
- F. Erxleben, M. Günther, M. Krötzsch, J. Mendez, and D. Vrandečić. The Semantic Web -- ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I, chapter Introducing Wikidata to the Linked Data Web, pages 50--65. Springer International Publishing, Cham, 2014. Google ScholarDigital Library
- W. Gassler, E. Zangerle, and G. Specht. Guided Curation of Semistructured Data in Collaboratively-built Knowledge Bases. Journal on Future Generation Computer Systems, 31:111--119, 2014. impact factor 1.978. Google ScholarDigital Library
- J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In ACM Sigmod Record, volume 29, pages 1--12. ACM, 2000. Google ScholarDigital Library
- D. Hernández, A. Hogan, and M. Krötzsch. Reifying rdf: What works well with wikidata? In Proceedings of the 11th International Workshop on Scalable Semantic Web Knowledge Base Systems co-located with 14th International Semantic Web Conference (ISWC 2015), Bethlehem, PA, USA, pages 32--47, 2015.Google Scholar
- H. B. Mann and D. R. Whitney. On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics, pages 50--60, 1947.Google Scholar
- C. D. Manning, P. Raghavan, H. Schütze, et al. Introduction to information retrieval, volume 1. Cambridge university press Cambridge, 2008. Google ScholarDigital Library
- C. Müller-Birn, B. Karran, J. Lehmann, and M. Luczak-Rösch. Peer-production system or collaborative ontology engineering effort: What is wikidata? In Proceedings of the 11th International Symposium on Open Collaboration, OpenSym '15, pages 20:1--20:10, New York, NY, USA, 2015. ACM. Google ScholarDigital Library
- A. Pfundner, T. Schönberg, J. Horn, R. D. Boyce, and M. Samwald. Utilizing the wikidata system to improve the quality of medical content in wikipedia in diverse languages: A pilot study. Journal of medical Internet research, 17(5), 2015.Google Scholar
- D. Sanchez, M. Vila, L. Cerda, and J. Serrano. Association rules applied to credit card fraud detection. Expert Systems with Applications, 36(2, Part 2):3630--3640, 2009. Google ScholarDigital Library
- A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock. Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pages 253--260. ACM, 2002. Google ScholarDigital Library
- C. Schmitz, A. Hotho, R. Jäschke, and G. Stumme. Data Science and Classification, chapter Mining Association Rules in Folksonomies, pages 261--270. Springer Berlin Heidelberg, Berlin, Heidelberg, 2006.Google Scholar
- B. Sigurbjörnsson and R. Van Zwol. Flickr tag recommendation based on collective knowledge. In Proceedings of the 17th international conference on World Wide Web, pages 327--336. ACM, 2008. Google ScholarDigital Library
- T. Steiner. Bots vs. wikipedians, anons vs. logged-ins (redux): A global study of edit activity on wikipedia and wikidata. In Proceedings of The International Symposium on Open Collaboration, OpenSym '14, pages 25:1--25:7, New York, NY, USA, 2014. ACM. Google ScholarDigital Library
- T. H. Ta and C. Anutariya. Semantic Technology: 4th Joint International Conference, JIST 2014, Chiang Mai, Thailand, November 9-11, 2014. Revised Selected Papers, chapter A Model for Enriching Multilingual Wikipedias Using Infobox and Wikidata Property Alignment, pages 335--350. Springer International Publishing, Cham, 2015.Google Scholar
- J. J. Treinen and R. Thurimella. A framework for the application of association rule mining in large intrusion detection infrastructures. In Recent Advances in Intrusion Detection, pages 1--18. Springer, 2006. Google ScholarDigital Library
- D. Vrandečić and M. Krötzsch. Wikidata: a free collaborative knowledgebase. Communications of the ACM, 57(10):78--85, 2014. Google ScholarDigital Library
- D. Vrandečić. Wikidata: A new platform for collaborative data collection. In Proceedings of the 21st International Conference on World Wide Web, WWW '12 Companion, pages 1063--1064, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- D. Vrandečić and M. Krötzsch. Wikidata: A free collaborative knowledgebase. Commun. ACM, 57(10):78--85, Sept. 2014. Google ScholarDigital Library
- O. R. Zaiane. Building a recommender agent for e-learning systems. In Computers in Education, 2002. Proceedings. International Conference on, pages 55--59 vol.1, Dec 2002. Google ScholarDigital Library
- E. Zangerle, W. Gassler, and G. Specht. Recommending Structure in Collaborative Semistructured Information Systems. In RecSys '10: Proceedings of the third ACM conference on Recommender systems, pages 141--145, Barcelona, Spain, 2010. ACM. Google ScholarDigital Library
Recommendations
Temporal diversity in recommender systems
SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrievalCollaborative Filtering (CF) algorithms, used to build web-based recommender systems, are often evaluated in terms of how accurately they predict user ratings. However, current evaluation techniques disregard the fact that users continue to rate items ...
A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems
UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and PersonalizationMuch of the focus of recommender systems research has been on the accurate prediction of users' ratings for unseen items. Recent work has suggested that objectives such as diversity and novelty in recommendations are also important factors in the ...
On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected
Special Sections on Diversity and Discovery in Recommender Systems, Online Advertising and Regular PapersAlthough the broad social and business success of recommender systems has been achieved across several domains, there is still a long way to go in terms of user satisfaction. One of the key dimensions for significant improvement is the concept of ...
Comments