Searching Linked Data with a Twist of Serendipity

Serendipity is defined as the discovery of a thing when one is not searching for it. In other words, serendipity means the discovery of information that provides valuable insights by unveiling previously unknown knowledge. This paper focuses on the problem of Linked Data serendipitous search. It first discusses how to capture a set of serendipity patterns in the context of Linked Data. Then, the paper introduces a Linked Data serendipitous search application, called the Serendipity Over Linked Data Search tool – SOL-Tool. Finally, the paper describes experiments with the tool to illustrate the serendipity effect using DBpedia. The experimental results present a promissory score of 90% of unexpectedness for real-world scenarios in the music domain.

[1]  Pasquale Lops,et al.  Introducing Serendipity in a Content-Based Recommender System , 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

[2]  Milan Stankovic,et al.  Linked-data based suggestion of relevant topics , 2011, I-Semantics '11.

[3]  P. Andel.  Anatomy of the Unsought Finding. Serendipity: Orgin, History, Domains, Traditions, Appearances, Patterns and Programmability , 1994, The British Journal for the Philosophy of Science.

[4]  Anand Rajaraman,et al.  Mining of Massive Datasets , 2011 .

[5]  M. Kaminskas.  Measuring Surprise in Recommender Systems , 2014 .

[6]  Sören Auer,et al.  LIMES - A Time-Efficient Approach for Large-Scale Link Discovery on the Web of Data , 2011, IJCAI.

[7]  Robert Isele,et al.  Silk Server - Adding missing Links while consuming Linked Data , 2010, COLD.

[8]  Alexandre Passant,et al.  dbrec - Music Recommendations Using DBpedia , 2010, SEMWEB.

[9]  Max L. Wilson,et al.  Exploring Opportunities to Facilitate Serendipity in Search , 2015, SIGIR.

[10]  Daniele Quercia,et al.  Auralist: introducing serendipity into music recommendation , 2012, WSDM '12.

[11]  Sergei Vassilvitskii,et al.  Getting recommender systems to think outside the box , 2009, RecSys '09.

[12]  Din J. Wasem,et al.  Mining of Massive Datasets , 2014 .

[13]  Susan T. Dumais,et al.  Discovery is never by chance: designing for (un)serendipity , 2009, C&C '09.

[14]  Sean M. McNee,et al.  Improving recommendation lists through topic diversification , 2005, WWW '05.

[15]  Ryohei Orihara,et al.  Metrics for Evaluating the Serendipity of Recommendation Lists , 2007, JSAI.

[16]  Francesco Bonchi,et al.  From machu_picchu to "rafting the urubamba river": anticipating information needs via the entity-query graph , 2013, WSDM '13.

[17]  Jure Leskovec,et al.  Mining of Massive Datasets, 2nd Ed , 2014 .

[18]  Panagiotis Adamopoulos,et al.  On Unexpectedness in Recommender Systems: Or How to Expect the Unexpected , 2011, DiveRS@RecSys.

[19]  Oscar de Bruijn,et al.  A New Framework for Theory-Based Interaction Design Applied to Serendipitous Information Retrieval , 2008, TCHI.