Abstract
In this paper we propose a method for suggesting potential collaborators for solving innovation challenges online, based on their competence, similarity of interests and social proximity with the user. We rely on Linked Data to derive a measure of semantic relatedness that we use to enrich both user profiles and innovation problems with additional relevant topics, thereby improving the performance of co-solver recommendation. We evaluate this approach against state of the art methods for query enrichment based on the distribution of topics in user profiles, and demonstrate its usefulness in recommending collaborators that are both complementary in competence and compatible with the user. Our experiments are grounded using data from the social networking service Twitter.com.
Keywords
- Linked Data
- Collaborator Recommendation
Download conference paper PDF
References
Jones, B.F., Wuchty, S., Uzzi, B.: Multi-university research teams: shifting impact, geography, and stratification in science. Science 322(5905), 1259–1262 (2008)
Hinds, P.J., Carley, K.M., Krackhardt, D., Wholey, D.: Choosing Work Group Members: Balancing Similarity, Competence, and Familiarity∙ 1. Organizational Behavior and Human Decision Processes 81(2), 226–251 (2000)
Chesbrough, H.W.: Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business Press (2003)
Jeppesen, L.B., Lakhani, K.R.: Marginality and Problem Solving Effectiveness in Broadcast Research. Organization Science 20 (2009)
Spertus, E., Sahami, M., Buyukkokten, O.: Evaluating similarity measures. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, New York, USA (2005)
Luo, H., Niu, C., Shen, R., Ullrich, C.: A collaborative filtering framework based on both local user similarity and global user similarity. Machine Learning 72(3), 231–245 (2008)
Guy, I., Jacovi, M., Perer, A., Ronen, I., Uziel, E.: Same places, same things, same people?: mining user similarity on social media. In: Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work, pp. 41–50. ACM (2010)
Chen, J., Geyer, W., Dugan, C., Muller, M., Guy, I.: Make new friends, but keep the old: recommending people on social networking sites. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems. ACM (2009)
Lakiotaki, K., Matsatsinis, N., Tsoukias, A.: Multicriteria User Modeling in Recommender Systems. IEEE Intelligent Systems 26(2), 64–76 (2011)
Kazienko, P., Musial, K., Kajdanowicz, T.: Multidimensional Social Network in the Social Recommender System. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 41(4), 746–759 (2011)
Siersdorfer, S., Sizov, S.: Social recommender systems for web 2.0 folksonomies. In: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, HT 2009, p. 261. ACM Press, New York (2009)
Text Retrieval Conference Proceedings (1992-2010)
Zoltan, K., Johann, S.: Semantic analysis of microposts for efficient people to people interactions. In: 10th Roedunet International Conference, RoEduNet 2011, pp. 1–4, 23–25 (2011)
Ziaimatin, H.: DC Proposal: Capturing Knowledge Evolution and Expertise in Community-Driven Knowledge Curation Platforms. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part II. LNCS, vol. 7032, pp. 381–388. Springer, Heidelberg (2011)
Abel, F., Gao, Q., Houben, G.J., Tao, K.: Analyzing Temporal Dynamics in Twitter Profiles for Personalized Recommendations in the Social Web. In: Web Science Conference, Koblenz (2011)
Stan, J., Do, V.-H., Maret, P.: Semantic User Interaction Profiles for Better People Recommendation. In: Advances in Social Networks Analysis and Mining, ASONAM 2001 (2011); Rowe, M., Angeletou, S., Alani, H.: Predicting Discussions on the Social Semantic Web. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 201. LNCS, vol. 6644, pp. 405–420. Springer, Heidelberg (2011)
Wagner, C.: Exploring the Wisdom of the Tweets: Towards Knowledge Acquisition from Social Awareness Streams. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part II. LNCS, vol. 6089, pp. 493–497. Springer, Heidelberg (2010)
Burton-Jones, A., Storey, V.C., Sugumaran, V., Purao, S.: A Heuristic-Based Methodology for Semantic Augmentation of User Queries on the Web. In: Song, I.-Y., Liddle, S.W., Ling, T.-W., Scheuermann, P. (eds.) ER 2003. LNCS, vol. 2813, pp. 476–489. Springer, Heidelberg (2003)
Ziegler, C.-N., Simon, K., Lausen, G.: Automatic Computation of Semantic Proximity Using Taxonomic Knowledge Categories and Subject Descriptors. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, CIKM 2006, Arlington, Virginia, USA, pp. 465–474. ACM, New York (2006)
Strube, M., Ponzetto, S.P.: WikiRelate! Computing semantic relatedness using Wikipedia. In: Proceedings of the National Conference on Artificial Intelligence, vol. 21, p. 1419. AAAI Press, MIT Press, Menlo Park, Cambridge (1996, 2006)
Resnik, P.: Using Information Content to Evaluate Semantic Similarity in a Taxonomy (1995)
Matos, S., Arrais, J.P., Maia-Rodrigues, J., Oliveira, J.L.: Concept-based query expansion for retrieving gene related publications from Medline. BMC Bioinformatics 11, 212 (2010)
Cilibrasi, R.L., Vitanyi, P.M.B.: The Google Similarity Distance. IEEE Transactions on Knowledge and Data Engineering 19(3), 370–383 (2007), doi:10.1109/TKDE.2007.48
Macdonald, C., Ounis, I.: Expertise drift and query expansion in expert search. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management - CIKM 2007, vol. 341. ACM Press, New York (2007)
Serdyukov, P., Chernov, S., Nejdl, W.: Enhancing Expert Search Through Query Modeling. In: Amati, G., Carpineto, C., Romano, G. (eds.) ECIR 2007. LNCS, vol. 4425, pp. 737–740. Springer, Heidelberg (2007)
Rizzo, G., Troncy, R.: NERD: Evaluating Named Entity Recognition Tools in the Web of Data. In: Proceedings of the 11th Interational Semantic Web Conference 2011, Bonn, Germany (2011)
Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., Ma, W.-Y.: Mining user similarity based on location history. In: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS 2008, p. 1. ACM Press, New York (2008)
Balog, K., de Rijke, M.: Finding similar experts. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2007, p. 821. ACM Press, New York (2007), doi:10.1145/1277741.1277926
Viswanathan, K.K., Finin, T.: Text Based Similarity Metrics and Deltas for Semantic Web Graphs. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., et al. (eds.) Proceedings of the 9th International Semantic Web Conferene - ISWC 2010, Shanghai, China (2010)
Stankovic, M., Breitfuss, W., Laublet, P.: Linked-Data Based Suggestion of Relevant Topics. In: Proceedings of I-SEMANTICS Conference 2011, Gratz, Austria, September 7-9 (2011)
Letierce, J., Passant, A., Decker, S., Breslin, J.: Understanding how Twitter is used to spread scientific messages. In: Proceedings of the WebSci 2010, Raleigh, NC, US (2010)
Fleiss, J.: Statistical Methods for Rates and Proportions. Wiley-Interscience (1981)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Stankovic, M., Rowe, M., Laublet, P. (2012). Finding Co-solvers on Twitter, with a Little Help from Linked Data. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds) The Semantic Web: Research and Applications. ESWC 2012. Lecture Notes in Computer Science, vol 7295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30284-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-30284-8_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30283-1
Online ISBN: 978-3-642-30284-8
eBook Packages: Computer ScienceComputer Science (R0)
