Abstract
Concept recommendation is a widely used technique aimed to assist users to chose the right tags, improve their Web search experience and a multitude of other tasks. In finding potential problem solvers in Open Innovation (OI) scenarios, the concept recommendation is of a crucial importance as it can help to discover the right topics, directly or laterally related to an innovation problem. Such topics then could be used to identify relevant experts. We propose two Linked Data-based concept recommendation methods for topic discovery. The first one, hyProximity, exploits only the particularities of Linked Data structures, while the other one applies a well-known Information Retrieval method, Random Indexing, to the linked data. We compare the two methods against the baseline in the gold standard-based and user study-based evaluations, using the real problems and solutions from an OI company.
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Chesbrough, H.W.: Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business Press (2003)
Speidel, K.-P.: Problem-Description in Open Problem-Solving. How to overcome Cognitive and Psychological Roadblocks. In: Sloane, P. (ed.) A Guide to Open Innovation and Crowdsourcing. Advice from Leading Experts. KoganPage, London (2011)
Stankovic, M., Jovanovic, J., Laublet, P.: Linked Data Metrics for Flexible Expert Search on the Open Web. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part I. LNCS, vol. 6643, pp. 108–123. Springer, Heidelberg (2011)
Jeppesen, L.B., Lakhani, K.R.: Marginality and Problem Solving Effectiveness in Broadcast Research. Organization Science 20 (2009)
Sigurbjörnsson, B., van Zwol, R.: Flickr tag recommendation based on collective knowledge. In: Proceeding of the 17th International Conference on World Wide Web, WWW 2008, p. 327. ACM Press, New York (2008), doi:10.1145/1367497.1367542
Mei, Q., Zhou, D., Church, K.: Query suggestion using hitting time. In: Proceeding of the 17th ACM Conference on Information and Knowledge Mining - CIKM 2008, New York, USA, p. 469 (2008)
Safar, B., Kefi, H.: OntoRefiner, a user query refinement interface usable for Semantic Web Portals. In: Proceedings of Application of Semantic Web Technologies to Web Communities Workshop, ECAI 2004, pp. 65–79 (2004)
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, p. 341. ACM Press, New York (2007)
Cross, V.: Semantic Relatedness Measures in Ontologies Using Information Content and Fuzzy Set Theory. In: Proc. of the 14th IEEE Int’l Conf. on Fuzzy Systems, pp. 114–119 (2005)
Gasevic, D., Zouaq, A., Torniai, C., Jovanovic, J., Hatala, M.: An Approach to Folksonomy-based Ontology Maintenance for Learning Environments. IEEE Transactions on Learning Technologies (2011) (in press)
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)
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 (2010)
Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)
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
Gracia, J., Mena, E.: Web-Based Measure of Semantic Relatedness. In: Bailey, J., Maier, D., Schewe, K.-D., Thalheim, B., Wang, X.S. (eds.) WISE 2008. LNCS, vol. 5175, pp. 136–150. Springer, Heidelberg (2008)
Sahlgren, M.: An introduction to random indexing. In: Methods and Applications of Semantic Indexing Workshop at the 7th International Conference on Terminology and Knowledge Engineering, TKE 2005 (2005)
Cohen, T., Schvaneveldt, R., Widdows, D.: Reflective random indexing and indirect inference: A scalable method for discovery of implicit connections. Journal of Biomedical Informatics (2009)
Passant, A.: dbrec — Music Recommendations Using DBpedia. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part II. LNCS, vol. 6497, pp. 209–224. Springer, Heidelberg (2010)
Waitelonis, J., Sack, H.: Towards Exploratory Video Search Using Linked Data. In: 2009 11th IEEE International Symposium on Multimedia, pp. 540–545. IEEE (2009), doi:10.1109/ISM.2009.111
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)
Deerwester, S., Dumais, S., Furnas, G., Landauer, T., Harshman, R.: Indexing by latent semantic analysis. Journal of the American Society for Information Science 41, 391–407 (1990)
Karlgren, J., Sahlgren, M.: From words to understanding. In: Uesaka, Y., Kanerva, P., Asoh, H. (eds.) Foundations of Real-World Intelligence, pp. 294–308. CSLI Publications, Stanford (2001)
Cohen, T.: Exploring medline space with random indexing and Pathfinder networks. In: Annual Symposium Proceedings/AMIA Symposium, pp. 126–130 (2008)
Rizzo, G., Troncy, R.: NERD: Evaluating Named Entity Recognition Tools in the Web of Data. In: ISWC 2011 Workshop on Web Scale Knowledge Extraction (WEKEX), Bonn, Germany (2011)
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Damljanovic, D., Stankovic, M., Laublet, P. (2012). Linked Data-Based Concept Recommendation: Comparison of Different Methods in Open Innovation Scenario. 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_9
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DOI: https://doi.org/10.1007/978-3-642-30284-8_9
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