Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Automatic computation of semantic proximity using taxonomic knowledge
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Expertise drift and query expansion in expert search
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Web-Based Measure of Semantic Relatedness
WISE '08 Proceedings of the 9th international conference on Web Information Systems Engineering
Query suggestion using hitting time
Proceedings of the 17th ACM conference on Information and knowledge management
Towards Exploratory Video Search Using Linked Data
ISM '09 Proceedings of the 2009 11th IEEE International Symposium on Multimedia
Journal of Biomedical Informatics
Marginality and Problem-Solving Effectiveness in Broadcast Search
Organization Science
dbrec: music recommendations using DBpedia
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
Linked data metrics for flexible expert search on the open web
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
Linked-data based suggestion of relevant topics
Proceedings of the 7th International Conference on Semantic Systems
An Approach to Folksonomy-Based Ontology Maintenance for Learning Environments
IEEE Transactions on Learning Technologies
Identifying candidate datasets for data interlinking
ICWE'13 Proceedings of the 13th international conference on Web Engineering
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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.