For expertise retrieval purposes we can rely on the endorsements received by members of a social network, with respect to some skill.Skills are correlated, which may affect individual rankings.An endorsement deduction method is proposed, which improves data consistency and completeness.Endorsement deduction makes use of the known correlation among skills.The method is validated in a synthetic network resembling LinkedIn. Some social networks, such as LinkedIn and ResearchGate, allow user endorsements for specific skills. In this way, for each skill we get a directed graph where the nodes correspond to users' profiles and the arcs represent endorsement relations. From the number and quality of the endorsements received, an authority score can be assigned to each profile. In this paper we propose an authority score computation method that takes into account the relations existing among different skills. Our method is based on enriching the information contained in the digraph of endorsements corresponding to a specific skill, and then applying a ranking method admitting weighted digraphs, such as PageRank. We describe the method, and test it on a synthetic network of 1493 nodes, fitted with endorsements. Display Omitted
To the best of our knowledge, there are no precedents for the use of endorsements in social networks, nor for the use of known relationships among different skills, in the context of expertise retrieval. The closest approach might be perhaps the one in @cite_20 , which uses the ACM classification system as an ontology that guides the mining process and expert profiling. Another (very recent) model that uses semantic relationships to increase the effectiveness and efficiency of the search is given in @cite_10 .
@cite_10 @cite_20
2078652018 1486353998
On the Semantic Web, the types of resources and the semantic relationships between resources are defined in an ontology. By using that information, the accuracy of information retrieval can be improved. In this paper, we present effective ranking and search techniques considering the semantic relationships in an ontology. Our technique retrieves top-k resources which are the most relevant to query keywords through the semantic relationships. To do this, we propose a weighting measure for the semantic relationship. Based on this measure, we propose a novel ranking method which considers the number of meaningful semantic relationships between a resource and keywords as well as the coverage and discriminating power of keywords. In order to improve the efficiency of the search, we prune the unnecessary search space using the length and weight thresholds of the semantic relationship path. In addition, we exploit Threshold Algorithm based on an extended inverted index to answer top-k results efficiently. The experimental results using real data sets demonstrate that our retrieval method using the semantic information generates accurate results efficiently compared to the traditional methods. This paper proposes an approach to discover the expertise of researchers using data mining with skill classification ontology. The skill classification ontology is an information model containing skills of doing research in the area of computer and information science. A methodology to build the ontology is presented. The expertise search system is developed, which uses the skill classification ontology, researcher profiles and research profiles in the retrieving process. These profiles and ontology are expressed by OWL. Also, the matching and ranking processes are proposed and these follow semantic-based matching. We explored the evaluation of the retrieving process and the result shows that the proposed approach enables the expertise search system to be efficient regarding accuracy.