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We present WISER, a new semantic search engine for expert finding inacademia. Our system is unsupervised and it jointly combines classical languagemodeling techniques, based on text evidences, with the Wikipedia KnowledgeGraph, via entity linking.  WISER indexes each academic author through a novel profiling technique whichmodels her expertise with a small, labeled and weighted graph drawn fromWikipedia. Nodes in this graph are the Wikipedia entities mentioned in theauthor's publications, whereas the weighted edges express the semanticrelatedness among these entities computed via textual and graph-basedrelatedness functions. Every node is also labeled with a relevance score whichmodels the pertinence of the corresponding entity to author's expertise, and iscomputed by means of a proper random-walk calculation over that graph; and witha latent vector representation which is learned via entity and other kinds ofstructural embeddings derived from Wikipedia.  At query time, experts are retrieved by combining classic document-centricapproaches, which exploit the occurrences of query terms in the author'sdocuments, with a novel set of profile-centric scoring strategies, whichcompute the semantic relatedness between the author's expertise and the querytopic via the above graph-based profiles.  The effectiveness of our system is established over a large-scaleexperimental test on a standard dataset for this task. We show that WISERachieves better performance than all the other competitors, thus proving theeffectiveness of modelling author's profile via our "semantic" graph ofentities. Finally, we comment on the use of WISER for indexing and profilingthe whole research community within the University of Pisa, and its applicationto technology transfer in our University.

WISER: A Semantic Approach for Expert Finding in Academia based on Entity Linking