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

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

Wiser: Wikipedia Expertise Rank

We present Wiser, a new search engine for expert finding in academia. Our system is unsupervised and it jointly combines multiple classical language modeling techniques, based on text evidences, with Wikipedia knowledge, via entity linking. The expertise of each indexed expert is modeled by Wiser through a graph-based representation of Wikipedia entities and their relationships. Each expert-graph is further refined via proper computations (e.g. clustering and random walks) and eventually enhanced with the latent representation of entities learned with word embeddings. The effectiveness of our system is established over a large-scale experimental test over standard datasets which shows better performance than other state-of-the-art competitors published in top conferences, such as WWW 2016

DrACO: Discovering available cloud offerings

Current cloud technologies suffer from a lack of standardisation, with different providers offering similar resources in a different manner. The aim of this work is to contribute overcoming such heterogeneity, by showing how the OASIS TOSCA standard can be exploited to provide a standard-based representation of the virtual machines and platforms offered by IaaS and PaaS cloud providers. We also present DrACO, an open-source prototype tool that permits to look-up for cloud offerings and to retrieve them in a standardised TOSCA format