Matchmaking arises when supply and demand meet in an electronic marketplace, or when agents search for a web service to perform some task, or even when recruiting agencies match curricula and job profiles. In such open environments, the objective of a matchmaking process is to discover best available offers to a given request.We address the problem of matchmaking from a knowledge representation perspective, with a formalization based on Description Logics. We devise Concept Abduction and Concept Contraction as non-monotonic inferences in Description Logics suitable for modeling matchmaking in a logical framework, and prove some related complexity results. We also present reasonable algorithms for semantic matchmaking based on the devised inferences, and prove that they obey to some commonsense properties.Finally, we report on the implementation of the proposed matchmaking framework, which has been used both as a mediator in e-marketplaces and for semantic web services discovery.

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In this paper, we present a Description Logic approach-fully compliant with the Semantic web vision and technologies to extended matchmaking between demands and supplies in a semantic-enabled Electronic Marketplace, which allows the semantic-based treatment of negotiable and strict requirements in the demand/supply descriptions. To this aim, we exploit two novel non-standard Description Logic inference services, Concept Contraction-which extends sat-isfiability-and Concept Abduction-which extends subsumption. Based on these services, we devise algorithms, which allow to find negotiation spaces and to determine the quality of a possible match, also in the presence of a distinction between strictly required and optional elements. Both the algorithms and the semantic-based approach are novel, and enable a mechanism to boost logic-based discovery and negotiation stages within an e-marketplace. A set of simple experiments confirm the validity of the approach.

The Web has moved, slowly but steady, from a collection of documents towards a collection of structured data. Knowledge graphs have then emerged as a way of representing the knowledge encoded in such data as well as a tool to reason on them in order to extract new and implicit information. Knowledge graphs are currently used, e.g., to explain search results, to explore knowledge spaces, to semantically enrich textual documents or to feed knowledge intensive applications such as recommender systems. In this work we describe how to create and exploit a knowledge graph to supply a hybrid recommendation engine with information that builds on top of a collections of documents describing musical and sound items. Tags and textual descriptions are exploited to extract and link entities to external graphs such as WordNet and DBpedia which are in turn used to semantically enrich the initial data. By means of the knowledge graph we build, recommendations are computed using a feature combination hybrid approach. Two explicit graph feature mappings are formulated to obtain meaningful item feature representations able to catch the knowledge embedded in the graph. Those content features are further combined with additional collaborative information deriving from implicit user feedback. An extensive evaluation on historical data is performed over two different datasets. A dataset of sounds composed by tags, textual descriptions and user's download information gathered from Freesound.org, and a dataset of songs that mixes song textual descriptions with tags and user's listening habits extracted from Songfacts.com and Last.fm respectively. Results show significant improvements with respect to state of the art collaborative algorithms in both datasets. In addition, we show how the semantic expansion of the initial descriptions helps in achieving much better recommendation quality in terms of aggregated diversity and novelty.

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Abstract. In this paper we present SPrank, a novel hybrid recommendation algorithm able to compute top-N item recommendations from implicit feedback exploiting the information available in the so called Web of Data. We leverage DBpedia, a well-known knowledge base in the LOD (Linked Open Data) compass, to extract semantic path-based features and to eventually compute recommendations using a learning to rank algorithm. Experiments with datasets on two different domains show that the proposed approach outperforms in terms of prediction accuracy several stateof-the-art top-N recommendation algorithms for implicit feedback in situations affected by different degrees of data sparsity.

We present a novel resource discovery framework for mcommerce. We extend the original Bluetooth Service Discovery Protocol by integrating a semantic layer within the application level of the standard. Given a request, this layer makes possible an enhanced discovery process exploiting the semantics of the resources descriptions exposed by a hotspot. The enhancement is compatible with the basic discovery protocol, thus allowing the smooth coexistence of the service discovery approaches. We present and motivate our semantic-based discovery protocol in an innovative m-commerce framework, and show its benefits.