Eugenio Di Sciascio

A system for principled matchmaking in an electronic marketplace

More and more resources are becoming available on the Web, and there is a growing need for infrastructures that, based on advertised descriptions, are able to semantically match demands with supplies.We formalize general properties a matchmaker should have, then we present a matchmaking facilitator, compliant with desired properties.The system embeds a NeoClassic reasoner, whose structural subsumption algorithm has been modified to allow match categorization into potential and partial, and ranking of matches within categories. Experiments carried out show the good correspondence between users and system rankings.

Concept abduction and contraction for semantic-based discovery of matches and negotiation spaces in an e-marketplace

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 satisfiability - 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.

Semantic matchmaking as non-monotonic reasoning: a description logic approach

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.

Top-N recommendations from implicit feedback leveraging linked open data

The advent of the Linked Open Data (LOD) initiative gave birth to a variety of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited. 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 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 state-of-the-art top-N recommendation algorithms for implicit feedback in situations affected by different degrees of data sparsity.

A System for Principled Matchmaking in an Electronic Marketplace

More and more resources are becoming available on the Web, and there is a growing need for infrastructures that, based on advertised descriptions, can match demands with supplies in an electronic marketplace. Several matchmakers rely on simple keyword matching, but significant matching results can only be obtained by exploiting the semantics inherent in structured descriptions. To this end, a novel knowledge representation approach is proposed, based on description logics, that is superior to both keyword- and basic subsumption-based matchmaking. The properties a matchmaker should have are formalized, and a matchmaking facilitator, compliant with the desired properties, is presented. The system embeds a NeoClassic reasoner whose structural subsumption algorithm has been modified to allow match categorization into potential and partial, and ranking of matches within categories. Experiments show good correspondence between users and system rankings.

Content-Based Image Retrieval over the Web Using Query by Sketch and Relevance Feedback

This paper investigates the combined use of query by sketch and relevance feedback as techniques to ease user interaction and improve retrieval effectiveness in content-based image retrieval over the World Wide Web. To substantiate our ideas we implemented DrawSearch, a prototype image retrieval by content system that uses color, shape and texture to index and retrieve images. The system avails of Java applets for query by sketch and uses relevance feedback to allow users dynamically refine queries.

Feature integration and relevance feedback analysis in image similarity evaluation

In this article we describe the results of a study on simi- larity evaluation in image retrieval using color, object orientation, and relative position as content features, in a framework oriented to image repositories where the semantics of stored images are limited to a specific domain. The focus is not on a complete description of image content, which is supposed to be known to some extent, but on the extraction of simple and immediate features that can assure, through their combination, automated image analysis and efficient retrieval. Relevance feedback is introduced as an effective way to improve retrieval accuracy. A simple prototype system is also intro- duced that computes feature descriptors and allows users to enter queries, browse the retrieved images, and refine the results through relevance feedback analysis.

Ranking the linked data: the case of DBpedia

The recent proliferation of crowd computing initiatives on the web calls for smarter methodologies and tools to annotate, query and explore repositories. There is the need for scalable techniques able to return also approximate results with respect to a given query as a ranked set of promising alternatives. In this paper we concentrate on annotation and retrieval of software components, exploiting semantic tagging relying on Linked Open Data. We focus on DBpedia and propose a new hybrid methodology to rank resources exploiting: (i) the graphbased nature of the underlying RDF structure, (ii) context independent semantic relations in the graph and (iii) external information sources such as classical search engine results and social tagging systems. We compare our approach with other RDF similarity measures, proving the validity of our algorithm with an extensive evaluation involving real users.

Semantic matchmaking in a P-2-P electronic marketplace

Matchmaking is the problem of matching offers and requests, such as supply and demand in a marketplace, services and customers in a service agency, etc., where both partners are peers in the transaction. Peer-to-Peer (P-2-P) e-commerce calls for an infrastructure treating in a uniform way supply and demand, which should base the match on a common ontology for describing both supply and demand. Knowledge representation --- in particular description logics --- can deal with this uniform treatment of knowledge from vendors and customers, by modelling both as generic concepts to be matched. We propose a logical approach to supply-demand matching in P-2-P e-commerce, which allows us to clearly distinguish between exact, potential and partial match, and to define a ranking within the categories. The approach is deployed in a prototype system implemented for a particular case study (but easily generalizable) and is based on Classic, a well-known knowledge representation system.

Semantic-Based Resource Discovery and Orchestration in Home and Building Automation: A Multi-Agent Approach

Home and building automation (HBA) trends toward the Ambient Intelligence paradigm, which aims to autonomously coordinate and control appliances and subsystems in a given environment. Nevertheless, HBA is based on an explicit user-home interaction and basically enables static and predetermined scenarios. This paper proposes a more flexible multi-agent approach, leveraging semantic-based resource discovery and orchestration for HBA applications. Backward-compatible enhancements to EIB/KNX domotic standard allow to support the semantic characterization of user profiles and device functionalities, thus enabling: 1) negotiation of the most suitable home services/functionalities according to implicit and explicit user needs and 2) device-driven interaction for adapting the environment to context evolution. A power-management problem in HBA is presented as a case study to better clarify the proposal and assess its effectiveness.