Eufemia Tinelli

A Nonmonotonic Approach to Semantic Matchmaking and Request Refinement in E-Marketplaces

Matchmaking in e-marketplaces consists of finding and retrieving promising counterparts for a given request from the set of available advertisements. This paper proposes the use of nonmonotonic inferences (concept contraction and concept abduction) in a semantic-matchmaking process for ranking resource descriptions. Concept contraction can be used to amend requests incompatible with the resource descriptions. The more amendments needed, the less is the degree of match. If a request is compatible with an advertisement but does not subsume it, concept abduction can be used to hypothesize extra features in the advertisement. The more it is necessary to hypothesize, the less is the degree of match. These basic ideas are utilized to compute a meaningful matchmaking ranking. Using logical explanations on matchmaking results, an approach and algorithms are proposed for the progressive refinement and revision of requests, up to an almost exact match. The related issue of user interaction is also tackled, and a user-friendly tool is presented that allows full utilization of the semantic-based query/revision/refinement process while completely hiding logical technicalities.

Semantic-Based Bluetooth-RFID Interaction for Advanced Resource Discovery in Pervasive Contexts

We propose a novel object discovery framework integrating the application layer of Bluetooth and RFID standards. The approach is motivated and illustrated in an innovative u-commerce setting. Given a request, it allows an advanced discovery process, exploiting semantically annotated descriptions of goods available in the u-marketplace. The RFID data exchange protocol and the Bluetooth service discovery protocol have been modified and enhanced to enable support for such semantic annotation of products. Modifications to the standards have been conceived to be backward compatible, thus allowing the smooth coexistence of the legacy discovery and/or identification features. Also noteworthy is the introduction of a dedicated compression tool to reduce storage/transmission problems due to the verbosity of XML-based semantic languages.

I.M.P.A.K.T.: An Innovative Semantic-based Skill Management System Exploiting Standard SQL

The paper presents I.M.P.A.K.T. (Information Management and Processing with the Aid of Knowledge-based Technologies), a semantic-enabled platform for skills and talent management. In spite of the full exploitation of recent advances in semantic technologies, the proposed system only relies on standard SQL queries. Distinguishing features include: the possibility to express both strict requirements and preferences in the requested profile, a logic-based ranking of retrieved candidates and the explanation of rank results. System features are discussed in comparison with similar approaches, e.g., SQLf, and both quantitative and qualitative experimental results are proposed.

Automating competence management through non-standard reasoning

Competence management calls for automated knowledge-based services in order to take full advantage from the know-how of a company. This paper presents an integrated semantic-based knowledge management system providing decision support services for several activities typical of competence management, including core competence evaluation, human resources allocation, training programs planning. Knowledge resources are represented according to the formalism of Description Logics, which also allows for inference services crucial for the implemented solutions. We present the various features of our approach, which exploit advanced non-standard reasoning services from Description Logics, specifically developed to support knowledge management.

Informative Top-k Retrieval for Advanced Skill Management

The paper presents a knowledge-based framework for skills and talent management based on an advanced matchmaking between profiles of candidates and available job positions. Interestingly, informative content of top-k retrieval is enriched through semantic capabilities. The proposed approach allows to: (1) express a requested profile in terms of both hard constraints and soft ones; (2) provide a ranking function based also on qualitative attributes of a profile; (3) explain the resulting outcomes (given a job request, a motivation for the obtained score of each selected profile is provided). Top-k retrieval allows to select most promising candidates according to an ontology formalizing the domain knowledge. Such a knowledge is further exploited to provide a semantic-based explanation of missing or conflicting features in retrieved profiles. They also indicate additional profile characteristics emerging by the retrieval procedure for a further request refinement. A concrete case study followed by an exhaustive experimental campaign is reported to prove the approach effectiveness.

Large scale skill matching through knowledge compilation

We present a logic-based framework for automated skill matching, able to return a ranked referral list and the related ranking explanation. Thanks to a Knowledge Compilation approach, a knowledge base in Description Logics is translated into a relational database, without loss of information. Skill matching inference services are then efficiently executed via SQL queries. Experimental results for scalability and turnaround times on large scale data sets are reported, confirming the validity of the approach.

Knowledge compilation for automated Team Composition exploiting standard SQL

Automatically finding suitable candidates in an organization to compose a team able to solve a given task is a typical problem in large companies. In this paper we present a Description Logics approach to Team Composition based on candidates technical knowledge and on tasks descriptions, modeled according to a skills ontology in ALE(D). The novelty of our approach is that our implemented service exploits standard-SQL querying expressiveness to emulate the proper reasoning procedures. The Team Composition service has been deployed as part of I.M.P.A.K.T., a skill management system, and results show the effectiveness of the proposed approach.

Finding informative commonalities in concept collections

The problem of finding commonalities characterizes several Knowledge Management scenarios involving collection of resources. The automatic extraction of shared features in a collection of resource descriptions formalized in accordance with a logic language has been in fact widely investigated in the past. In particular, with reference to Description Logics concept descriptions, Least Common Subsumers have been specifically introduced. Nevertheless, such studies focused on identifying features shared by the whole collection. The paper proposes instead novel reasoning services in Description Logics, aimed at identifying commonalities in a significant portion of the collection, rather than in the collection as a whole. In particular, common subsumers adding informative content to the one provided by the Least Common Subsumer are here investigated. The new services are useful in all scenarios where features are not required to be fully shared, like the one motivating our research: Core Competence extraction in knowledge intensive companies.

Semantic-Based Top-k Retrieval for Competence Management

We present a knowledge-based system, for skills and talent management, exploiting semantic technologies combined with top-k retrieval techniques. The system provides advanced distinguishing features, including the possibility to formulate queries by expressing both strict requirements and preferences in the requested profile and a semantic-based ranking of retrieved candidates. Based on the knowledge formalized within a domain ontology, the system implements an approach exploiting top-k based reasoning services to evaluate semantic similarity between the requested profile and retrieved ones. System performance is discussed through the presentation of experimental results.

A semantic-based registry enabling discovery, composition and substitution of pervasive services

In this paper we present a semantic-enhanced registry specifically devised for pervasive environments, able to cope with automated mobile service discovery and composition, compliant with OWL-S and with Semantic Web technologies. The proposed approach also deals with non-exact matches (computing an approximate result) and implements a dynamic substitution of services, especially useful in highly unpredictable contexts. It has been implemented and tested in a home/office automation case study, and we also report on experimental results.