Top-k Retrieval in Description Logic Programs Under Vagueness for the Semantic Web

Lukasiewicz, Thomas; Straccia, Umberto

doi:10.1007/978-3-540-75410-7_2

Cited by 37 publications

(42 citation statements)

References 19 publications

(26 reference statements)

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“…We extend it by allowing soft constraint predicates to appear in rules and queries (we call the language Top-k-Datalog). The proposal here extends the work [35] in which soft constraint predicates, i.e., fuzzy predicates, may appear in queries only and is conceptually equivalent to [23,36]. Basically, we allow vague/fuzzy predicates to occur in rule bodies, which have the effect that each tuple in the answer set of a query has a score in [0,1].…”

Section: Basic Technologiesmentioning

confidence: 98%

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Fuzzy matchmaking in e-marketplaces of peer entities using Datalog

Ragone

Straccia

Noia

et al. 2009

Fuzzy Sets and Systems

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We present an approach to matchmaking in electronic marketplaces of peer entities, which mixes in a formal and principled way Datalog, fuzzy sets and utility theory, in order to determine the most promising matches between prospective counterparts. The use of Datalog ensures the scalability of our approach to large marketplaces, while Fuzzy Logic provides a neat connection with logical specifications and allows to model soft constraints and how well they could be satisfied by an agreement. Noteworthy is that our approach takes into account in the peer-to-peer matchmaking also preferences of each counterpart and their utilities. This allows to rule out of the match list those counteroffers that, although seemingly appealing for the buyer, would probably lead to failure due to contrasting preferences of the seller, and paves the way to the actual negotiation stage.

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Section: Basic Technologiesmentioning

confidence: 98%

“…Observe that b and v 3 are variables, that can unify only with a buyer and a seller, respectively, due to the new definitions (22)(23) of Buyer and Seller. Now we can have two matchmaking services in the marketplace.…”

Section: A General Marketplacementioning

confidence: 99%

Fuzzy matchmaking in e-marketplaces of peer entities using Datalog

Ragone

Straccia

Noia

et al. 2009

Fuzzy Sets and Systems

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“…Sound and complete reasoning procedures were introduced in [100] for a basic fuzzy description logic and a particular choice for the fuzzy logic connectives. More recently, among others, more expressive description logics have been considered [97,99], larger classes of fuzzy logic connectives [37], and more complex reasoning tasks [54]. Of particular interest are fuzzy description logics with concrete domains [101], which allow to explicitly define fuzzy predicates which can then be used in the definition of concepts.…”

Section: Fuzzy Description Logicsmentioning

confidence: 99%

Fuzzy Methods on the Web: A Critical Discussion

Schockaert

Makarytska

Cock

2010

35 Years of Fuzzy Set Theory

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Gradual concepts abound in many web-related domains, ranging from the notion of relevance in information retrieval, to the strength of connection in online social networks. As a result, fuzzy set theory is often a natural choice in implementing web systems. In this chapter, we give an overview of applications of fuzzy set theory in this area, focusing in particular on information retrieval, the semantic web, and recommender systems. In each case, we contrast fuzzy methods with other approaches, analyzing why and how the ideas of fuzzy set theory may be beneficial.

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“…The logic extends DLR-Lite by enriching it with built-in predicates. Conjunctive queries are enriched with scoring functions that allow to rank and retrieve the top-k answers, that is, we support Top-k Query Answering [14,17,18,19,20], (find top-k scored tuples satisfying query), e.g., "find candidates with excellent knowledge in DLR-Lite", where EXCELLENT is a function of the years of experience.…”

Section: Why Another System For Hrmmentioning

confidence: 99%

Semantic-Based Top-k Retrieval for Competence Management

Straccia

Tinelli

Colucci

et al. 2009

Lecture Notes in Computer Science

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Abstract. 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.

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Top-k Retrieval in Description Logic Programs Under Vagueness for the Semantic Web

Cited by 37 publications

References 19 publications

Fuzzy matchmaking in e-marketplaces of peer entities using Datalog

Fuzzy matchmaking in e-marketplaces of peer entities using Datalog

Fuzzy Methods on the Web: A Critical Discussion

Semantic-Based Top-k Retrieval for Competence Management

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