SMART &amp;#8212; A Semantic Matchmaking Portal for Electronic Markets

Agarwal, Sudhir; Lamparter, Steffen

doi:10.1109/icect.2005.84

Cited by 28 publications

(33 citation statements)

References 5 publications

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“…In particular, it shows a similar behavior as reference-class reasoning in a number of uncontroversial examples. 1 But it also avoids many drawbacks of reference-class reasoning (which are pointed out in [5,87]): differently from reference-class reasoning, probabilistic lexicographic entailment can handle complex scenarios and even purely probabilistic subjective knowledge as input, and probabilistic lexicographic entailment draws conclusions in a global way from all the available knowledge as a whole. Furthermore, probabilistic lexicographic entailment also has very nice nonmonotonic properties, which are essentially inherited from Lehmann's lexicographic entailment [72].…”

Section: Probabilistic Uncertainty and Description Logicsmentioning

confidence: 99%

“…Possibilistic formulas have the form P ≥ l or N ≥ l, where is an event, and l is a real number from [0,1]. Informally, such formulas encode to what extent is possibly respectively necessarily true.…”

Section: Possibilistic Logicmentioning

confidence: 99%

“…Changing the usual true/false convention leads to a new concept of proposition, whose compatibility with a given state of facts is a matter of degree and can be measured on an ordered scale S that is no longer {0, 1}, but, e.g. the unit interval [0,1]. This leads to identifying a "fuzzy proposition" with a fuzzy set of possible states of affairs; the degree of membership of a state of affairs to this fuzzy set evaluates the degree of fit between the proposition and the state of facts it refers to.…”

Section: Many-valued Logicsmentioning

confidence: 99%

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Managing uncertainty and vagueness in description logics for the Semantic Web

Lukasiewicz

Straccia

2008

Journal of Web Semantics

328

101

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a b s t r a c tOntologies play a crucial role in the development of the Semantic Web as a means for defining shared terms in web resources. They are formulated in web ontology languages, which are based on expressive description logics. Significant research efforts in the semantic web community are recently directed towards representing and reasoning with uncertainty and vagueness in ontologies for the Semantic Web. In this paper, we give an overview of approaches in this context to managing probabilistic uncertainty, possibilistic uncertainty, and vagueness in expressive description logics for the Semantic Web.

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Section: Probabilistic Uncertainty and Description Logicsmentioning

confidence: 99%

Section: Possibilistic Logicmentioning

confidence: 99%

Section: Many-valued Logicsmentioning

confidence: 99%

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Managing uncertainty and vagueness in description logics for the Semantic Web

Lukasiewicz

Straccia

2008

Journal of Web Semantics

328

101

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“…Concerning the DL component, O is a finite set of DLR-Lite axioms 1 . A DLR-Lite axiom has the form C 1 C 2 (concept inclusion) or has the form…”

Section: Componentmentioning

confidence: 99%

“…An extended matchmaking approach, with negotiable and strict constraints in a DL framework has been proposed in [7], using both concept contraction and concept abduction. The need to work in someway with approximation and ranking in DL-based approaches to matchmaking has also recently led to adopting fuzzy-DLs, as in sMART [1] or hybrid approaches, as in the OWLS-MX matchmaker [13]. sMART is a semantic matchmaking portal, based on fuzzy-DLs, able to deal with approximation in the requests description handled by crisp DL-reasoners.…”

Section: Related Workmentioning

confidence: 99%

Vague Knowledge Bases for Matchmaking in P2P E-Marketplaces

Ragone

Straccia

Noia

et al. 2007

Lecture Notes in Computer Science

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Abstract. In this paper we propose an approach to semantic matchmaking that exploits various knowledge representation technologies to find most promising partners in peer-to-peer e-marketplaces. In particular we mix in a formal and principled way the semantic expressiveness of DLR-lite Logic Programs, fuzzy logic and utility theory. We adopt DLR-Lite Logic Programs to obtain a reasonable compromise between expressiveness and complexity to ensure the scalability of our approach to large e-marketplaces, and Fuzzy Logic to model logical specifications as soft constraints. Furthermore, fully exploiting the peer-to-peer paradigm, we consider in the matchmaking process preferences and corresponding utilities of both parties.

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Explanation Services and Request Refinement in User Friendly Semantic-Enabled B2C E-Marketplaces

Colucci

Noia

Sciascio

et al. 2006

Data Engineering Issues in E-Commerce and Services

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Abstract. This paper presents an approach aimed at fully exploiting semantics of supply/demand descriptions in B2C and C2C e-marketplaces. Distinguishing aspects include logic-based explanation of request results, semantic ranking of matchmaking results, logic-based request refinement. The user interface has been designed and implemented to be immediate and simple, and it requires no knowledge of any logic principle to be fully used.

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SMART — A Semantic Matchmaking Portal for Electronic Markets

Cited by 28 publications

References 5 publications

Managing uncertainty and vagueness in description logics for the Semantic Web

Managing uncertainty and vagueness in description logics for the Semantic Web

Vague Knowledge Bases for Matchmaking in P2P E-Marketplaces

Explanation Services and Request Refinement in User Friendly Semantic-Enabled B2C E-Marketplaces

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