Variable-strength conditional preferences for ranking objects in ontologies

Lukasiewicz, Thomas; Schellhase, Jörg

doi:10.1016/j.websem.2007.06.001

Cited by 19 publications

(21 citation statements)

References 27 publications

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“…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%

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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: A General Marketplacementioning

confidence: 99%

“…terparts, based on textual information [42], or to compare the logical representations of supply and demand [40,27], or combine both scores and logic in some way [20,39,7,22]. Our proposal falls in this last category, mixing in a formal way Datalog, Fuzzy sets, and Utility Theory.…”

Section: Introductionmentioning

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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“…However, the pragmatic considerations discussed above lead us to not adopt it directly, although it remains the gold standard. The theories (or models) in our fielded systems are based on qualitative probabilistic matching (Smyth and Poole, 2004;Poole and Smyth, 2005;Lukasiewicz and Schellhase, 2007), with the following properties:…”

Section: Bayesian Modelling Meets Pragmatismmentioning

confidence: 99%

Semantic Science: Ontologies, Data and Probabilistic Theories

Poole

Smyth²,

Sharma³

2008

Lecture Notes in Computer Science

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Abstract. This chapter overviews work on semantic science. The idea is that, using rich ontologies, both observational data and theories that make (probabilistic) predictions on data are published for the purposes of improving or comparing the theories, and for making predictions in new cases. This paper concentrates on issues and progress in having machine accessible scientific theories that can be used in this way. This paper presents the grand vision, issues that have arisen in building such systems for the geological domain (minerals exploration and geohazards), and sketches the formal foundations that underlie this vision. The aim is to get to the stage where: any new scientific theory can be tested on all available data; any new data can be used to evaluate all existing theories that make predictions on that data; and when someone has a new case they can use the best theories that make predictions on that case.

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“…Nevertheless in such approaches the matchmaking process is defined according to buyer's perspective. In [16] a language able to express conditional preferences is proposed to perform a matchmaking in Description Logics. Also in this case nothing is said on how to compute a agreementas needed in P2P scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…They either try to compute a score of possible counterparts, based on textual information [25], or to compare the logical representations of supply and demand [10], or combine both scores and logic in some way [7,16]. Our proposal falls in this last category, mixing in a formal way ontologies in DLR-Lite, Datalog rules, Fuzzy sets, and Utility Theory.…”

Section: Introductionmentioning

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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Variable-strength conditional preferences for ranking objects in ontologies

Cited by 19 publications

References 27 publications

Fuzzy matchmaking in e-marketplaces of peer entities using Datalog

Fuzzy matchmaking in e-marketplaces of peer entities using Datalog

Semantic Science: Ontologies, Data and Probabilistic Theories

Vague Knowledge Bases for Matchmaking in P2P E-Marketplaces

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