Variable-Strength Conditional Preferences for Ranking Objects in Ontologies

Lukasiewicz, Thomas; Schellhase, Jörg

doi:10.2139/ssrn.3199366

Cited by 5 publications

References 30 publications

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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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Vague Knowledge Bases for Matchmaking in P2P E-Marketplaces

Ragone

Straccia

Noia

et al. 2007

Lecture Notes in Computer Science

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Increasing Bid Expressiveness for Effective and Balanced E-Barter Trading

Ragone

Noia

Sciascio

et al. 2009

Lecture Notes in Computer Science

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Abstract. We present a novel knowledge-based approach for automated electronic barter trade systems. An e-barter is basically a closed e-marketplace, where agents may exchange (buy/sell) goods -or equivalent trade dollars-only with other participants to the e-barter. Obviously, in such systems one of the major issues is keeping exchanges as balanced as possible. If the description of goods or services to be exchanged is simple and limited to a well defined set, e.g., oil, wheat, transport, etc., then an exchange based only on price and quantity is enough. But, what if goods or services to be exchanged are described in a complex way? Is it a suitable exchange the one involving mobile phones supporting video streaming with a QWERTY keyboard if the agent is looking for smart phones? Those two descriptions, although very different form a syntactic point of view, are very similar with respect to their meaning (semantics). How could an agent manage and exploit the knowledge on a given domain to deal with such a semantic information and optimize exchanges?We focus on how to find most promising matches, in a many-to-many matchmaking process, between bids (supplies/demands), taking into account not only the price and quantities as in classical barter trade systems, but also a semantic similarity among bid descriptions while keeping exchanges balanced.To this aim we use a logical language to express agent preferences, thereby enhancing bid expressiveness. We also define a logic-based utility function that allows to evaluate the semantic similarity between bids. Finally we illustrate the optimization problem we solve in order to clear the market.

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