Relational Probabilistic Conditionals and Their Instantiations under Maximum Entropy Semantics for First-Order Knowledge Bases

Beierle, Christoph; Finthammer, Marc; Kern-Isberner, Gabriele

doi:10.3390/e17020852

Cited by 11 publications

(5 citation statements)

References 15 publications

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“…We show that this approach corresponds to the definition of fuzzy filters [33]. We also provide an interpretation in probabilistic logic with maximum entropy semantics [34,35].…”

Section: Enriched Matchingmentioning

confidence: 96%

“…This enables an interpretation using fuzzy filters [33]. For the probabilistic extension our research will be based on the probabilistic logic with maximum entropy semantics in [34,35], for which sophisticated reasoning methods exist [36].…”

Section: Related Workmentioning

confidence: 99%

“…The lattice and the extra edges can be handled from an information-theoretical point of view with probabilistic logic programs [35], or from set theoretic point of view with probabilistic models [34] as well. Here we adapt the latter approach using apply the maximum entropy model to define a probabilistic matching method.…”

Section: Probabilistic Matchingmentioning

confidence: 99%

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Accurate and efficient profile matching in knowledge bases

Martinez‐Gil

Paoletti

Rácz

et al. 2018

Data & Knowledge Engineering

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A profile describes a set of properties, e.g. a set of skills a person may have, a set of skills required for a particular job, or a set of abilities a football player may have with respect to a particular team strategy. Profile matching aims to determine how well a given profile fits to a requested profile and vice versa. The approach taken in this article is grounded in a matching theory that uses filters in lattices to represent profiles, and matching values in the interval [0,1]: the higher the matching value the better is the fit. Such lattices can be derived from knowledge bases exploiting description logics to represent the knowledge about profiles. An interesting first question is, how human expertise concerning the matching can be exploited to obtain most accurate matchings. It will be shown that if a set of filters together with matching values by some human expert is given, then under some mild plausibility assumptions a matching measure can be determined such that the computed matching values preserve the relevant rankings given by the expert. A second question concerns the efficient querying of databases of profile instances. For matching queries that result in a ranked list of profile instances matching a given one it will be shown how corresponding top-k queries can be evaluated on grounds of pre-computed matching values, which in turn allows the maintenance of the knowledge base to be decoupled from the maintenance of profile instances. In addition, it will be shown how the matching queries can be exploited for gap queries that determine how profile

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“…We show that this approach corresponds to the definition of fuzzy filters [33]. We also provide an interpretation in probabilistic logic with maximum entropy semantics [34,35].…”

Section: Enriched Matchingmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Accurate and efficient profile matching in knowledge bases

Martinez‐Gil

Paoletti

Rácz

et al. 2018

Data & Knowledge Engineering

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show abstract

“…This leads to an enriched matching theory by means of values associated to paths, which enables an interpretation using fuzzy filters [12]. For the probabilistic extension this research exploits probabilistic logic with maximum entropy semantics in [1,15], for which sophisticated reasoning methods exist [30]. In the meantime this research has been taken further showing that it is possible to compute an extended lattice such that matching measures for profiles in the extended lattice capture exactly the same as the path values [28].…”

Section: Strict Matching For Fuzzy Offers and Applicationsmentioning

confidence: 99%

Refined Fuzzy Profile Matching

Rácz,

Sali,

Schewe

2023

Acta Cybern

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A profile describes a set of properties, e.g. a set of skills a person may have or a set of skills required for a particular job. Profile matching aims to determine how well a given profile fits to a requested profile and vice versa. Fuzzyness is naturally attached to this problem. The filter-based matching theory uses filters in lattices to represent profiles, and matching values in the interval [0,1], so the lattice order refers to subsumption between the concepts in a profile. In this article the lattice is extended by additional information in form of weighted extra edges that represent partial quantifiable relationships between these concepts. This gives rise to fuzzy filters, which permit a refinement of profile matching. Another way to introduce fuzzyness is to treat profiles as fuzzy sets. In the present paper we combine these two aproaches. Extra edges may introduce directed cycles in the directed graph of the ontology, and the structure of a lattice is lost. We provide a construction grounded in formal concept analysis to extend the original lattice and remove the cycles such that matching values determined over the extended lattice are exactly those resulting from the use of fuzzy filters in case of crisp profiles. For fuzzy profiles we show how to modify the weighting construction while eliminating the directed cycles but still regaining the matching values. We also give sharp estimates for the growth of the number of vertices in this construction.

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“…Christoph Beierle, Marc Finthammer and Gabriele Kern-Isberner apply MaxEnt to knowledge bases containing conditionals in which relations occur [6]. They develop a logical framework, PCI, which captures captures two kinds of semantics: grounding semantics and aggregation semantics.…”

mentioning

confidence: 99%

Maximum Entropy Applied to Inductive Logic and Reasoning

Landes

Williamson

2015

Entropy

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This editorial explains the scope of the special issue and provides a thematic introduction to the contributed papers.Keywords: entropy; MaxEnt; inductive logic; reasoning With this special issue, we wanted to provide a platform for practitioners, proponents and opponents of Maximum Entropy methods (MaxEnt) applied to inductive logic and reasoning. Unfortunately, we received no papers arguing against MaxEnt. However, we did receive an exciting array of positive contributions, which are described below.There has been much debate in the inductive logic and reasoning literature on the justificatory status of MaxEnt. Jeff Paris, in his paper [1], defends MaxEnt against the charge of language dependence and warns against mistaking subjective degrees of belief for estimates of objective probabilities and mis-applying the Principle of Insufficient Reason.The other contributed papers focus on extensions of the standard framework for MaxEnt in inductive logic and reasoning. The standard framework considers a single agent equipped with a finite propositional language (or a finite domain of propositions) and a propositional knowledge base, seeking to determine probabilities from these ingredients. Papers in this volume extend the standard framework in four different directions: the multi-agent setting, the dynamic setting, more elaborate knowledge bases, and richer underlying languages.

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Relational Probabilistic Conditionals and Their Instantiations under Maximum Entropy Semantics for First-Order Knowledge Bases

Cited by 11 publications

References 15 publications

Accurate and efficient profile matching in knowledge bases

Accurate and efficient profile matching in knowledge bases

Refined Fuzzy Profile Matching

Maximum Entropy Applied to Inductive Logic and Reasoning

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