2,454 research outputs found
Upwards Closed Dependencies in Team Semantics
We prove that adding upwards closed first-order dependency atoms to
first-order logic with team semantics does not increase its expressive power
(with respect to sentences), and that the same remains true if we also add
constancy atoms. As a consequence, the negations of functional dependence,
conditional independence, inclusion and exclusion atoms can all be added to
first-order logic without increasing its expressive power.
Furthermore, we define a class of bounded upwards closed dependencies and we
prove that unbounded dependencies cannot be defined in terms of bounded ones.Comment: In Proceedings GandALF 2013, arXiv:1307.416
On the probabilistic logical modelling of quantum and geometrically-inspired IR
Information Retrieval approaches can mostly be classed into probabilistic, geometric or logic-based. Recently, a new unifying framework for IR has emerged that integrates a probabilistic description within a geometric framework, namely vectors in Hilbert spaces. The geometric model leads naturally to a predicate logic over linear subspaces, also known as quantum logic. In this paper we show the relation between this model and classic concepts such as the Generalised Vector Space Model, highlighting similarities and differences. We also show how some fundamental components of quantum-based IR can be modelled in a descriptive way using a well-established tool, i.e. Probabilistic Datalog
Bayesian Logic Programs
Bayesian networks provide an elegant formalism for representing and reasoning
about uncertainty using probability theory. Theyare a probabilistic extension
of propositional logic and, hence, inherit some of the limitations of
propositional logic, such as the difficulties to represent objects and
relations. We introduce a generalization of Bayesian networks, called Bayesian
logic programs, to overcome these limitations. In order to represent objects
and relations it combines Bayesian networks with definite clause logic by
establishing a one-to-one mapping between ground atoms and random variables. We
show that Bayesian logic programs combine the advantages of both definite
clause logic and Bayesian networks. This includes the separation of
quantitative and qualitative aspects of the model. Furthermore, Bayesian logic
programs generalize both Bayesian networks as well as logic programs. So, many
ideas developedComment: 52 page
Unified Foundations of Team Semantics via Semirings
Semiring semantics for first-order logic provides a way to trace how facts
represented by a model are used to deduce satisfaction of a formula. Team
semantics is a framework for studying logics of dependence and independence in
diverse contexts such as databases, quantum mechanics, and statistics by
extending first-order logic with atoms that describe dependencies between
variables. Combining these two, we propose a unifying approach for analysing
the concepts of dependence and independence via a novel semiring team
semantics, which subsumes all the previously considered variants for
first-order team semantics. In particular, we study the preservation of
satisfaction of dependencies and formulae between different semirings. In
addition we create links to reasoning tasks such as provenance, counting, and
repairs
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