3,014 research outputs found
Inductive Logic Programming in Databases: from Datalog to DL+log
In this paper we address an issue that has been brought to the attention of
the database community with the advent of the Semantic Web, i.e. the issue of
how ontologies (and semantics conveyed by them) can help solving typical
database problems, through a better understanding of KR aspects related to
databases. In particular, we investigate this issue from the ILP perspective by
considering two database problems, (i) the definition of views and (ii) the
definition of constraints, for a database whose schema is represented also by
means of an ontology. Both can be reformulated as ILP problems and can benefit
from the expressive and deductive power of the KR framework DL+log. We
illustrate the application scenarios by means of examples. Keywords: Inductive
Logic Programming, Relational Databases, Ontologies, Description Logics, Hybrid
Knowledge Representation and Reasoning Systems. Note: To appear in Theory and
Practice of Logic Programming (TPLP).Comment: 30 pages, 3 figures, 2 tables
Relational Representations in Reinforcement Learning: Review and Open Problems
This paper is about representation in RL.We discuss some of the concepts in representation and generalization in reinforcement learning and argue for higher-order representations, instead of the commonly used propositional representations. The paper contains a small review of current reinforcement learning systems using higher-order representations, followed by a brief discussion. The paper ends with research directions and open problems.\u
On the Implementation of the Probabilistic Logic Programming Language ProbLog
The past few years have seen a surge of interest in the field of
probabilistic logic learning and statistical relational learning. In this
endeavor, many probabilistic logics have been developed. ProbLog is a recent
probabilistic extension of Prolog motivated by the mining of large biological
networks. In ProbLog, facts can be labeled with probabilities. These facts are
treated as mutually independent random variables that indicate whether these
facts belong to a randomly sampled program. Different kinds of queries can be
posed to ProbLog programs. We introduce algorithms that allow the efficient
execution of these queries, discuss their implementation on top of the
YAP-Prolog system, and evaluate their performance in the context of large
networks of biological entities.Comment: 28 pages; To appear in Theory and Practice of Logic Programming
(TPLP
Automated Game Design Learning
While general game playing is an active field of research, the learning of
game design has tended to be either a secondary goal of such research or it has
been solely the domain of humans. We propose a field of research, Automated
Game Design Learning (AGDL), with the direct purpose of learning game designs
directly through interaction with games in the mode that most people experience
games: via play. We detail existing work that touches the edges of this field,
describe current successful projects in AGDL and the theoretical foundations
that enable them, point to promising applications enabled by AGDL, and discuss
next steps for this exciting area of study. The key moves of AGDL are to use
game programs as the ultimate source of truth about their own design, and to
make these design properties available to other systems and avenues of inquiry.Comment: 8 pages, 2 figures. Accepted for CIG 201
Introduction to the TPLP special issue, logic programming in databases: From Datalog to semantic-web rules
Much has happened in data and knowledge base research since the introduction
of the relational model in Codd (1970) and its strong logical foundations influence
its advances ever since. Logic has been a common ground where Database and
Artificial Intelligence research competed and collaborated with each other for a
long time (Abiteboul et al. 1995). The product of this joint effort has been a set of
logic-based formalisms, such as the Relational Calculus (Codd 1970), Datalog (Ceri
et al. 1990), Description Logics (Baader et al. 2007), etc., capturing not only the
structure but also the semantics of data in an explicit way, thus enabling complex
inference procedures.This special issue contains three rigorously reviewed articles addressing problems
that span from Query Answering to Data Mining. All these contributions have their
roots in the foundational formalisms of Data and Knowledge Bases such as Logic
Programming, Description Logic and Hybrid Logics, representing a clear example
of the effort that the Database and the Semantic-Web communities are producing to
bridge the various schools of thinking in modern Data and Knowledge Management
10302 Abstracts Collection -- Learning paradigms in dynamic environments
From 25.07. to 30.07.2010, the Dagstuhl Seminar 10302 ``Learning paradigms in dynamic environments \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Empowering Knowledge Bases: a Machine Learning Perspective
The construction of Knowledge Bases requires quite often
the intervention of knowledge engineering and domain experts, resulting
in a time consuming task. Alternative approaches have been developed
for building knowledge bases from existing sources of information such
as web pages and crowdsourcing; seminal examples are NELL, DBPedia,
YAGO and several others. With the goal of building very large sources of
knowledge, as recently for the case of Knowledge Graphs, even more complex
integration processes have been set up, involving multiple sources of
information, human expert intervention, crowdsourcing. Despite signiā-
cant eāorts for making Knowledge Graphs as comprehensive and reliable
as possible, they tend to suāer of incompleteness and noise, due to the
complex building process. Nevertheless, even for highly human curated
knowledge bases, cases of incompleteness can be found, for instance with
disjointness axioms missing quite often. Machine learning methods have
been proposed with the purpose of reāning, enriching, completing and
possibly raising potential issues in existing knowledge bases while showing
the ability to cope with noise. The talk will concentrate on classes
of mostly symbol-based machine learning methods, speciācally focusing
on concept learning, rule learning and disjointness axioms learning problems,
showing how the developed methods can be exploited for enriching
existing knowledge bases. During the talk it will be highlighted as, a
key element of the illustrated solutions, is represented by the integration
of: background knowledge, deductive reasoning and the evidence coming
from the mass of the data. The last part of the talk will be devoted
to the presentation of an approach for injecting background knowledge
into numeric-based embedding models to be used for predictive tasks on
Knowledge Graphs
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