39 research outputs found
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
Querying Incomplete Data : Complexity and Tractability via Datalog and First-Order Rewritings
To answer database queries over incomplete data the gold standard is finding
certain answers: those that are true regardless of how incomplete data is
interpreted. Such answers can be found efficiently for conjunctive queries and
their unions, even in the presence of constraints. With negation added, the
problem becomes intractable however. We concentrate on the complexity of
certain answers under constraints, and on effficiently answering queries
outside the usual classes of (unions) of conjunctive queries by means of
rewriting as Datalog and first-order queries. We first notice that there are
three different ways in which query answering can be cast as a decision
problem. We complete the existing picture and provide precise complexity bounds
on all versions of the decision problem, for certain and best answers. We then
study a well-behaved class of queries that extends unions of conjunctive
queries with a mild form of negation. We show that for them, certain answers
can be expressed in Datalog with negation, even in the presence of functional
dependencies, thus making them tractable in data complexity. We show that in
general Datalog cannot be replaced by first-order logic, but without
constraints such a rewriting can be done in first-order. The paper is under
consideration in Theory and Practice of Logic Programming (TPLP).Comment: Under consideration in Theory and Practice of Logic Programming
(TPLP
Domain ontology learning from the web
El Aprendizaje de Ontologías se define como el conjunto de métodos utilizados para construir, enriquecer o adaptar una ontología existente de forma semiautomática, utilizando fuentes de información heterogéneas. En este proceso se emplea texto, diccionarios electrónicos, ontologías lingüísticas e información estructurada y semiestructurada para extraer conocimiento. Recientemente, gracias al enorme crecimiento de la Sociedad de la Información, la Web se ha convertido en una valiosa fuente de información para casi cualquier dominio. Esto ha provocado que los investigadores empiecen a considerar a la Web como un repositorio válido para Recuperar Información y Adquirir Conocimiento. No obstante, la Web presenta algunos problemas que no se observan en repositorios de información clásicos: presentación orientada al usuario, ruido, fuentes no confiables, alta dinamicidad y tamaño abrumador. Pese a ello, también presenta algunas características que pueden ser interesantes para la adquisición de conocimiento: debido a su enorme tamaño y heterogeneidad, se asume que la Web aproxima la distribución real de la información a nivel global. Este trabajo describe una aproximación novedosa para el aprendizaje de ontologías, presentando nuevos métodos para adquirir conocimiento de la Web. La propuesta se distingue de otros trabajos previos principalmente en la particular adaptación de algunas técnicas clásicas de aprendizaje al corpus Web y en la explotación de las características interesantes del entorno Web para componer una aproximación automática, no supervisada e independiente del dominio. Con respecto al proceso de construcción de la ontologías, se han desarrollado los siguientes métodos: i) extracción y selección de términos relacionados con el dominio, organizándolos de forma taxonómica; ii) descubrimiento y etiquetado de relaciones no taxonómicas entre los conceptos; iii) métodos adicionales para mejorar la estructura final, incluyendo la detección de entidades con nombre, atributos, herencia múltiple e incluso un cierto grado de desambiguación semántica. La metodología de aprendizaje al completo se ha implementado mediante un sistema distribuido basado en agentes, proporcionando una solución escalable. También se ha evaluado para varios dominios de conocimiento bien diferenciados, obteniendo resultados de buena calidad. Finalmente, se han desarrollado varias aplicaciones referentes a la estructuración automática de librerías digitales y recursos Web, y la recuperación de información basada en ontologías.Ontology Learning is defined as the set of methods used for building from scratch, enriching or adapting an existing ontology in a semi-automatic fashion using heterogeneous information sources. This data-driven procedure uses text, electronic dictionaries, linguistic ontologies and structured and semi-structured information to acquire knowledge. Recently, with the enormous growth of the Information Society, the Web has become a valuable source of information for almost every possible domain of knowledge. This has motivated researchers to start considering the Web as a valid repository for Information Retrieval and Knowledge Acquisition. However, the Web suffers from problems that are not typically observed in classical information repositories: human oriented presentation, noise, untrusted sources, high dynamicity and overwhelming size. Even though, it also presents characteristics that can be interesting for knowledge acquisition: due to its huge size and heterogeneity it has been assumed that the Web approximates the real distribution of the information in humankind. The present work introduces a novel approach for ontology learning, introducing new methods for knowledge acquisition from the Web. The adaptation of several well known learning techniques to the web corpus and the exploitation of particular characteristics of the Web environment composing an automatic, unsupervised and domain independent approach distinguishes the present proposal from previous works.With respect to the ontology building process, the following methods have been developed: i) extraction and selection of domain related terms, organising them in a taxonomical way; ii) discovery and label of non-taxonomical relationships between concepts; iii) additional methods for improving the final structure, including the detection of named entities, class features, multiple inheritance and also a certain degree of semantic disambiguation. The full learning methodology has been implemented in a distributed agent-based fashion, providing a scalable solution. It has been evaluated for several well distinguished domains of knowledge, obtaining good quality results. Finally, several direct applications have been developed, including automatic structuring of digital libraries and web resources, and ontology-based Web Information Retrieval
The DLV System for Knowledge Representation and Reasoning
This paper presents the DLV system, which is widely considered the
state-of-the-art implementation of disjunctive logic programming, and addresses
several aspects. As for problem solving, we provide a formal definition of its
kernel language, function-free disjunctive logic programs (also known as
disjunctive datalog), extended by weak constraints, which are a powerful tool
to express optimization problems. We then illustrate the usage of DLV as a tool
for knowledge representation and reasoning, describing a new declarative
programming methodology which allows one to encode complex problems (up to
-complete problems) in a declarative fashion. On the foundational
side, we provide a detailed analysis of the computational complexity of the
language of DLV, and by deriving new complexity results we chart a complete
picture of the complexity of this language and important fragments thereof.
Furthermore, we illustrate the general architecture of the DLV system which
has been influenced by these results. As for applications, we overview
application front-ends which have been developed on top of DLV to solve
specific knowledge representation tasks, and we briefly describe the main
international projects investigating the potential of the system for industrial
exploitation. Finally, we report about thorough experimentation and
benchmarking, which has been carried out to assess the efficiency of the
system. The experimental results confirm the solidity of DLV and highlight its
potential for emerging application areas like knowledge management and
information integration.Comment: 56 pages, 9 figures, 6 table
IMPACTS in natural language generation NLG between technology and applications : workshop at Schloss Dagstuhl, Germany July 26-28, 2000
Instructional text, because it is a useful and relatively constrained sub-Ianguage, has been a popular target for research-oriented generation systems. This work has demonstrated that existing technology is adequate for generating draft instructions; the problem, as is typical of generation work in general, has been with the acquisition of domain and lexicogrammatical knowledge. This acquisition task is a formidable barrier to the practical use of generation
technology. The Isolde project attempts to address this problem by extracting
parts of the required knowledge from existing models and by building tools to
tailor what is extracted into a form suitable for generation
IMPACTS in natural language generation NLG between technology and applications : workshop at Schloss Dagstuhl, Germany July 26-28, 2000
Instructional text, because it is a useful and relatively constrained sub-Ianguage, has been a popular target for research-oriented generation systems. This work has demonstrated that existing technology is adequate for generating draft instructions; the problem, as is typical of generation work in general, has been with the acquisition of domain and lexicogrammatical knowledge. This acquisition task is a formidable barrier to the practical use of generation
technology. The Isolde project attempts to address this problem by extracting
parts of the required knowledge from existing models and by building tools to
tailor what is extracted into a form suitable for generation