26 research outputs found
A Goal-Directed Implementation of Query Answering for Hybrid MKNF Knowledge Bases
Ontologies and rules are usually loosely coupled in knowledge representation
formalisms. In fact, ontologies use open-world reasoning while the leading
semantics for rules use non-monotonic, closed-world reasoning. One exception is
the tightly-coupled framework of Minimal Knowledge and Negation as Failure
(MKNF), which allows statements about individuals to be jointly derived via
entailment from an ontology and inferences from rules. Nonetheless, the
practical usefulness of MKNF has not always been clear, although recent work
has formalized a general resolution-based method for querying MKNF when rules
are taken to have the well-founded semantics, and the ontology is modeled by a
general oracle. That work leaves open what algorithms should be used to relate
the entailments of the ontology and the inferences of rules. In this paper we
provide such algorithms, and describe the implementation of a query-driven
system, CDF-Rules, for hybrid knowledge bases combining both (non-monotonic)
rules under the well-founded semantics and a (monotonic) ontology, represented
by a CDF Type-1 (ALQ) theory. To appear in Theory and Practice of Logic
Programming (TPLP
Epistemic Reasoning in OWL 2 DL
We extend the description logic SROIQ (OWL 2 DL) with the epistemic operator K and argue that unintended effects occur when imposing the semantics traditionally employed. Consequently, we identify the most expressive DL for which the traditional approach can still be adapted. For the epistemic extension of SROIQ and alike expressive DLs, we suggest a revised semantics that behaves more intuitively in these cases and coincides with the traditional semantics on less expressive DLs
Combining open and closed world reasoning for the semantic web
Dissertação para obtenção do Grau de Doutor
em InformáticaOne important problem in the ongoing standardization of knowledge representation
languages for the Semantic Web is combining open world ontology languages, such as the OWL-based ones, and closed world rule-based languages.
The main difficulty of such a combination is that both formalisms are quite orthogonal w.r.t. expressiveness and how decidability is achieved. Combining non-monotonic rules and ontologies is thus a challenging task
that requires careful balancing between expressiveness of the knowledge representation language and the computational complexity of reasoning.
In this thesis, we will argue in favor of a combination of ontologies and nonmonotonic
rules that tightly integrates the two formalisms involved, that has a computational complexity that is as low as possible, and that allows us to query for information instead of calculating the whole model. As our starting point we choose the mature approach of hybrid MKNF knowledge
bases, which is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. We extend the two-valued framework of MKNF logics to a three-valued logics, and we propose a well-founded semantics for non-disjunctive hybrid MKNF knowledge bases. This new semantics promises to provide better efficiency of reasoning,and it is faithful w.r.t. the original two-valued MKNF semantics and compatible with both the OWL-based semantics and the traditional Well-
Founded Semantics for logic programs. We provide an algorithm based on operators to compute the unique model, and we extend SLG resolution with tabling to a general framework that allows us to query a combination of non-monotonic rules and any given ontology language. Finally, we
investigate concrete instances of that procedure w.r.t. three tractable ontology
languages, namely the three description logics underlying the OWL 2 pro les.Fundação para a Ciência e Tecnologia - grant contract SFRH/BD/28745/200
OWL and Rules
The relationship between the Web Ontology Language OWL and rule-based formalisms has been the subject of many discussions and research investigations, some of them controversial. From the many attempts to reconcile the two paradigms, we present some of the newest developments. More precisely, we show which kind of rules can be modeled in the current version of OWL, and we show how OWL can be extended to incorporate rules. We finally give references to a large body of work on rules and OWL
Derivation methods for hybrid knowledge bases with rules and ontologies
Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia InformáticaFirst of all, I would like to thank my advisor, José Júlio Alferes, for his incredible support. Right from the start, during the first semester of this work, when we were 2700 km apart and meeting regularly via Skype, until the end of this dissertation, he was always committed and available for discussions, even when he had lots of other urgent things to do.
A really special thanks to Terrance Swift, whom acted as an advisor, helping me a lot in
the second implementation, and correcting all XSB’s and CDF’s bugs. This implementation
wouldn’t surely have reached such a fruitful end without his support.
I would also like to thank all my colleagues and friends at FCT for the great work environment and for not letting me take myself too serious. A special thanks to my colleagues from Dresden for encouraging me to work even when there were so many other interesting things to do as an Erasmus student.
I’m indebted to Luís Leal, Bárbara Soares, Jorge Soares and Cecília Calado, who kindly
accepted to read a preliminary version of this report and gave me their valuable comments.
For giving me working conditions and a partial financial support, I acknowledge the Departamento de Informática of the Faculdade de Ciências e Tecnologias of Universidade Nova de Lisboa.
Last, but definitely not least, I would like to thank my parents and all my family for their continuous encouragement and motivation. A special thanks to Bruno for his love, support and patience
Local Closed-World Reasoning with Description Logics under the Well-Founded Semantics
An important question for the upcoming Semantic Web is how to best combine open world ontology languages, such as the OWL-based ones, with closed world rule-based languages. One of the most mature proposals for this combination is known as hybrid MKNF knowledge bases (Motik and Rosati, 2010 [52]), and it is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. In this paper we propose a well-founded semantics for nondisjunctive hybrid MKNF knowledge bases that promises to provide better efficiency of reasoning, and that is compatible with both the OWL-based semantics and the traditional Well-Founded Semantics for logic programs. Moreover, our proposal allows for the detection of inconsistencies, possibly occurring in tightly integrated ontology axioms and rules, with only little additional effort. We also identify tractable fragments of the resulting language
Efficient paraconsistent reasoning with rules and ontologies for the semantic web
Ontologies formalized by means of Description Logics (DLs) and rules in the form of Logic Programs (LPs) are two prominent formalisms in the field of Knowledge Representation and Reasoning. While DLs adhere to the OpenWorld Assumption and are suited for taxonomic reasoning, LPs implement reasoning under the Closed World Assumption, so that default knowledge can be expressed. However, for many applications it is useful to have a means that allows reasoning over an open domain and expressing rules with exceptions at the same time. Hybrid MKNF knowledge bases make such a means available by formalizing DLs and LPs in a common logic, the Logic of Minimal Knowledge and Negation as Failure (MKNF).
Since rules and ontologies are used in open environments such as the Semantic Web,
inconsistencies cannot always be avoided. This poses a problem due to the Principle of Explosion, which holds in classical logics. Paraconsistent Logics offer a solution to this issue by assigning meaningful models even to contradictory sets of formulas. Consequently, paraconsistent semantics for DLs and LPs have been investigated intensively. Our goal is to apply the paraconsistent approach to the combination of DLs and LPs in hybrid MKNF knowledge bases.
In this thesis, a new six-valued semantics for hybrid MKNF knowledge bases is introduced, extending the three-valued approach by Knorr et al., which is based on the wellfounded semantics for logic programs. Additionally, a procedural way of computing paraconsistent well-founded models for hybrid MKNF knowledge bases by means of an alternating fixpoint construction is presented and it is proven that the algorithm is sound and complete w.r.t. the model-theoretic characterization of the semantics. Moreover, it is shown that the new semantics is faithful w.r.t. well-studied paraconsistent semantics for DLs and LPs, respectively, and maintains the efficiency of the approach it extends
Automatically selecting patients for clinical trials with justifications
Clinical trials are human research studies that are used to evaluate the effectiveness
of a surgical, medical, or behavioral intervention. They have been widely used by researchers
to determine whether a new treatment, such as a new medication, is safe and
effective in humans. A clinical trial is frequently performed to determine whether a new
treatment is more successful than the current treatment or has less harmful side effects.
However, clinical trials have a high failure rate. One method applied is to find patients
based on patient records. Unfortunately, this is a difficult process. This is because this
process is typically performed manually, making it time-consuming and error-prone.
Consequently, clinical trial deadlines are often missed, and studies do not move forward.
Time can be a determining factor for success. Therefore, it would be advantageous to have
automatic support in this process. Since it is also important to be able to validate whether
the patients were selected correctly for the trial, avoiding eventual health problems, it
would be important to have a mechanism to present justifications for the selected patients.
In this dissertation, we present one possible solution to solve the problem of patient
selection for clinical trials. We developed the necessary algorithms and created a simple
and intuitive web application that features the selection of patients for clinical trials automatically.
This was achieved by combining knowledge expressed in different formalisms.
We integrated medical knowledge using ontologies, with criteria that were expressed
using nonmonotonic rules. To address the validation procedure automatically, we developed
a mechanism that generates the justifications for each selection together with the
results of the patients who were selected.
In the end, it is expected that a user can easily enter a set of trial criteria, and the
application will generate the results of the selected patients and their respective justifications,
based on the criteria inserted, medical information and a database of patient
information.Os ensaios clínicos são estudos de pesquisa em humanos, utilizados para avaliar a
eficácia de uma intervenção cirúrgica, médica ou comportamental. Estes estudos, têm
sido amplamente utilizados pelos investigadores para determinar se um novo tratamento,
como é o caso de um novo medicamento, é seguro e eficaz em humanos. Um ensaio clínico
é realizado frequentemente, para determinar se um novo tratamento tem mais sucesso
do que o tratamento atual ou se tem menos efeitos colaterais prejudiciais.
No entanto, os ensaios clínicos têm uma taxa de insucesso alta. Um método aplicado
é encontrar pacientes com base em registos. Infelizmente, este é um processo difícil.
Isto deve-se ao facto deste processo ser normalmente realizado à mão, o que o torna
demorado e propenso a erros. Consequentemente, o prazo dos ensaios clínicos é muitas
vezes ultrapassado e os estudos acabam por não avançar. O tempo pode ser por vezes um
fator determinante para o sucesso. Seria então vantajoso ter algum apoio automático neste
processo. Visto que também seria importante validar se os pacientes foram selecionados
corretamente para o ensaio, evitando até eventuais problemas de saúde, seria importante
ter um mecanismo que apresente justificações para os pacientes selecionados.
Nesta dissertação, apresentamos uma possível solução para resolver o problema da
seleção de pacientes para ensaios clínicos, através da criação de uma aplicação web, intuitiva
e fácil de utilizar, que apresenta a seleção de pacientes para ensaios clínicos de
forma automática. Isto foi alcançado através da combinação de conhecimento expresso
em diferentes formalismos. Integrámos o conhecimento médico usando ontologias, com
os critérios que serão expressos usando regras não monotónicas. Para tratar do processo
de validação, desenvolvemos um mecanismo que gera justificações para cada seleção
juntamente com os resultados dos pacientes selecionados.
No final, é esperado que o utilizador consiga inserir facilmente um conjunto de critérios
de seleção, e a aplicação irá gerar os resultados dos pacientes selecionados e as
respetivas justificações, com base nos critérios inseridos, informações médicas e uma base
de dados com informações dos pacientes