Reasoning with Inconsistencies in Hybrid MKNF Knowledge Bases

This article is concerned with the handling of inconsistencies occurring in the combination of description logics and rules, especially in hybrid MKNF knowledge bases. More precisely, we present a paraconsistent semantics for hybrid MKNF knowledge bases (called para-MKNF knowledge bases) based on four-valued logic as proposed by Belnap. We also reduce this paraconsistent semantics to the stable model semantics via a linear transformation operator, which shows the relationship between the two semantics and indicates that the data complexity in our paradigm is not higher than that of classical reasoning. Moreover, we provide fixpoint operators to compute paraconsistent MKNF models, each suitable to different kinds of rules. At last we present the data complexity of instance checking in different para-MKNF knowledge bases

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

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

Towards Closed World Reasoning in Dynamic Open Worlds (Extended Version)

The need for integration of ontologies with nonmonotonic rules has been gaining importance in a number of areas, such as the Semantic Web. A number of researchers addressed this problem by proposing a unified semantics for hybrid knowledge bases composed of both an ontology (expressed in a fragment of first-order logic) and nonmonotonic rules. These semantics have matured over the years, but only provide solutions for the static case when knowledge does not need to evolve. In this paper we take a first step towards addressing the dynamics of hybrid knowledge bases. We focus on knowledge updates and, considering the state of the art of belief update, ontology update and rule update, we show that current solutions are only partial and difficult to combine. Then we extend the existing work on ABox updates with rules, provide a semantics for such evolving hybrid knowledge bases and study its basic properties. To the best of our knowledge, this is the first time that an update operator is proposed for hybrid knowledge bases.Comment: 40 pages; an extended version of the article published in Theory and Practice of Logic Programming, 10 (4-6): 547 - 564, July. Copyright 2010 Cambridge University Pres

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

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