A Lightweight Defeasible Description Logic in Depth: Quantification in Rational Reasoning and Beyond

Description Logics (DLs) are increasingly successful knowledge representation formalisms, useful for any application requiring implicit derivation of knowledge from explicitly known facts. A prominent example domain benefiting from these formalisms since the 1990s is the biomedical field. This area contributes an intangible amount of facts and relations between low- and high-level concepts such as the constitution of cells or interactions between studied illnesses, their symptoms and remedies. DLs are well-suited for handling large formal knowledge repositories and computing inferable coherences throughout such data, relying on their well-founded first-order semantics. In particular, DLs of reduced expressivity have proven a tremendous worth for handling large ontologies due to their computational tractability. In spite of these assets and prevailing influence, classical DLs are not well-suited to adequately model some of the most intuitive forms of reasoning. The capability for abductive reasoning is imperative for any field subjected to incomplete knowledge and the motivation to complete it with typical expectations. When such default expectations receive contradicting evidence, an abductive formalism is able to retract previously drawn, conflicting conclusions. Common examples often include human reasoning or a default characterisation of properties in biology, such as the normal arrangement of organs in the human body. Treatment of such defeasible knowledge must be aware of exceptional cases - such as a human suffering from the congenital condition situs inversus - and therefore accommodate for the ability to retract defeasible conclusions in a non-monotonic fashion. Specifically tailored non-monotonic semantics have been continuously investigated for DLs in the past 30 years. A particularly promising approach, is rooted in the research by Kraus, Lehmann and Magidor for preferential (propositional) logics and Rational Closure (RC). The biggest advantages of RC are its well-behaviour in terms of formal inference postulates and the efficient computation of defeasible entailments, by relying on a tractable reduction to classical reasoning in the underlying formalism. A major contribution of this work is a reorganisation of the core of this reasoning method, into an abstract framework formalisation. This framework is then easily instantiated to provide the reduction method for RC in DLs as well as more advanced closure operators, such as Relevant or Lexicographic Closure. In spite of their practical aptitude, we discovered that all reduction approaches fail to provide any defeasible conclusions for elements that only occur in the relational neighbourhood of the inspected elements. More explicitly, a distinguishing advantage of DLs over propositional logic is the capability to model binary relations and describe aspects of a related concept in terms of existential and universal quantification. Previous approaches to RC (and more advanced closures) are not able to derive typical behaviour for the concepts that occur within such quantification. The main contribution of this work is to introduce stronger semantics for the lightweight DL EL_bot with the capability to infer the expected entailments, while maintaining a close relation to the reduction method. We achieve this by introducing a new kind of first-order interpretation that allocates defeasible information on its elements directly. This allows to compare the level of typicality of such interpretations in terms of defeasible information satisfied at elements in the relational neighbourhood. A typicality preference relation then provides the means to single out those sets of models with maximal typicality. Based on this notion, we introduce two types of nested rational semantics, a sceptical and a selective variant, each capable of deriving the missing entailments under RC for arbitrarily nested quantified concepts. As a proof of versatility for our new semantics, we also show that the stronger Relevant Closure, can be imbued with typical information in the successors of binary relations. An extensive investigation into the computational complexity of our new semantics shows that the sceptical nested variant comes at considerable additional effort, while the selective semantics reside in the complexity of classical reasoning in the underlying DL, which remains tractable in our case

Closing Information Gaps with Need-driven Knowledge Sharing

Informationslücken schließen durch bedarfsgetriebenen Wissensaustausch Systeme zum asynchronen Wissensaustausch – wie Intranets, Wikis oder Dateiserver – leiden häufig unter mangelnden Nutzerbeiträgen. Ein Hauptgrund dafür ist, dass Informationsanbieter von Informationsuchenden entkoppelt, und deshalb nur wenig über deren Informationsbedarf gewahr sind. Zentrale Fragen des Wissensmanagements sind daher, welches Wissen besonders wertvoll ist und mit welchen Mitteln Wissensträger dazu motiviert werden können, es zu teilen. Diese Arbeit entwirft dazu den Ansatz des bedarfsgetriebenen Wissensaustauschs (NKS), der aus drei Elementen besteht. Zunächst werden dabei Indikatoren für den Informationsbedarf erhoben – insbesondere Suchanfragen – über deren Aggregation eine fortlaufende Prognose des organisationalen Informationsbedarfs (OIN) abgeleitet wird. Durch den Abgleich mit vorhandenen Informationen in persönlichen und geteilten Informationsräumen werden daraus organisationale Informationslücken (OIG) ermittelt, die auf fehlende Informationen hindeuten. Diese Lücken werden mit Hilfe so genannter Mediationsdienste und Mediationsräume transparent gemacht. Diese helfen Aufmerksamkeit für organisationale Informationsbedürfnisse zu schaffen und den Wissensaustausch zu steuern. Die konkrete Umsetzung von NKS wird durch drei unterschiedliche Anwendungen illustriert, die allesamt auf bewährten Wissensmanagementsystemen aufbauen. Bei der Inversen Suche handelt es sich um ein Werkzeug das Wissensträgern vorschlägt Dokumente aus ihrem persönlichen Informationsraum zu teilen, um damit organisationale Informationslücken zu schließen. Woogle erweitert herkömmliche Wiki-Systeme um Steuerungsinstrumente zur Erkennung und Priorisierung fehlender Informationen, so dass die Weiterentwicklung der Wiki-Inhalte nachfrageorientiert gestaltet werden kann. Auf ähnliche Weise steuert Semantic Need, eine Erweiterung für Semantic MediaWiki, die Erfassung von strukturierten, semantischen Daten basierend auf Informationsbedarf der in Form strukturierter Anfragen vorliegt. Die Umsetzung und Evaluation der drei Werkzeuge zeigt, dass bedarfsgetriebener Wissensaustausch technisch realisierbar ist und eine wichtige Ergänzung für das Wissensmanagement sein kann. Darüber hinaus bietet das Konzept der Mediationsdienste und Mediationsräume einen Rahmen für die Analyse und Gestaltung von Werkzeugen gemäß der NKS-Prinzipien. Schließlich liefert der hier vorstellte Ansatz auch Impulse für die Weiterentwicklung von Internetdiensten und -Infrastrukturen wie der Wikipedia oder dem Semantic Web

The semantic Web : theories, languages, and applications

La popularité croissante du Web permet la diffusion d’une quantité phénoménale d’information de toutes sortes et l’accès à une panoplie de services en ligne en raison du développement effréné de ses contenus et de leur consultation quotidienne à faible coût. Malheureusement, cette explosion du Web cause un problème de surabondance de données pas toujours crédibles et souvent inutilisables; les réponses obtenues des engins de recherche peuvent être inadéquates ou imprécises et les services en ligne sont exclusifs ou incompatibles entre eux. Dans le but de pallier à ces inconvénients, le consortium W3C a proposé une solution globale, connue sous le nom de Web sémantique, qui améliore les structures de représentation des données de façon à rendre les contenus signifiants et à permettre l’inférence de nouvelles connaissances par des programmes. Ce mémoire explore les théories sous-jacentes au Web sémantique ainsi que les technologies qui lui sont propres. D’une part, les concepts de logique descriptive et de structure ontologique sont présentés et des liens sont établis entre eux. D’autre part, une hiérarchie de langages incluant, entre autres, les langages XML, RDF, DAML+OIL et OWL est introduite ainsi qu’une étude comparative de plusieurs moteurs d’inférence basés sur ces langages. Enfin, ce mémoire présente un exemple complet qui permet d’illustrer les principaux concepts du Web sémantique et d’évaluer la faisabilité de la mise en oeuvre d’une application par rapport à l’état actuel des technologies

Pseudo-contractions as Gentle Repairs

Updating a knowledge base to remove an unwanted consequence is a challenging task. Some of the original sentences must be either deleted or weakened in such a way that the sentence to be removed is no longer entailed by the resulting set. On the other hand, it is desirable that the existing knowledge be preserved as much as possible, minimising the loss of information. Several approaches to this problem can be found in the literature. In particular, when the knowledge is represented by an ontology, two different families of frameworks have been developed in the literature in the past decades with numerous ideas in common but with little interaction between the communities: applications of AGM-like Belief Change and justification-based Ontology Repair. In this paper, we investigate the relationship between pseudo-contraction operations and gentle repairs. Both aim to avoid the complete deletion of sentences when replacing them with weaker versions is enough to prevent the entailment of the unwanted formula. We show the correspondence between concepts on both sides and investigate under which conditions they are equivalent. Furthermore, we propose a unified notation for the two approaches, which might contribute to the integration of the two areas

Tools for enterprises collaboration in virtual enterprises

Virtual Enterprise (VE) is an organizational collaboration concept which provides a competitive edge in the globalized business environment. The life cycle of a VE consists of four stages i.e. opportunity identification (Pre-Creation), partner selection (Creation), operation and dissolution. The success of VEs depends upon the efficient execution of their VE-lifecycles along with knowledge enhancement for the partner enterprises to facilitate the future formation of efficient VEs. This research aims to study the different issues which occur in the VE lifecycle and provides a platform for the formation of high performance enterprises and VEs. In the pre-creation stage, enterprises look for suitable partners to create their VE and to exploit a market opportunity. This phase requires explicit and implicit information extraction from enterprise data bases (ECOS-ontology) for the identification of suitable partners. A description logic (DL) based query system is developed to extract explicit and implicit information and to identify potential partners for the creation of the VE. In the creation phase, the identified partners are analysed using different risks paradigms and a cooperative game theoretic approach is used to develop a revenue sharing mechanism based on enterprises inputs and risk minimization for optimal partner selection. In the operation phases, interoperability remains a key issue for seamless transfer of knowledge information and data. DL-based ontology mapping is applied in this research to provide interoperability in the VE between enterprises with different domains of expertise. In the dissolution stage, knowledge acquired in the VE lifecycle needs to be disseminated among the enterprises to enhance their competitiveness. A DL-based ontology merging approach is provided to accommodate new knowledge with existing data bases with logical consistency. Finally, the proposed methodologies are validated using the case study. The results obtained in the case study illustrate the applicability and effectiveness of proposed methodologies in each stage of the VE life cycle

Aquisição e Interrogação de Conhecimento de Prática Clínica usando Linguagem Natural

The scientific concepts, methodologies and tools in the Knowledge Representation (KR) sub- domain of applied Artificial Intelligence (AI) came a long way with enormous strides in recent years. The usage of domain conceptualizations that are Ontologies is now powerful enough to aim at computable reasoning over complex realities. One of the most challenging scientific and technical human endeavors is the daily Clinical Prac- tice (CP) of Cardiovascular (CV) specialty healthcare providers. Such a complex domain can benefit largely from the possibility of clinical reasoning aids that are now at the edge of being available. We research into a complete end-to-end solid ontological infrastructure for CP knowledge represen- tation as well as the associated processes to automatically acquire knowledge from clinical texts and reason over it

Clinical practice knowledge acquisition and interrogation using natural language: aquisição e interrogação de conhecimento de prática clínica usando linguagem natural

Os conceitos científicos, metodologias e ferramentas no sub-dominio da Representação de Conhecimento da área da Inteligência Artificial Aplicada têm sofrido avanços muito significativos nos anos recentes. A utilização de Ontologias como conceptualizações de domínios é agora suficientemente poderosa para aspirar ao raciocínio computacional sobre realidades complexas. Uma das tarefas científica e tecnicamente mais desafiante é prestação de cuidados pelos profissionais de saúde na especialidade cardiovascular. Um domínio de tal forma complexo pode beneficiar largamente da possibilidade de ajudas ao raciocínio clínico que estão neste momento a beira de ficarem disponíveis. Investigamos no sentido de desenvolver uma infraestrutura sólida e completa para a representação de conhecimento na prática clínica bem como os processes associados para adquirir o conhecimento a partir de textos clínicos e raciocinar automaticamente sobre esse conhecimento; ABSTRACT: The scientific concepts, methodologies and tools in the Knowledge Representation (KR) subdomain of applied Artificial Intelligence (AI) came a long way with enormous strides in recent years. The usage of domain conceptualizations that are Ontologies is now powerful enough to aim at computable reasoning over complex realities. One of the most challenging scientific and technical human endeavors is the daily Clinical Practice (CP) of Cardiovascular (C V) specialty healthcare providers. Such a complex domain can benefit largely from the possibility of clinical reasoning aids that are now at the edge of being available. We research into al complete end-to-end solid ontological infrastructure for CP knowledge representation as well as the associated processes to automatically acquire knowledge from clinical texts and reason over it

A Default-Logic Paradigm for Legal Reasoning and Factfinding

Unlike research in linguistics and artificial intelligence, legal research has not used advances in logical theory very effectively. This article uses default logic to develop a paradigm for analyzing all aspects of legal reasoning, including factfinding. The article provides a formal model that integrates legal rules and policies with the evaluation of both expert and non-expert evidence – whether the reasoning occurs in courts or administrative agencies, and whether in domestic, foreign, or international legal systems. This paradigm can standardize the representation of legal reasoning, guide empirical research into the dynamics of such reasoning, and put the representations and research results to immediate use through artificial intelligence software. This new model therefore has the potential to transform legal practice and legal education, as well as legal theory