Hybrid Rules with Well-Founded Semantics

A general framework is proposed for integration of rules and external first order theories. It is based on the well-founded semantics of normal logic programs and inspired by ideas of Constraint Logic Programming (CLP) and constructive negation for logic programs. Hybrid rules are normal clauses extended with constraints in the bodies; constraints are certain formulae in the language of the external theory. A hybrid program is a pair of a set of hybrid rules and an external theory. Instances of the framework are obtained by specifying the class of external theories, and the class of constraints. An example instance is integration of (non-disjunctive) Datalog with ontologies formalized as description logics. The paper defines a declarative semantics of hybrid programs and a goal-driven formal operational semantics. The latter can be seen as a generalization of SLS-resolution. It provides a basis for hybrid implementations combining Prolog with constraint solvers. Soundness of the operational semantics is proven. Sufficient conditions for decidability of the declarative semantics, and for completeness of the operational semantics are given

Argumentation for machine learning: a survey

Existing approaches using argumentation to aid or improve machine learning differ in the type of machine learning technique they consider, in their use of argumentation and in their choice of argumentation framework and semantics. This paper presents a survey of this relatively young field highlighting, in particular, its achievements to date, the applications it has been used for as well as the benefits brought about by the use of argumentation, with an eye towards its future

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Neuro-symbolic Computation for XAI: Towards a Unified Model

The idea of integrating symbolic and sub-symbolic approaches to make intelligent systems (IS) understandable and explainable is at the core of new fields such as neuro-symbolic computing (NSC). This work lays under the umbrella of NSC, and aims at a twofold objective. First, we present a set of guidelines aimed at building explainable IS, which leverage on logic induction and constraints to integrate symbolic and sub-symbolic approaches. Then, we reify the proposed guidelines into a case study to show their effectiveness and potential, presenting a prototype built on the top of some NSC technologies

Doctor of Philosophy

dissertationGene expression data repositories provide large and ever increasing data for secondary use by translational informatics methods. For example, Gene Expression Omnibus (GEO) houses over 37,000 experiments with the goal of supporting further research. To use these published results in a larger meta-analysis, consolidation of the data are needed; however, the data are largely unstructured, thus hindering data integration efforts. Here, I propose the use of a novel pipeline, Ontology Based Data Integration (OBDI), which uses an ontological approach to combine the samples across multiple GEO experiments. The ODBI pipeline uses machine learning algorithms that permit researchers to consolidate and analyze data across GEO experiments. Here, I demonstrate how using an ontological approach to integrate samples across experiments can be used to explore the immune response at a molecular level. As part of this process, a Web Ontology Language (OWL) was developed for each data platform used. OWL serves as a core component in successfully processing different sample types. Immunological experiments from GEO were consolidated to evaluate this methodology. The experiments included samples analyzed on expression arrays, BeadChips, and sequencing technologies. The integration of a complex biological system and the incorporation of different biological data types will validate the potential of OBDI. iv The nature of biological data is highly dimensional. OBDI incorporates tools and techniques that can handle the analysis of various biological data. The machine learning analysis performed within the OBDI pipeline successfully evaluated the newly annotated experiments and provides insights that can be further explored. The OBDI pipeline can help researchers annotate experiments using ontologies and analyze the annotated experiments. To successfully build the pipeline, ontologies served as the backbone of integrating samples from GEO Series records into machine learning experiments using ML-Flex. By using the OBDI pipeline, researchers can access the uncurated experiments from GEO (GEO Data Series) and annotate the data using the terms in the ontologies. This mechanism allows for the organization of data sets in relationship to new experiments independent of GEO's GDS curation process. The OBDI system allows ontologies to grow organically around a cluster of experiments. These experiments are then further analyzed in ML-Flex using machine learning algorithms. The curated experiments are analyzed in silico and the computational analyses are supported by the OBDI ontological system