Reasoning about Explanations for Negative Query Answers in DL-Lite

In order to meet usability requirements, most logic-based applications provide explanation facilities for reasoning services. This holds also for Description Logics, where research has focused on the explanation of both TBox reasoning and, more recently, query answering. Besides explaining the presence of a tuple in a query answer, it is important to explain also why a given tuple is missing. We address the latter problem for instance and conjunctive query answering over DL-Lite ontologies by adopting abductive reasoning; that is, we look for additions to the ABox that force a given tuple to be in the result. As reasoning tasks we consider existence and recognition of an explanation, and relevance and necessity of a given assertion for an explanation. We characterize the computational complexity of these problems for arbitrary, subset minimal, and cardinality minimal explanations

Towards Understanding Reasoning Complexity in Practice

Although the computational complexity of the logic underlying the standard OWL 2 for the Web Ontology Language (OWL) appears discouraging for real applications, several contributions have shown that reasoning with OWL ontologies is feasible in practice. It turns out that reasoning in practice is often far less complex than is suggested by the established theoretical complexity bound, which reflects the worstcase scenario. State-of-the reasoners like FACT++, HERMIT, PELLET and RACER have demonstrated that, even with fairly expressive fragments of OWL 2, acceptable performances can be achieved. However, it is still not well understood why reasoning is feasible in practice and it is rather unclear how to study this problem. In this paper, we suggest first steps that in our opinion could lead to a better understanding of practical complexity. We also provide and discuss some initial empirical results with HERMIT on prominent ontologie

Explaining Trained Neural Networks with Semantic Web Technologies: First Steps

The ever increasing prevalence of publicly available structured data on the World Wide Web enables new applications in a variety of domains. In this paper, we provide a conceptual approach that leverages such data in order to explain the input-output behavior of trained artificial neural networks. We apply existing Semantic Web technologies in order to provide an experimental proof of concept

Remarks on logic for process descriptions in ontological reasoning: A Drug Interaction Ontology case study

We present some ideas on logical process descriptions, using relations from the DIO (Drug Interaction Ontology) as examples and explaining how these relations can be naturally decomposed in terms of more basic structured logical process descriptions using terms from linear logic. In our view, the process descriptions are able to clarify the usual relational descriptions of DIO. In particular, we discuss the use of logical process descriptions in proving linear logical theorems. Among the types of reasoning supported by DIO one can distinguish both (1) basic reasoning about general structures in reality and (2) the domain-specific reasoning of experts. We here propose a clarification of this important distinction between (realist) reasoning on the basis of an ontology and rule-based inferences on the basis of an expert’s view

Value creation and change in social structures: the role of entrepreneurial innovation from an emergence perspective

Aim: Our aim is to develop a more complete understanding of how processes that entrepreneurs perform interact with wider society and the causal effects of society on entrepreneurial behaviour and vice versa. We aim to show how entrepreneurial agency is put into effect in relation to the disruption of social structure and social change. This has implications for innovation and entrepreneurship policy and practice, and for entrepreneurship theory. We also investigate the role of ‘value’ in these processes. Contribution to the literature Our central argument is that emergent forms (or ‘emergents’) may be short lived (ephemeral) but have causal power on the performance of the actors in the system of inter-relationships in the innovation ecosystem. The emphasis on inter-related social processes and ontological stratification provides theoretical development of extant entrepreneurship theory on new venture creation (by explaining process), effectuation (by linking individualism and holism) and opportunity recognition (by deconstructing opportunity into anticipation, ontology and process). Methodology The paper takes an 'emergence' perspective as a way to understand entrepreneurial processes that give rise to innovation. The anticipation of value and the inter-relationship with social and organisational structures are fundamental to this perspective. A longitudinal analysis of a case study of the development of a new business model within an entrepreneurial firm is described. The case is followed through seven phases in which the relationship between process and emergent ontological status is shown to have destabilising and stabilising effects which produce emergent properties. Results and Implications One methodological contribution is framing how to conceptualise the empirical evidence. Emergents have causal effects on the anticipations of value inherent in their particular system of innovation. This causality is manifest as the attraction of resource in the firm; the stabilisation of the emergent constitutes strategy in the enterprise. A key role of the entrepreneurs in our case study was the creation and maintenance of evolving ontological materiality, as meaningful to themselves and to those with whom they interacted. In simple terms, they made things meaningful to people who mattered

Using Description Logics for Recognising Textual Entailment

The aim of this paper is to show how we can handle the Recognising Textual Entailment (RTE) task by using Description Logics (DLs). To do this, we propose a representation of natural language semantics in DLs inspired by existing representations in first-order logic. But our most significant contribution is the definition of two novel inference tasks: A-Box saturation and subgraph detection which are crucial for our approach to RTE