Inductive Logic Programming in Databases: from Datalog to DL+log

In this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e. the issue of how ontologies (and semantics conveyed by them) can help solving typical database problems, through a better understanding of KR aspects related to databases. In particular, we investigate this issue from the ILP perspective by considering two database problems, (i) the definition of views and (ii) the definition of constraints, for a database whose schema is represented also by means of an ontology. Both can be reformulated as ILP problems and can benefit from the expressive and deductive power of the KR framework DL+log. We illustrate the application scenarios by means of examples. Keywords: Inductive Logic Programming, Relational Databases, Ontologies, Description Logics, Hybrid Knowledge Representation and Reasoning Systems. Note: To appear in Theory and Practice of Logic Programming (TPLP).Comment: 30 pages, 3 figures, 2 tables

Collaborative Engineering Environments. Two Examples of Process Improvement

Companies are recognising that innovative processes are determining factors in competitiveness. Two examples from projects in aircraft development describe the introduction of collaborative engineering environments as a way to improve engineering processes. A multi-disciplinary simulation environment integrates models from all disciplines involved in a common functional structure. Quick configuration for specific design problems and powerful feedback / visualisation capabilities enable engineering teams to concentrate on the integrated behaviour of the design. An engineering process management system allows engineering teams to work concurrently in tasks, following a defined flow of activities, applying tools on a shared database. Automated management of workspaces including data consistency enables engineering teams to concentrate on the design activities. The huge amount of experience in companies must be transformed for effective application in engineering processes. Compatible concepts, notations and implementation platforms make tangible knowledge like models and algorithms accessible. Computer-based design management makes knowledge on engineering processes and methods explicit

How Gibbs distributions may naturally arise from synaptic adaptation mechanisms. A model-based argumentation

This paper addresses two questions in the context of neuronal networks dynamics, using methods from dynamical systems theory and statistical physics: (i) How to characterize the statistical properties of sequences of action potentials ("spike trains") produced by neuronal networks ? and; (ii) what are the effects of synaptic plasticity on these statistics ? We introduce a framework in which spike trains are associated to a coding of membrane potential trajectories, and actually, constitute a symbolic coding in important explicit examples (the so-called gIF models). On this basis, we use the thermodynamic formalism from ergodic theory to show how Gibbs distributions are natural probability measures to describe the statistics of spike trains, given the empirical averages of prescribed quantities. As a second result, we show that Gibbs distributions naturally arise when considering "slow" synaptic plasticity rules where the characteristic time for synapse adaptation is quite longer than the characteristic time for neurons dynamics.Comment: 39 pages, 3 figure

LANDHOLDER COOPERATION FOR SUSTAINABLE UPLAND WATERSHED MANAGEMENT: A THEORETICAL REVIEW OF THE PROBLEMS AND PROSPECTS

Despite national legislation and substantial donor investments, watershed degradation continues to threaten the sustained economic development and social welfare of millions of citizens in the developing world. Past efforts have largely concentrated on the physical rather than institutional aspects of watersheds, and have often relied on external incentives to coerce or persuade individuals to adopt conservation practices. In contrast to this conventional "physical" perspective, watersheds can be considered as sets of vested interests (and social relations) within a physically defined space. In essence, watersheds are physically defined subsets of rural society. Actors with vested interests within watersheds are interdependent because of water flow across political boundaries. From this perspective, the achievement of watershed management is a question of social relations, and cooperation between individual actors. Though there is growing realization for an expanded role of local, cooperative institutions in watershed management, theories on how such institutions might be identified, evolve or be promoted are limited. Toward this end, this paper examines some of the theoretical aspects of landholder cooperation for watershed management: the socio-political setting of upland watersheds; the physical attributes of watersheds influencing cooperation; the nature of externalities and incentives in watersheds; and the economic and socio-cultural factors affecting the emergence of collective action units. The processes by which collective action groups actually form are also reviewed. The paper concludes with a synthesis of the prospects for landholder cooperation approaches, the appropriate role of policy and a proposed process for promoting such cooperation.Resource /Energy Economics and Policy,

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio

Dependence relationships between Gene Ontology terms based on TIGR gene product annotations

The Gene Ontology is an important tool for the representation and processing of information about gene products and functions. It provides controlled vocabularies for the designations of cellular components, molecular functions, and biological processes used in the annotation of genes and gene products. These constitute three separate ontologies, of cellular components), molecular functions and biological processes, respectively. The question we address here is: how are the terms in these three separate ontologies related to each other? We use statistical methods and formal ontological principles as a first step towards finding answers to this question