Ontology Building Using Parallel Enumerative Structures

National audienceThe semantics of a text is carried by both the natural language it contains and its layout. As ontology building processes have so far taken only plain text into consideration, our aim is to elicit its textual structure. We focus here on parallel enumerative structures because they bear implicit or explicit hierarchical relations, they have salient visual properties, and they are frequently found in corpora. We have defined a process which identifies them in a text, translates them into ontology structures and finally links such structures to the concepts of an existing ontology. We have assessed this process on Wikipedia encyclopaedic articles as they are rich in definitions and statements, and contain many enumerations. The many ontology structures we have obtained are thus used to enrich an ontology which we had automatically built from database specification documents

Correcting pervasive errors in RNA crystallography through enumerative structure prediction

Three-dimensional RNA models fitted into crystallographic density maps exhibit pervasive conformational ambiguities, geometric errors and steric clashes. To address these problems, we present enumerative real-space refinement assisted by electron density under Rosetta (ERRASER), coupled to Python-based hierarchical environment for integrated 'xtallography' (PHENIX) diffraction-based refinement. On 24 data sets, ERRASER automatically corrects the majority of MolProbity-assessed errors, improves the average Rfree factor, resolves functionally important discrepancies in noncanonical structure and refines low-resolution models to better match higher-resolution models

Learning from Experience: A Philosophical Perspective

This work examines philosophical solutions to David Hume’s problem of induction—a skeptical attack on our ability to learn from experience. I explore the logical, ontological, and epistemic difficulties behind the everyday assumption that the future will resemble the past. While historical solutions by philosophers such as Bertrand Russell and Karl Popper have been unsuccessful at tackling these complications, combining recent work on natural kinds and naturalistic epistemology has promise. Ultimately, I expand on work done by Howard Sankey, Hilary Kornblith, and Brian Ellis to create an account of nature and epistemology that explains why objects in nature have predictable behavior. I find Sankey\u27s solution incomplete, but I fix the major I identify and show why the work by Sankey builds into a powerful solution to Hume\u27s problem

Interactive Knowledge Construction in the Collaborative Building of an Encyclopedia

International audienceOne of the major challenges of Applied Artificial Intelligence is to provide environments where high level human activities like learning, constructing theories or performing experiments, are enhanced by Artificial Intelligence technologies. This paper starts with the description of an ambitious project: EnCOrE2. The specific real world EnCOrE scenario, significantly representing a much wider class of potential applicative contexts, is dedicated to the building of an Encyclopedia of Organic Chemistry in the context of Virtual Communities of experts and students. Its description is followed by a brief survey of some major AI questions and propositions in relation with the problems raised by the EnCOrE project. The third part of the paper starts with some definitions of a set of “primitives” for rational actions, and then integrates them in a unified conceptual framework for the interactive construction of knowledge. To end with, we sketch out protocols aimed at guiding both the collaborative construction process and the collaborative learning process in the EnCOrE project.The current major result is the emerging conceptual model supporting interaction between human agents and AI tools integrated in Grid services within a socio-constructivist approach, consisting of cycles of deductions, inductions and abductions upon facts (the shared reality) and concepts (their subjective interpretation) submitted to negotiations, and finally converging to a socially validated consensus

Complexity, BioComplexity, the Connectionist Conjecture and Ontology of Complexity\ud

This paper develops and integrates major ideas and concepts on complexity and biocomplexity - the connectionist conjecture, universal ontology of complexity, irreducible complexity of totality & inherent randomness, perpetual evolution of information, emergence of criticality and equivalence of symmetry & complexity. This paper introduces the Connectionist Conjecture which states that the one and only representation of Totality is the connectionist one i.e. in terms of nodes and edges. This paper also introduces an idea of Universal Ontology of Complexity and develops concepts in that direction. The paper also develops ideas and concepts on the perpetual evolution of information, irreducibility and computability of totality, all in the context of the Connectionist Conjecture. The paper indicates that the control and communication are the prime functionals that are responsible for the symmetry and complexity of complex phenomenon. The paper takes the stand that the phenomenon of life (including its evolution) is probably the nearest to what we can describe with the term “complexity”. The paper also assumes that signaling and communication within the living world and of the living world with the environment creates the connectionist structure of the biocomplexity. With life and its evolution as the substrate, the paper develops ideas towards the ontology of complexity. The paper introduces new complexity theoretic interpretations of fundamental biomolecular parameters. The paper also develops ideas on the methodology to determine the complexity of “true” complex phenomena.\u

" Quand rédiger c'est décrire " : Mise en forme matérielle des textes et construction d'ontologies à partir de textes

National audienceLa construction d'ontologie à partir de textes met classiquement en oeuvre des outils issus du Traitement Automatique de la Langue et/ou des outils d'apprentissage supervisé ou non. Dans cet article nous revenons sur la possibilité d'exploiter des objets textuels à la fois facilement identifiables, souvent fertiles en connaissances ontologiques, et dont la sémantique peut clairement être explicitée par les théories du discours : les structures énumératives. Ici, nous ajoutons une nouvelle classe de relations sémantiques portée par les structures énumératives très présentes dans nos corpus : les relations lexicales telles que l'homonymie ou la synonymie. Ces relations semblent propices pour alimenter la facette terminologique d'une Ressource Termino-Ontologique. Nous montrons que ces relations peuvent être formellement caractérisées. Une évaluation de notre approche à partir d'un corpus annoté manuellement nous permet de valider notre position, ce qui constitue une première étape vers un outil d'apprentissage supervisé pour la construction d'ontologie à partir de texte