13,011 research outputs found
Two Approaches to Ontology Aggregation Based on Axiom Weakening
Axiom weakening is a novel technique that allows
for fine-grained repair of inconsistent ontologies.
In a multi-agent setting, integrating ontologies corresponding
to multiple agents may lead to inconsistencies.
Such inconsistencies can be resolved after
the integrated ontology has been built, or their
generation can be prevented during ontology generation.
We implement and compare these two approaches.
First, we study how to repair an inconsistent
ontology resulting from a voting-based aggregation
of views of heterogeneous agents. Second,
we prevent the generation of inconsistencies by letting
the agents engage in a turn-based rational protocol
about the axioms to be added to the integrated
ontology. We instantiate the two approaches using
real-world ontologies and compare them by measuring
the levels of satisfaction of the agents w.r.t.
the ontology obtained by the two procedures
Cognitive context and arguments from ontologies for learning
The deployment of learning resources on the web by different experts has resulted in the accessibility of multiple viewpoints about the same topics. In
this work we assume that learning resources are underpinned by ontologies. Different formalizations of domains may result from different contexts, different use of
terminology, incomplete knowledge or conflicting knowledge. We define the notion of cognitive learning context which describes the cognitive context of an agent who refers to multiple and possibly inconsistent ontologies to determine the truth of a proposition. In particular we describe the cognitive states of ambiguity and inconsistency
resulting from incomplete and conflicting ontologies respectively. Conflicts between ontologies can be identified through the derivation of conflicting arguments
about a particular point of view. Arguments can be used to detect inconsistencies between ontologies. They can also be used in a dialogue between a human learner and a software tutor in order to enable the learner to justify her views and detect inconsistencies between her beliefs and the tutor’s own. Two types of arguments are discussed, namely: arguments inferred directly from taxonomic relations
between concepts, and arguments about the necessary an
MIREOT: the Minimum Information to Reference an External Ontology Term
While the Web Ontology Language (OWL) provides a mechanism to import ontologies, this mechanism is not always suitable. First, given the current state of editing tools and the issues they have working with large ontologies, direct OWL imports have sometimes proven impractical for day-to-day development. Second, ontologies chosen for integration may be under active development and not aligned with the chosen design principles. Importing heterogeneous ontologies in their entirety may lead to inconsistencies or unintended inferences. In this paper we propose a set of guidelines for importing required terms from an external resource into a target ontology. We describe the guidelines, their implementation, present some examples of application, and outline future work and extensions
RaDON - Repair and Diagnosis in Ontology Networks
One of the major challenges in managing networked and dynamic ontologies is to handle inconsistencies in single ontologies, and inconsistencies introduced by integrating multiple distributed ontologies. Our RaDON system provides functionalities to repair and diagnose ontology networks by extending the capabilities of existing reasoners. The system integrates several new debugging and repairing algorithms, such as a relevance-directed algorithm to meet the various needs of the users
Repairing Ontologies via Axiom Weakening.
Ontology engineering is a hard and error-prone task, in which
small changes may lead to errors, or even produce an inconsistent
ontology. As ontologies grow in size, the need for automated
methods for repairing inconsistencies while preserving
as much of the original knowledge as possible increases.
Most previous approaches to this task are based on removing
a few axioms from the ontology to regain consistency.
We propose a new method based on weakening these axioms
to make them less restrictive, employing the use of refinement
operators. We introduce the theoretical framework for
weakening DL ontologies, propose algorithms to repair ontologies
based on the framework, and provide an analysis of
the computational complexity. Through an empirical analysis
made over real-life ontologies, we show that our approach
preserves significantly more of the original knowledge of the
ontology than removing axioms
Survey-based naming conventions for use in OBO Foundry ontology development
A wide variety of ontologies relevant to the biological and medical domains are
available through the OBO Foundry portal, and their number is growing rapidly. Integration of these ontologies, while requiring considerable effort, is extremely desirable. However, heterogeneities in format and style pose serious obstacles to such integration. In particular, inconsistencies in naming conventions can impair the readability and navigability of ontology class hierarchies, and hinder their alignment and integration. While other sources of diversity are tremendously complex and challenging, agreeing a set of common naming conventions is an achievable goal, particularly if those conventions are based on lessons drawn from pooled practical
experience and surveys of community opinion. We summarize a review of existing naming conventions and highlight certain disadvantages with respect to general applicability in the biological domain. We also present the results of a survey carried out to establish which naming conventions are currently employed by OBO Foundry ontologies and to determine what their special requirements regarding the naming
of entities might be. Lastly, we propose an initial set of typographic, syntactic and semantic conventions for labelling classes in OBO Foundry ontologies. Adherence to common naming conventions is more than just a matter of aesthetics. Such conventions provide guidance to ontology creators, help developers avoid flaws and
inaccuracies when editing, and especially when interlinking, ontologies. Common naming conventions will also assist consumers of ontologies to more readily understand what meanings were intended by the authors of ontologies used in annotating bodies of data
Problems and challenges for ontology integration in the SemanticWeb
The Semantic Web is a future vision of the current web in which the resources have exact meaning. The meaning of resources is given by means of ontology definitions. When these ontologies are defined in isolation, the union of two or more ontologies can result in inconsistencies. Resolving such inconsistencies in order to put the ontologies into mutual agreement is known as ontology integration. In this paper, we briefly survey the languages for representing information in the web and the SemanticWeb. We also review some methodologies for performing ontology integration.
Part of our current research is focused into providing alternative representations of current standards for defining ontologies in order to overcome the problems associated with the traditional methods for ontology integration.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI
A Framework for Design and Composition of Semantic Web Services
Semantic Web Services (SWS) are Web Services (WS)
whose description is semantically enhanced with markup
languages (e.g., OWL-S). This semantic description will enable external agents and programs to discover, compose and
invoke SWSs. However, as a previous step to the specification of SWSs in a language, it must be designed at a conceptual level to guarantee its correctness and avoid
inconsistencies among its internal components. In this
paper, we present a framework for design and (semi)
automatic composition of SWSs at a language-independent
and knowledge level. This framework is based on a stack of
ontologies that (1) describe the different parts of a SWS;
and (2) contain a set of axioms that are really design rules to be verified by the ontology instances. Based on these ontologies, design and composition of SWSs can be viewed as the correct instantiation of the ontologies themselves. Once these instances have been created they will be exported to SWS languages such as OWL-S
Catalogue of Anti-Patterns for formal Ontology debugging
Debugging of inconsistent OWL ontologies is normally a tedious and time-consuming task where a combination of ontology engineers and domain expert is often required to understand whether the changes to be performed in order to make the OWL ontology consistent are actually changing the intended meaning of the original knowledge model. This task is aided by existing ontology debugging systems, incorporated in existing reasoners and ontology engineering tools, which ameliorate this problem but in complex cases are still far from providing adequate support to ontology engineers, due to lack of efficiency or lack of precision in determining the main causes for inconsistencies. In this paper we describe a set of anti-patterns commonly found in OWL ontologies, which can be useful in the task of ontology debugging in combination with those debugging tools
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