Collaborating Authors

Merging of Ontologies Through Merging of Their Rules Artificial Intelligence

Ontology merging is important, but not always effective. The main reason, why ontology merging is not effective, is that ontology merging is perform ed without considering goals. Goals define the way, in which ontologies to be merg ed more effectively. The paper illustrates ontology m erging by means of rules, which are generate d from these ontologies. This is necessary for further use in expert systems.


AI Magazine

These tools can be used for a great range of activities in the ontology development process, such as ontology design, browsing and implementation, ontology importation, ontology merge and alignment, and ontology-based resource annotation. The middle tier is based on an application server, which makes it easy to create and add new services. A wide range of services for importing and exporting ontologies are available, as we describe in the next section, and some other services rely on them. WAB) to edit formal axioms and rules. Bibliographic references, synonyms, and acronyms can be attached to any of the aforementioned ontology components.

On Dealing with Conflicting, Uncertain and Partially Ordered Ontologies

AAAI Conferences

We focus on handling conflicting and uncertain information in lightweight ontologies, where uncertainty is represented in a possibilistic logic setting. We use DL-Lite, a tractable fragment of Description Logic, to specify terminological knowledge (i.e., TBox). We assume the TBox to be stable and coherent, while its combination with a set of assertional facts (i.e., ABox) may be inconsistent. We address the problem of dealing with conflicts when the reliability relation between sources is only partially ordered. We propose to represent the uncertain ABox as a symbolic weighted base, where a strict partial preorder is applied on the weights. In this context, we provide a strategy for computing a single repair for the ABox, called the partial possibilistic repair. The idea is to consider all compatible bases of a partially preordered ABox (which intuitively encode total extensions of the partial preorder), compute their associated possibilistic repairs, before intersecting those repairs. We define the notion of π-accepted assertions and provide an equivalent characterization, therefore ensuring tractable computations of our method.

Datalog - Ontology Consolidation

Journal of Artificial Intelligence Research

Knowledge bases in the form of ontologies are receiving increasing attention as they allow to clearly represent both the available knowledge, which includes the knowledge in itself and the constraints imposed to it by the domain or the users. In particular, Datalog ontologies are attractive because of their property of decidability and the possibility of dealing with the massive amounts of data in real world environments; however, as it is the case with many other ontological languages, their application in collaborative environments often lead to inconsistency related issues. In this paper we introduce the notion of incoherence regarding Datalog ontologies, in terms of satisfiability of sets of constraints, and show how under specific conditions incoherence leads to inconsistent Datalog ontologies. The main contribution of this work is a novel approach to restore both consistency and coherence in Datalog ontologies. The proposed approach is based on kernel contraction and restoration is performed by the application of incision functions that select formulas to delete. Nevertheless, instead of working over minimal incoherent/inconsistent sets encountered in the ontologies, our operators produce incisions over non-minimal structures called clusters. We present a construction for consolidation operators, along with the properties expected to be satisfied by them. Finally, we establish the relation between the construction and the properties by means of a representation theorem. Although this proposal is presented for Datalog ontologies consolidation, these operators can be applied to other types of ontological languages, such as Description Logics, making them apt to be used in collaborative environments like the Semantic Web.

WEBODE in a Nutshell

AI Magazine

WEBODE is a scalable workbench for ontological engineering that eases the design, development, and management of ontologies and includes middleware services to aid in the integration of ontologies into real-world applications. WEBODE presents a framework to integrate new ontology-based tools and services, where developers only worry about the new logic they want to provide on top of the knowledge stored in their ontologies.