Formal Concept Analysis Methods for Description Logics

This work presents mainly two contributions to Description Logics (DLs) research by means of Formal Concept Analysis (FCA) methods: supporting bottom-up construction of DL knowledge bases, and completing DL knowledge bases. Its contribution to FCA research is on the computational complexity of computing generators of closed sets

Reasoning-Supported Quality Assurance for Knowledge Bases

The increasing application of ontology reuse and automated knowledge acquisition tools in ontology engineering brings about a shift of development efforts from knowledge modeling towards quality assurance. Despite the high practical importance, there has been a substantial lack of support for ensuring semantic accuracy and conciseness. In this thesis, we make a significant step forward in ontology engineering by developing a support for two such essential quality assurance activities

Learning Description Logic Knowledge Bases from Data Using Methods from Formal Concept Analysis

Description Logics (DLs) are a class of knowledge representation formalisms that can represent terminological and assertional knowledge using a well-defined semantics. Often, knowledge engineers are experts in their own fields, but not in logics, and require assistance in the process of ontology design. This thesis presents three methods that can extract terminological knowledge from existing data and thereby assist in the design process. They are based on similar formalisms from Formal Concept Analysis (FCA), in particular the Next-Closure Algorithm and Attribute-Exploration. The first of the three methods computes terminological knowledge from the data, without any expert interaction. The two other methods use expert interaction where a human expert can confirm each terminological axiom or refute it by providing a counterexample. These two methods differ only in the way counterexamples are provided

Generating Armstrong ABoxes for ALC TBoxes

A challenge in ontology engineering is the mismatch in expertise between the ontology engineer and domain expert, which often leads to important constraints not being specified. Domain experts often only focus on specifying constraints that should hold and not on specifying constraints that could possibly be violated. In an attempt to bridge this gap we propose the use of “perfect test data”. The generated test data is perfect in that it satisfies all the constraints of an application domain that are required, including ensuring that the test data violates constraints that can be violated. In the context of Description Logic ontologies we call this test data an “Armstrong ABox”, a notion derived from Armstrong relations in relational database theory. In this paper we detail the theoretical development of Armstrong ABoxes for ALC TBoxes as well as an algorithm for generating such Armstrong ABoxes. The proposed algorithm is based, via the ontology completion algorithm of Baader et al., on attribute exploration in formal concept analysis.http://link.springer.combookseries/5582019-11-03hj2018Informatic