Approximate Assertional Reasoning Over Expressive Ontologies

In this thesis, approximate reasoning methods for scalable assertional reasoning are provided whose computational properties can be established in a well-understood way, namely in terms of soundness and completeness, and whose quality can be analyzed in terms of statistical measurements, namely recall and precision. The basic idea of these approximate reasoning methods is to speed up reasoning by trading off the quality of reasoning results against increased speed

Approximate Instance Retrieval on Ontologies

With the development of more expressive description logics (DLs) for the Web Ontology Language OWL the question arises how we can properly deal with the high computational complexity for efficient reasoning. In application cases that require scalable reasoning with expressive ontologies, non-standard reasoning solutions such as approximate reasoning are necessary to tackle the intractability of reasoning in expressive DLs. In this paper, we are concerned with the approximation of the reasoning task of instance retrieval on DL knowledge bases, trading correctness of retrieval results for gain of speed. We introduce our notion of an approximate concept extension and we provide implementations to compute an approximate answer for a concept query by a suitable mapping to efficient database operations. Furthermore, we report on experiments of our approach on instance retrieval with the Wine ontology and discuss first results in terms of error rate and speed-up

Approximate OWL Instance Retrieval with SCREECH

With the increasing interest in expressive ontologies for the Semantic Web, it is critical to develop scalable and efficient ontology reasoning techniques that can properly cope with very high data volumes. For certain application domains, approximate reasoning solutions, which trade soundness or completeness for increased reasoning speed, will help to deal with the high computational complexities which state of the art ontology reasoning tools have to face. In this paper, we present a comprehensive overview of the SCREECH approach to approximate instance retrieval with OWL ontologies, which is based on the KAON2 algorithms, facilitating a compilation of OWL DL TBoxes into Datalog, which is tractable in terms of data complexity. We present three different instantiations of the Screech approach, and report on experiments which show that the gain in efficiency outweighs the number of introduced mistakes in the reasoning process

Initial Framework for Measuring and Evaluating Heuristic Problem Solving

One of the key aspects in the development of LarKC is how to evaluate the performance of the platform and its constituent components in order to guarantee that the execution of a pipeline will match the user’s needs and provide the desired solutions (answers) to the user’s queries. Therefore, in this deliverable, the first in a series three documents concerned with the definition of a Framework for Measuring and Evaluating Heuristic Problem Solving, we make the first steps towards defining such framework by considering the theoretical foundations and principles of evaluation and measurement theory, discussing several important aspects related to the process of evaluating LarKC and its platform and, reporting on several dimensions and methods by which the components of the platform and the platform itself can be evaluated. Our primary interest in the context of this deliverable is a formative evaluation framework that is designed by the members of the LarKC project and aims to identify project strengths and weaknesses, focus managerial and research efforts, and measure progress towards achieving the project goals. Such a framework can also serve as the foundation for a subsequent summative evaluation