Design and Evaluation of Algorithms for Parallel Classification of Ontologies

Description Logics are a family of knowledge representation formalisms with formal semantics. In recent years, DLs have influenced the design and standardization of the Web Ontology Language OWL. The acceptance of OWL as a web standard has promoted the widespread utilization of DL ontologies on the web. One of the most frequently used inference services of description logic reasoners classifies all named classes of OWL ontologies into a subsumption hierarchy. Due to emerging OWL ontologies from the web community consisting of up to hundreds of thousand of named classes and the increasing availability of multi-processor and multi- or many-core computers, the need for parallelizing description logic inference services to achieve a better scalability is expected. The contribution of this thesis has two aspects. On a theoretical level, it first presents algorithms to construct a TBox in parallel, which are independent of a particular DL logic, however they sacrifice completeness. Then, a sound and complete algorithm for TBox classification in parallel is presented. In this algorithm all the subsumption relationships between concepts of a partition assigned to a single thread are found correctly, in other words, correctness of the TBox subsumption hierarchy is guaranteed. Thereafter, we provide an extension of the sound and complete algorithm which is used to handle TBox classification concurrently and more efficiently. This thesis also describes an optimization technique suitable for better partitioning the list of concepts to be inserted into the TBox. On a practical level, a running prototype, Parallel TBox Classifier was implemented for each generation of the classifier based on the above theoretical foundations, respectively. The Parallel TBox Classifier is used to evaluate the practical merit of the proposed algorithms as well as the effectiveness of the designed optimizations against existing state-of-the-art benchmarks. The empirical results illustrate that Parallel TBox Classifier outperforms the Sequential TBox Classifier on real world ontologies with a linear or superlinear speedup factor. Parallel TBox Classifier can form a basis to develop more efficient parallel classification techniques for real world ontologies with different sizes and DL complexities

Optimizing the computation of overriding

We introduce optimization techniques for reasoning in DLN---a recently introduced family of nonmonotonic description logics whose characterizing features appear well-suited to model the applicative examples naturally arising in biomedical domains and semantic web access control policies. Such optimizations are validated experimentally on large KBs with more than 30K axioms. Speedups exceed 1 order of magnitude. For the first time, response times compatible with real-time reasoning are obtained with nonmonotonic KBs of this size

A Classification Approach for Automated Reasoning Systems--A Case Study in Graph Theory

Reasoning systems which create classifications of structured objects face the problem of how object descriptions can be used to reflect their components as well as relations among these components. Current reasoning systems on graph theory do not adequately provide models to discover complex relations among mathematical concepts (eg: relations involving subgraphs) mainly due to the inability to solve this problem. This thesis presents an approach to construct a knowledge-based system, GC (Graph Classification), which overcomes this difficulty in performing automated reasoning in graph theory. We describe graph concepts based on an attribute called Linear Recursive Constructivity (LRC). LRC defines classes by an algebraic formula supported by background knowledge of graph types. We use subsumption checking on decomposed algebraic expressions of graph classes as a major proof method. The search is guided by case-split-based inferencing. Using the approach GC has generated proofs for many theorems such as any two distinct cycles (closed paths) having a common edge e contain a cycle not traversing e , if cycle C1 contains edges e1, e2, and cycle C2 contains edges e2, e3, then there exists a cycle that contains e1 and e3 and the union of a tree and a path is a tree if they have only a single common vertex. The main contributions of this thesis are: (1) Development of a classification-based knowledge representation and a reasoning approach for graph concepts, thus providing a simple model for structured mathematical objects. (2) Development of an algebraic theory for simplifying and decomposing graph concepts. (3) Development of a proof search and a case-splitting technique with the guidance of graph type knowledge. (4) Development of a proving mechanism that can be generate constructive proofs by manipulating only simple linear formalization of theorems

Behavior Flexibility for Autonomous Unmanned Aerial Systems

Autonomous unmanned aerial systems (UAS) could supplement and eventually subsume a substantial portion of the mission set currently executed by remote pilots, making UAS more robust, responsive, and numerous than permitted by teleoperation alone. Unfortunately, the development of robust autonomous systems is difficult, costly, and time-consuming. Furthermore, the resulting systems often make little reuse of proven software components and offer limited adaptability for new tasks. This work presents a development platform for UAS which promotes behavioral flexibility. The platform incorporates the Unified Behavior Framework (a modular, extensible autonomy framework), the Robotic Operating System (a RSF), and PX4 (an open- source flight controller). Simulation of UBF agents identify a combination of reactive robotic control strategies effective for small-scale navigation tasks by a UAS in the presence of obstacles. Finally, flight tests provide a partial validation of the simulated results. The development platform presented in this work offers robust and responsive behavioral flexibility for UAS agents in simulation and reality. This work lays the foundation for further development of a unified autonomous UAS platform supporting advanced planning algorithms and inter-agent communication by providing a behavior-flexible framework in which to implement, execute, extend, and reuse behaviors

AFRANCI : multi-layer architecture for cognitive agents

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Tractable approximate deduction for OWL

Acknowledgements This work has been partially supported by the European project Marrying Ontologies and Software Technologies (EU ICT2008-216691), the European project Knowledge Driven Data Exploitation (EU FP7/IAPP2011-286348), the UK EPSRC project WhatIf (EP/J014354/1). The authors thank Prof. Ian Horrocks and Dr. Giorgos Stoilos for their helpful discussion on role subsumptions. The authors thank Rafael S. Gonçalves et al. for providing their hotspots ontologies. The authors also thank BoC-group for providing their ADOxx Metamodelling ontologies.Peer reviewedPostprin

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