Scalable integration of uncertainty reasoning and semantic web technologies

In recent years formal logical standards for knowledge representation to model real world knowledge and domains and make them accessible for computers gained a lot of trac- tion. They provide an expressive logical framework for modeling, consistency checking, reasoning, and query answering, and have proven to be versatile methods to capture knowledge of various fields. Those formalisms and methods focus on specifying knowl- edge as precisely as possible. At the same time, many applications in particular on the Semantic Web have to deal with uncertainty in their data; and handling uncertain knowledge is crucial in many real- world domains. However, regular logic is unable to capture the real-world properly due to its inherent complexity and uncertainty, all the while handling uncertain or incomplete information is getting more and more important in applications like expert system, data integration or information extraction. The overall objective of this dissertation is to identify scenarios and datasets where methods that incorporate their inherent uncertainty improve results, and investigate approaches and tools that are suitable for the respective task. In summary, this work is set out to tackle the following objectives: 1. debugging uncertain knowledge bases in order to generate consistent knowledge graphs to make them accessible for logical reasoning, 2. combining probabilistic query answering and logical reasoning which in turn uses these consistent knowledge graphs to answer user queries, and 3. employing the aforementioned techniques to the problem of risk management in IT infrastructures, as a concrete real-world application. We show that in all those scenarios, users can benefit from incorporating uncertainty in the knowledge base. Furthermore, we conduct experiments that demonstrate the real- world scalability of the demonstrated approaches. Overall, we argue that integrating uncertainty and logical reasoning, despite being theoretically intractable, is feasible in real-world application and warrants further research

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Dagstuhl News January - December 2011

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

Proceedings of the ECMLPKDD 2015 Doctoral Consortium

ECMLPKDD 2015 Doctoral Consortium was organized for the second time as part of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), organised in Porto during September 7-11, 2015. The objective of the doctoral consortium is to provide an environment for students to exchange their ideas and experiences with peers in an interactive atmosphere and to get constructive feedback from senior researchers in machine learning, data mining, and related areas. These proceedings collect together and document all the contributions of the ECMLPKDD 2015 Doctoral Consortium