@conference {758, title = {Tracking Human Behavioural Consistency by Analysing Periodicity of Household Water Consumption}, booktitle = {2nd International Conference on Sensors, Signal and Image Processing (SSIP 19)}, year = {2019}, publisher = {ACM}, organization = {ACM}, address = {Prague, Czech Republic}, abstract = {

People are living longer than ever due to advances in healthcare, and this has prompted many healthcare providers to look towards remote patient care as a means to meet the needs of the future. It is now a priority to enable people to reside in their own homes rather than in overburdened facilities whenever possible. The increasing maturity of IoT technologies and the falling costs of connected sensors has made the deployment of remote healthcare at scale an increasingly attractive prospect. In this work we demonstrate that we can measure the consistency and regularity of the behaviour of a household using sensor readings generated from interaction with the home environment. We show that we can track changes in this behaviour regularity longitudinally and detect changes that may be related to significant life events or trends that may be medically significant. We achieve this using periodicity analysis on water usage readings sampled from the main household water meter every 15 minutes for over 8 months. We utilise an IoT Application Enablement Platform in conjunction with low cost LoRa-enabled sensors and a Low Power Wide Area Network in order to validate a data collection methodology that could be deployed at large scale in future. We envision the statistical methods described here being applied to data streams from the homes of elderly and at-risk groups, both as a means of\  early illness\  detection\  and\  for\  monitoring\  the well-being of those with known illnesses.

}, keywords = {Ambient Assisted Living, Home Monitoring, Internet of Things, Sensor Applications, Sensor Networks}, author = {Quinn, Se{\'a}n and Murphy, Noel and Smeaton, Alan F.} } @conference {748, title = {Pseudo-Random ALC Syntax Generation}, booktitle = {The Semantic Web: ESWC 2018 Satellite Events - ESWC 2018 Satellite Events, Heraklion, Crete, Greece, June 3-7, 2018, Revised Selected Papers}, volume = {11155}, year = {2018}, pages = {19{\textendash}22}, publisher = {Springer}, organization = {Springer}, address = {Heraklion, Crete, Greece}, abstract = {We discuss a tool capable of rapidly generating pseudo-random syntactically valid ALC expression trees. The program is meant to allow a researcher to create large sets of independently valid expressions with a minimum of personal bias for experimentation.}, keywords = {ALC, Description Logic, DL, random generation, synthetic data}, isbn = {978-3-319-98191-8}, doi = {10.1007/978-3-319-98192-5\_4}, url = {https://doi.org/10.1007/978-3-319-98192-5\_4}, author = {Aaron Eberhart and Michelle Cheatham and Pascal Hitzler} } @proceedings {606, title = {Explaining Trained Neural Networks with Semantic Web Technologies: First Steps}, journal = {Twelveth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy}, year = {2017}, month = {07/2017}, edition = {12}, address = {London, UK}, abstract = {

The ever increasing prevalence of publicly available structured data on the World Wide Web enables new applications in a variety of domains. In this paper, we provide a conceptual approach that leverages such data in order to explain the input-output behavior of trained artificial neural networks. We apply existing Semantic Web technologies in order to provide an experimental proof of concept.

}, keywords = {Artificial Intelligence}, url = {http://daselab.cs.wright.edu/nesy/NeSy17/}, author = {Md Kamruzzaman Sarker and Ning Xie and Derek Doran and Michael Raymer and Pascal Hitzler} } @conference {132, title = {An Ontology Design Pattern for Activity Reasoning}, booktitle = {Proceedings of the 5th Workshop on Ontology and Semantic Web Patterns (WOP2014) co-located with the 13th International Semantic Web Conference {(ISWC} 2014), Riva del Garda, Italy, October 19, 2014.}, year = {2014}, pages = {78{\textendash}81}, abstract = {

Activity is an important concept in many fields, and a number of activity-related ontologies have been developed. While suitable for their designated use cases, these ontologies cannot be easily generalized to other applications. This paper aims at providing a generic ontology design pattern to model the common core of activities in different domains. Such a pattern can be used as a building block to construct more specific activity ontologies.

}, keywords = {Activity, Ontology Design Pattern, OWL}, url = {http://ceur-ws.org/Vol-1302/paper8.pdf}, author = {Amin Abdalla and Yingjie Hu and David Carral and Naicong Li and Krzysztof Janowicz} } @article {61, title = {Paraconsistent OWL and Related Logics}, journal = {Semantic Web}, volume = {4}, year = {2013}, pages = {395{\textendash}427}, abstract = {The Web Ontology Language OWL is currently the most prominent formalism for representing ontologies in Semantic Web applications. OWL is based on description logics, and automated reasoners are used to infer knowledge implicitly present in OWL ontologies. However, because typical description logics obey the classical principle of explosion, reasoning over inconsistent ontologies is impossible in OWL. This is so despite the fact that inconsistencies are bound to occur in many realistic cases, e.g., when multiple ontologies are merged or when ontologies are created by machine learning or data mining tools. In this paper, we present four-valued paraconsistent description logics which can reason over inconsistencies. We focus on logics corresponding to OWL DL and its profiles. We present the logic SROIQ4, showing that it is both sound relative to classical SROIQ and that its embedding into SROIQ is consequence preserving. We also examine paraconsistent varieties of EL++, DL-Lite, and Horn-DLs. The general framework described here has the distinct advantage of allowing classical reasoners to draw sound but nontrivial conclusions from even inconsistent knowledge bases. Truth-value gaps and gluts can also be selectively eliminated from models (by inserting additional axioms into knowledge bases). If gaps but not gluts are eliminated, additional classical conclusions can be drawn without affecting paraconsistency.}, keywords = {Automated Deduction, Complexity, Description Logic, OWL, Paraconsistency, Semantic Web, Web Ontology Language}, doi = {10.3233/SW-2012-0066}, url = {http://dx.doi.org/10.3233/SW-2012-0066}, author = {Frederick Maier and Yue Ma and Pascal Hitzler} } @proceedings {314, title = {10302 Abstracts Collection - Learning paradigms in dynamic environments}, journal = {Learning paradigms in dynamic environments}, year = {2010}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany}, address = {Dagstuhl, Germany}, keywords = {Autonomous learning, Dynamic systems, Neural-symbolic integration, Neurobiology, Recurrent neural networks, Speech processing}, issn = {1862-4405}, url = {http://drops.dagstuhl.de/opus/volltexte/2010/2804}, editor = {Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint} } @article {83, title = {Computational Complexity and Anytime Algorithm for Inconsistency Measurement}, journal = {International Journal of Software and Informatics}, volume = {4}, year = {2010}, pages = {3{\textendash}21}, abstract = {

Measuring inconsistency degrees of inconsistent knowledge bases is an important problem as it provides context information for facilitating inconsistency handling. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. In this paper, we consider the computational aspects of inconsistency degrees of propositional knowledge bases under 4-valued semantics. We first give a complete analysis of the computational complexity of computing inconsistency degrees. As it turns out that computing the exact inconsistency degree is intractable, we then propose an anytime algorithm that provides tractable approximations of the inconsistency degree from above and below. We show that our algorithm satisfies some desirable properties and give experimental results of our implementation of the algorithm

}, keywords = {algorithm, computational complexity, inconsistency measurement, Knowledge representation, multi-valued logic}, url = {http://www.ijsi.org/ch/reader/view_abstract.aspx?file_no=i41\&flag=1}, author = {Yue Ma and Guilin Qi and Guohui Xiao and Pascal Hitzler and Zuoquan Lin} } @article {78, title = {A Reasonable Semantic Web}, journal = {Semantic Web}, volume = {1}, year = {2010}, pages = {39{\textendash}44}, abstract = {

The realization of Semantic Web reasoning is central to substantiating the Semantic Web vision. However, current mainstream research on this topic faces serious challenges, which forces us to question established lines of research and to rethink the underlying approaches. We argue that reasoning for the Semantic Web should be understood as "shared inference," which is not necessarily based on deductive methods. Model-theoretic semantics (and sound and complete reasoning based on it) functions as a gold standard, but applications dealing with large-scale and noisy data usually cannot afford the required runtimes. Approximate methods, including deductive ones, but also approaches based on entirely different methods like machine learning or natureinspired computing need to be investigated, while quality assurance needs to be done in terms of precision and recall values (as in information retrieval) and not necessarily in terms of soundness and completeness of the underlying algorithms.

}, keywords = {Automated Reasoning, Formal Semantics, Knowledge representation, Linked Open Data, Semantic Web}, doi = {10.3233/SW-2010-0010}, url = {http://dx.doi.org/10.3233/SW-2010-0010}, author = {Pascal Hitzler and Frank van Harmelen} }