2016
We venture beyond the customary semantic approach in NMR, namely that of placing orderings on worlds (or valuations). In a modal-logic setting, we motivate and investigate the idea of ordering elements of the accessibility relations in Kripke frames, i.e, world pairs (w,w') (or arrows). The underlying intuition is that some world pairs may be seen as more normal (or typical, or expected) than others. We show this delivers an elegant and intuitive semantic construction, which gives a new perspective on present notions of defeasible necessity. From a modeler's perspective, the new framework we propose is more intuitively appealing. Technically, though, the revisited logic happens to not substantively increase the expressive power of the previously defined preferential modalities. This conclusion follows from an analysis of both semantic constructions via a generalisation of bisimulations to the preferential case. Lest this be seen as a negative result, it essentially means that reasoners based on the previous semantics (which have been shown to preserve the computational complexity of the underlying classical modal language) suffice for reasoning over the new semantics. Finally, we show that the kind of construction we here propose has many fruitful applications, notably in a description-logic context, where it provides the foundations on which to ground useful notions of defeasibility in ontologies yet to be explored.
@misc{135,
author = {Katarina Britz and Ivan Varzinczak},
title = {Preferential modalities revisited},
abstract = {We venture beyond the customary semantic approach in NMR, namely that of placing orderings on worlds (or valuations). In a modal-logic setting, we motivate and investigate the idea of ordering elements of the accessibility relations in Kripke frames, i.e, world pairs (w,w') (or arrows). The underlying intuition is that some world pairs may be seen as more normal (or typical, or expected) than others. We show this delivers an elegant and intuitive semantic construction, which gives a new perspective on present notions of defeasible necessity. From a modeler's perspective, the new framework we propose is more intuitively appealing. Technically, though, the revisited logic happens to not substantively increase the expressive power of the previously defined preferential modalities. This conclusion follows from an analysis of both semantic constructions via a generalisation of bisimulations to the preferential case. Lest this be seen as a negative result, it essentially means that reasoners based on the previous semantics (which have been shown to preserve the computational complexity of the underlying classical modal language) suffice for reasoning over the new semantics. Finally, we show that the kind of construction we here propose has many fruitful applications, notably in a description-logic context, where it provides the foundations on which to ground useful notions of defeasibility in ontologies yet to be explored.},
year = {2016},
url = {http://nmr2016.cs.uct.ac.za/proceedings_nmr2016_online.pdf},
}
2015
The language fuzzing with name binding project generates syntactically valid test programs that exercise the name binding semantics of a language processor. We introduce generation algorithm and a tool, NameFuzz, for the test suite generation. It achieves this by by parsing in a ANTLR grammar representing the context free grammar of the language, along with the language’s name binding rules in the NaBL meta-language. The test sentences are intended to be either accepted (positive test cases) or rejected (failing test cases) by the language processor. The intention is to promote confidence in the language processor as far as semantic correctness is concerned. The generated test suite in syntactically correct, but the limitations of not taking type checking into account or having a method to evaluate expressions lead to a large number of test sentences that are semantically incorrect. To a degree these limitations are overcome by the combinatorial nature of the generation algorithm that ensures that each possible type correct sentence is generated as well.
@phdthesis{137,
author = {G. Newman and Bernd Fischer},
title = {Language Fuzzing with Name Binding},
abstract = {The language fuzzing with name binding project generates syntactically valid test programs that exercise the name binding semantics of a language processor. We introduce generation algorithm and a tool, NameFuzz, for the test suite generation. It achieves this by by parsing in a ANTLR grammar representing the context free grammar of the language, along with the language’s name binding rules in the NaBL meta-language. The test sentences are intended to be either accepted (positive test cases) or rejected (failing test cases) by the language processor. The intention is to promote confidence in the language processor as far as semantic correctness is concerned. The generated test suite in syntactically correct, but the limitations of not taking type checking into account or having a method to evaluate expressions lead to a large number of test sentences that are semantically incorrect. To a degree these limitations are overcome by the combinatorial nature of the generation algorithm that ensures that each possible type correct sentence is generated as well.},
year = {2015},
volume = {Honours},
}
Visualising software can be a tedious and cluttered affair with the design process and development often being out of sync. Some development methodologies even largely do away with the design entirely and focus on short bursts of coding and validation to make sure the project is still on the right track. This document focuses on deriving a methodology and subsequent tool to iteratively and progressively expand concepts, the understanding of the project and development cycles. An existing visualisation is adapted to better suit the needs of the designer by providing the ability to view the project from different layers of abstraction in one concise visualisation.
@phdthesis{136,
author = {J. Breytenbach and Bernd Fischer},
title = {Progressive Software Design Tool},
abstract = {Visualising software can be a tedious and cluttered affair with the design process and development often being out of sync. Some development methodologies even largely do away with the design entirely and focus on short bursts of coding and validation to make sure the project is still on the right track. This document focuses on deriving a methodology and subsequent tool to iteratively and progressively expand concepts, the understanding of the project and development cycles. An existing visualisation is adapted to better suit the needs of the designer by providing the ability to view the project from different layers of abstraction in one concise visualisation.},
year = {2015},
volume = {Honours},
}
In this paper, we enrich description logics (DLs) with non-monotonic reasoning features in a number of ways. We start by investigating a notion of defeasible conditional in the spirit of KLM-style defeasible consequence. In particular, we consider a natural and intuitive semantics for defeasible subsumption in terms of DL interpretations enriched with a preference relation. We propose and investigate syntactic properties (à la Gentzen) for both preferential and rational conditionals and prove representation results for the description logic ALC. This representation result paves the way for more effective decision procedures for defeasible reasoning in DLs. We then move to non-monotonicity in DLs at the level of entailment. We investigate versions of entailment in the context of both preferential and rational subsumption, relate them to preferential and rational closure, and show that computing them can be reduced to classical ALC entailment. This provides further evidence that our semantic constructions are appropriate in a non-monotonic DL setting. One of the barriers to evaluating performance scalability of rational closure is the abscence of naturally occurring DL-based ontologies with defeasible features. We overcome this barrier by devising an approach to introduce defeasible subsumption into classical real world ontologies. This culminates in a set of semi-natural defeasible ontologies that is used, together with a purely artificial set, to test our rational closure algorithms. We found that performance is scalable on the whole with no major bottlenecks.
@misc{130,
author = {Katarina Britz and Giovanni Casini and Tommie Meyer and Kody Moodley and U. Sattler and Ivan Varzinczak},
title = {Rational Defeasible Reasoning for Expressive Description Logics},
abstract = {In this paper, we enrich description logics (DLs) with non-monotonic reasoning features in a number of ways. We start by investigating a notion of defeasible conditional in the spirit of KLM-style defeasible consequence. In particular, we consider a natural and intuitive semantics for defeasible subsumption in terms of DL interpretations enriched with a preference relation. We propose and investigate syntactic properties (à la Gentzen) for both preferential and rational conditionals and prove representation results for the description logic ALC. This representation result paves the way for more effective decision procedures for defeasible reasoning in DLs. We then move to non-monotonicity in DLs at the level of entailment. We investigate versions of entailment in the context of both preferential and rational subsumption, relate them to preferential and rational closure, and show that computing them can be reduced to classical ALC entailment. This provides further evidence that our semantic constructions are appropriate in a non-monotonic DL setting. One of the barriers to evaluating performance scalability of rational closure is the abscence of naturally occurring DL-based ontologies with defeasible features. We overcome this barrier by devising an approach to introduce defeasible subsumption into classical real world ontologies. This culminates in a set of semi-natural defeasible ontologies that is used, together with a purely artificial set, to test our rational closure algorithms. We found that performance is scalable on the whole with no major bottlenecks.},
year = {2015},
}
No Abstract
@{129,
author = {Steve Kroon and S. Nienaber and M.J. Booysen},
title = {A Comparison of Low-Cost Monocular Vision Techniques for Pothole Distance Estimation},
abstract = {No Abstract},
year = {2015},
journal = {IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems},
pages = {419-426},
month = {08/12-10/12},
}
No Abstract
@article{128,
author = {Brink van der Merwe and W.C. Visser and Heila van der Merwe and S.E.A. Nel and O. Tkachuk},
title = {Environment Modeling Using Runtime Values for JPF-Android},
abstract = {No Abstract},
year = {2015},
journal = {ACM SIGSOFT Software Engineering Notes},
volume = {40},
pages = {1-5},
issue = {6},
publisher = {ACM},
address = {New York, USA},
url = {http://dx.doi.org/10.1145/2830719.2830727},
}
No Abstract
@article{126,
author = {Bernd Fischer and G.J. Greene},
title = {Interactive tag cloud visualization of software version control repositories},
abstract = {No Abstract},
year = {2015},
journal = {Software Visualization (VISSOFT)},
pages = {56-65},
url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7332415&isnumber=7332403},
}
Data-driven elicitation of ontologies from structured data is a well-recognized knowledge acquisition bottleneck. The development of efficient techniques for (semi-)automating this task is therefore practically vital --- yet, hindered by the lack of robust theoretical foundations. In this paper, we study the problem of learning Description Logic TBoxes from interpretations, which naturally translates to the task of ontology learning from data. In the presented framework, the learner is provided with a set of positive interpretations (i.e., logical models) of the TBox adopted by the teacher. The goal is to correctly identify the TBox given this input. We characterize the key constraints on the models that warrant finite learnability of TBoxes expressed in selected fragments of the Description Logic $\mathcal{EL}$ and define corresponding learning algorithms.
@{125,
author = {Katarina Britz and Simon Klarman},
title = {Ontology learning from interpretations in lightweight description logics},
abstract = {Data-driven elicitation of ontologies from structured data is a well-recognized knowledge acquisition bottleneck. The development of efficient techniques for (semi-)automating this task is therefore practically vital --- yet, hindered by the lack of robust theoretical foundations. In this paper, we study the problem of learning Description Logic TBoxes from interpretations, which naturally translates to the task of ontology learning from data. In the presented framework, the learner is provided with a set of positive interpretations (i.e., logical models) of the TBox adopted by the teacher. The goal is to correctly identify the TBox given this input. We characterize the key constraints on the models that warrant finite learnability of TBoxes expressed in selected fragments of the Description Logic $\mathcal{EL}$ and define corresponding learning algorithms.},
year = {2015},
journal = {25th International Conference on Inductive Logic Programming},
month = {20/08-22/08},
}
Data-driven elicitation of ontologies from structured data is a well-recognized knowledge acquisition bottleneck. The development of efficient techniques for (semi-)automating this task is therefore practically vital --- yet, hindered by the lack of robust theoretical foundations. In this paper, we study the problem of learning Description Logic TBoxes from interpretations, which naturally translates to the task of ontology learning from data. In the presented framework, the learner is provided with a set of positive interpretations (i.e., logical models) of the TBox adopted by the teacher. The goal is to correctly identify the TBox given this input. We characterize the key constraints on the models that warrant finite learnability of TBoxes expressed in selected fragments of the Description Logic EL and define corresponding learning algorithms.
@misc{116,
author = {Katarina Britz and Simon Klarman},
title = {Towards unsupervised ontology learning from data},
abstract = {Data-driven elicitation of ontologies from structured data is a well-recognized knowledge acquisition bottleneck. The development of efficient techniques for (semi-)automating this task is therefore practically vital --- yet, hindered by the lack of robust theoretical foundations. In this paper, we study the problem of learning Description Logic TBoxes from interpretations, which naturally translates to the task of ontology learning from data. In the presented framework, the learner is provided with a set of positive interpretations (i.e., logical models) of the TBox adopted by the teacher. The goal is to correctly identify the TBox given this input. We characterize the key constraints on the models that warrant finite learnability of TBoxes expressed in selected fragments of the Description Logic EL and define corresponding learning algorithms.},
year = {2015},
publisher = {CEUR-WS Volume1423},
isbn = {ISSN 1613-0073},
url = {http://ceur-ws.org/Vol-1423/},
}
2014
No Abstract
@phdthesis{86,
author = {Henno Gous and Aurona Gerber and C. Schutte},
title = {Towards a reference architecture for integrated knowledge networks},
abstract = {No Abstract},
year = {2014},
volume = {PhD},
}
2011
In this paper we describe a research project to develop an optimal information retrieval system in an Information Operations domain. Information Operations is the application and management of information to gain an advantage over an opponent and to defend one’s own interests. Corporations, governments, and military forces are facing increasing exposure to strategic information-based actions. Most national defence and security organisations regard Information Operations as both a defensive and offensive tool, and some commercial institutions are also starting to recognise the value of Information Operations. An optimal information retrieval system should have the capability to extract relevant and reasonably complete information from different electronic data sources which should decrease information overload. Information should be classified in a way such that it can be searched and extracted effectively. The authors of this paper have completed an initial phase in the investigation and design of a knowledge system that can be used to extract relevant and complete knowledge for the planning and execution of Information Operations. During this initial phase of the project, we performed a needs analysis and problem analysis and our main finding is the recommendation of the use of logic-based ontologies: it has the advantage of an unambiguous semantics, facilitates intelligent search, provides an optimal trade-off between expressivity and complexity, and yields optimal recall of information. The risk of adopting this technology is its status as an emerging technology and therefore we include recommendations for the development of a prototype system.
@{465,
author = {Louise Leenen and Ronell Alberts and Katarina Britz and Aurona Gerber and Tommie Meyer},
title = {Developing a Knowledge System for Information Operations},
abstract = {In this paper we describe a research project to develop an optimal information retrieval system in an Information Operations domain. Information Operations is the application and management of information to gain an advantage over an opponent and to defend one’s own interests. Corporations, governments, and military forces are facing increasing exposure to strategic information-based actions. Most national defence and security organisations regard Information Operations as both a defensive and offensive tool, and some commercial institutions are also starting to recognise the value of Information Operations. An optimal information retrieval system should have the capability to extract relevant and reasonably complete information from different electronic data sources which should decrease information overload. Information should be classified in a way such that it can be searched and extracted effectively. The authors of this paper have completed an initial phase in the investigation and design of a knowledge system that can be used to extract relevant and complete knowledge for the planning and execution of Information Operations. During this initial phase of the project, we performed a needs analysis and problem analysis and our main finding is the recommendation of the use of logic-based ontologies: it has the advantage of an unambiguous semantics, facilitates intelligent search, provides an optimal trade-off between expressivity and complexity, and yields optimal recall of information. The risk of adopting this technology is its status as an emerging technology and therefore we include recommendations for the development of a prototype system.},
year = {2011},
journal = {6th International Conference on Information Warfare and Security},
month = {17/03-18/03},
address = {Washington D.C., United States of America},
isbn = {978-1-906638-93-1},
doi = {10204/4951},
}
