Connection-Minimal Abduction in {E}L via Translation to {FOL} (Extended Abstract)

International audienceAbduction in description logics finds extensions of a knowledge base to make it entail an observation. As such, it can be used to explain why the observation does not follow, to repair incomplete knowledge bases, and to provide possible explanations for unexpected observations. We consider TBox abduction in the lightweight description logic EL , where the observation is a concept inclusion and the background knowledge is a TBox, i.e., a set of concept inclusions. To avoid useless answers, such problems usually come with further restrictions on the solution space and/or minimality criteria that help sort the chaff from the grain. We argue that existing minimality notions are insufficient, and introduce connection minimality. This criterion follows Occam’s razor by rejecting hypotheses that use concept inclusions unrelated to the problem at hand. We show how to compute a special class of connection-minimal hypotheses in a sound and complete way. Our technique is based on a translation to first-order logic, and constructs hypotheses based on prime implicates. We evaluate a prototype implementation of our approach on ontologies from the medical domain

Connection-Minimal Abduction in EL via Translation to FOL: Extended Abstract

International audienceAbduction is the task of finding possible extensions of a given knowledge base that would make a given sentence logically entailed. As such, it can be used to explain why the sentence does not follow, to repair incomplete knowledge bases, and to provide possible explanations for unexpected observations. We consider TBox abduction in the lightweight description logic EL, where the observation is a concept inclusion and the background knowledge is a description logic TBox. To avoid useless answers, such problems usually come with further restrictions on the solution space and/or minimality criteria that help sort the chaff from the grain. We argue that existing minimality notions are insufficient, and introduce connection minimality. This criterion rejects hypotheses that use concept inclusions “disconnected” from the problem at hand. We show how to compute a special class of connection-minimal hypotheses in a sound and complete way. Our technique is based on a translation to first-order logic, and constructs hypotheses based on prime implicates. We evaluate a prototype implementation of our approach on ontologies from the medical domain

Strategies and Techniques for Use and Exploitation of Contextual Information in High-Level Fusion Architectures

Proceedings of: 13th Conference on Information Fusion (FUSION 2010): Edinburgh, UK. 26-29 July 2010.Contextual Information is proving to be not only an additional exploitable information source for improving entity and situational estimates in certain Information Fusion systems, but can also be the entire focus of estimation for such systems as those directed to Ambient Intelligence (AI) and Context-Aware(CA) applications. This paper will discuss the role(s) of Contextual Information (CI) in a wide variety of IF applications to include AI, CA, Defense, and Cyber-security among possible others, the issues involved in designing strategies and techniques for CI use and exploitation, provide some exemplars of evolving CI use/exploitation designs on our current projects, and describe some general frameworks that are evolving in various application domains where CI is proving critical.The UC3M Team gratefully acknowledge that this research activity is supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732- C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02. UC3M also thanks Prof. James Llinas for his helpful comments during his stay, which has been supported by the collaboration agreement ‘Chairs of Excellence’ between University Carlos III and Banco Santander. The US/UB Team gratefully acknowledge that this research activity is supported by a Multidisciplinary University Research Initiative (MURI) grant (Number W911NF-09-1-0392) for “Unified Research on Networkbased Hard/Soft Information Fusion”, issued by the US Army Research Office (ARO) under the program management of Dr. John LaveryPublicad

Ontological representation of context knowledge for visual data fusion

8 pages, 4 figures.-- Contributed to: 12th International Conference on Information Fusion, 2009 (FUSION '09, Seattle, Washington, US, Jul 6-9, 2009).Context knowledge is essential to achieve successful information fusion, especially at high JDL levels. Context can be used to interpret the perceived situation, which is required for accurate assessment. Both types of knowledge, contextual and perceptual, can be represented with formal languages such as ontologies, which support the creation of readable representations and reasoning with them. In this paper, we present an ontology-based model compliant with JDL to represent knowledge in cognitive visual data fusion systems. We depict the use of the model with an example on surveillance. We show that such a model promotes system extensibility and facilitates the incorporation of humans in the fusion loop.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM MADRINET S-0505/TIC/0255 and DPS2008-07029-C02-02.Publicad

An Efficient Bit Vector Approach to Semantics-Based Machine Perception in Resource-Constrained Devices

The primary challenge of machine perception is to define efficient computational methods to derive high-level knowledge from low-level sensor observation data. Emerging solutions are using ontologies for expressive representation of concepts in the domain of sensing and perception, which enable advanced integration and interpretation of heterogeneous sensor data. The computational complexity of OWL, however, seriously limits its applicability and use within resource-constrained environments, such as mobile devices. To overcome this issue, we employ OWL to formally define the inference tasks needed for machine perception – explanation and discrimination – and then provide efficient algorithms for these tasks, using bit-vector encodings and operations. The applicability of our approach to machine perception is evaluated on a smart-phone mobile device, demonstrating dramatic improvements in both efficiency and scale

Reasoning about Explanations for Negative Query Answers in DL-Lite

In order to meet usability requirements, most logic-based applications provide explanation facilities for reasoning services. This holds also for Description Logics, where research has focused on the explanation of both TBox reasoning and, more recently, query answering. Besides explaining the presence of a tuple in a query answer, it is important to explain also why a given tuple is missing. We address the latter problem for instance and conjunctive query answering over DL-Lite ontologies by adopting abductive reasoning; that is, we look for additions to the ABox that force a given tuple to be in the result. As reasoning tasks we consider existence and recognition of an explanation, and relevance and necessity of a given assertion for an explanation. We characterize the computational complexity of these problems for arbitrary, subset minimal, and cardinality minimal explanations

Queer(y)ing agent-based modeling for use in LGBTQ Studies: an example from workplace inequalities

This article explores the contribution agent-based modeling (ABM) can make to the study of LGBTQ workplace inequalities and, conversely, how ABM can adapt to theoretical traditions integral to LGBTQ studies. It introduces an example LGBTQ workplace model, developed as part of the CILIA-LGBTQI+ project, to illustrate how ABM complements existing methods, can address methodological binarism and bridge macro and micro accounts within LGBTQ studies of the workplace. The model is intended as an important starting point in developing the role of ABM in LGBTQ research and for bridging qualitative- and quantitative-derived insights. Likewise, the article discusses some approaches for negotiating theoretical and methodological tensions identified when integrating queer and intersectional insight with ABM

High-Level Information Fusion in Visual Sensor Networks

Information fusion techniques combine data from multiple sensors, along with additional information and knowledge, to obtain better estimates of the observed scenario than could be achieved by the use of single sensors or information sources alone. According to the JDL fusion process model, high-level information fusion is concerned with the computation of a scene representation in terms of abstract entities such as activities and threats, as well as estimating the relationships among these entities. Recent experiences confirm that context knowledge plays a key role in the new-generation high-level fusion systems, especially in those involving complex scenarios that cause the failure of classical statistical techniques –as it happens in visual sensor networks. In this chapter, we study the architectural and functional issues of applying context information to improve high-level fusion procedures, with a particular focus on visual data applications. The use of formal knowledge representations (e.g. ontologies) is a promising advance in this direction, but there are still some unresolved questions that must be more extensively researched.The UC3M Team gratefully acknowledges that this research activity is supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02