Towards Contingent World Descriptions in Description Logics

The philosophical, logical, and terminological junctions between Description Logics (DLs) and Modal Logic (ML) are important because they can support the formal analysis of modal notions of ‘possibility’ and ‘necessity’ through the lens of DLs. This paper introduces functional contingents in order to (i) structurally and terminologically analyse ‘functional possibility’ and ‘functional necessity’ in DL world descriptions and (ii) logically and terminologically annotate DL world descriptions based on functional contingents. The most significant contributions of this research are the logical characterisation and terminological analysis of functional contingents in DL world descriptions. The ultimate goal is to investigate how modal operators can – logically and terminologically – be expressed within DL world descriptions

Large-Scale Storage and Reasoning for Semantic Data Using Swarms

Scalable, adaptive and robust approaches to store and analyze the massive amounts of data expected from Semantic Web applications are needed to bring the Web of Data to its full potential. The solution at hand is to distribute both data and requests onto multiple computers. Apart from storage, the annotation of data with machine-processable semantics is essential for realizing the vision of the Semantic Web. Reasoning on webscale data faces the same requirements as storage. Swarm-based approaches have been shown to produce near-optimal solutions for hard problems in a completely decentralized way. We propose a novel concept for reasoning within a fully distributed and self-organized storage system that is based on the collective behavior of swarm individuals and does not require any schema replication. We show the general feasibility and efficiency of our approach with a proof-of-concept experiment of storage and reasoning performance. Thereby, we positively answer the research question of whether swarm-based approaches are useful in creating a large-scale distributed storage and reasoning system. © 2012 IEEE

A Knowledge Graph Based Approach to Social Science Surveys

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A Knowledge Graph Based Approach to Social Science Surveys

Recent success of knowledge graphs has spurred interest in applying them in open science, such as on intelligent survey systems for scientists. However, efforts to understand the quality of candidate survey questions provided by these methods have been limited. Indeed, existing methods do not consider the type of on-the-fly content planning that is possible for face-to-face surveys and hence do not guarantee that selection of subsequent questions is based on response to previous questions in a survey. To address this limitation, we propose a dynamic and informative solution for an intelligent survey system that is based on knowledge graphs. To illustrate our proposal, we look into social science surveys, focusing on ordering the questions of a questionnaire component by their level of acceptance, along with conditional triggers that further customise participants' experience. Our main findings are: (i) evaluation of the proposed approach shows that the dynamic component can be beneficial in terms of lowering the number of questions asked per variable, thus allowing more informative data to be collected in a survey of equivalent length; and (ii) a primary advantage of the proposed approach is that it enables grouping of participants according to their responses, so that participants are not only served appropriate follow-up questions, but their responses to these questions may be analysed in the context of some initial categorisation. We believe that the proposed approach can easily be applied to other social science surveys based on grouping definitions in their contexts. The knowledge-graph-based intelligent survey approach proposed in our work allows online questionnaires to approach face-to-face interaction in their level of informativity and responsiveness, as well as duplicating certain advantages of interview-based data collection

extending description logics with uncertainty reasoning in possibilistic logic

Possibilistic logic provides a convenient tool for dealing with uncertainty and handling inconsistency. In this paper, we propose possibilistic description logics as an extension of description logics, which are a family of well-known ontology languages. We first give the syntax and semantics of possibilistic description logics and define several inference services in possibilistic description logics. We show that these inference serviced can be reduced to the task of computing the inconsistency degree of a knowledge base in possibilistic description logics. Since possibilistic inference services suffer from the drowning problem, that is, axioms whose confidence degrees are less than or equal to the inconsistency are not used, we consider a drowning-free variant of possibilistic inference, called linear order inference. We propose an algorithm for computing the inconsistency degree of a possibilistic description logic knowledge base and an algorithm for the linear order inference. We consider the impact of our possibilistic description logics on ontology learning and ontology merging. Finally, we implement these algorithms and provide some interesting evaluation results. © 2011 Wiley Periodicals, Inc

Extending Description Logics with Uncertainty Reasoning in Possibilistic Logic

Possibilistic logic provides a convenient tool for dealing with inconsistency and handling uncertainty. In this paper, we propose possibilistic description logics as an extension of description logics. We give semantics and syntax of possibilistic description logics. We then define two inference services in possibilistic description logics. Since possibilistic inference suffers from the drowning problem, we consider a drowning-free variant of possibilistic inference, called linear order inference. Finally, we implement the algorithms for inference services in possibilistic description logics using KAON2 reasoner

Principles of KLM-style Defeasible Description Logics

The past 25 years have seen many attempts to introduce defeasible-reasoning capabilities into a description logic setting. Many, if not most, of these attempts are based on preferential extensions of description logics, with a significant number of these, in turn, following the so-called KLM approach to defeasible reasoning initially advocated for propositional logic by Kraus, Lehmann, and Magidor. Each of these attempts has its own aim of investigating particular constructions and variants of the (KLM-style) preferential approach. Here our aim is to provide a comprehensive study of the formal foundations of preferential defeasible reasoning for description logics in the KLM tradition. We start by investigating a notion of defeasible subsumption in the spirit of defeasible conditionals as studied by Kraus, Lehmann, and Magidor in the propositional case. In particular, we consider a natural and intuitive semantics for defeasible subsumption, and we investigate KLM-style syntactic properties for both preferen- tial and rational subsumption. Our contribution includes two representation results linking our semantic constructions to the set of preferential and rational properties considered. Besides showing that our seman- tics is appropriate, these results pave the way for more effective decision procedures for defeasible reasoning in description logics. Indeed, we also analyse the problem of non-monotonic reasoning in description logics at the level of entailment and present an algorithm for the computation of rational closure of a defeasible knowledge base. Importantly, our algorithm relies completely on classical entailment and shows that the computational complexity of reasoning over defeasible knowledge bases is no worse than that of reasoning in the underlying classical DL ALC