Proof Explanation in the DR-DEVICE System

Trust is a vital feature for Semantic Web: If users (humans and agents) are to use and integrate system answers, they must trust them. Thus, systems should be able to explain their actions, sources, and beliefs, and this issue is the topic of the proof layer in the design of the Semantic Web. This paper presents the design and implementation of a system for proof explanation on the Semantic Web, based on defeasible reasoning. The basis of this work is the DR-DEVICE system that is extended to handle proofs. A critical aspect is the representation of proofs in an XML language, which is achieved by a RuleML language extension

USE: a concept-based recommendation system to support creative search

Semiotics is a field on which research in Computer Science methodologies has focused, mainly concerning Syntax and Semantics. These methodologies, however, are lacking some flexibility for the continuously evolving web community, in which the knowledge is classified with tags rather than with ontologies. In this paper we propose a system for the recommendation of tagged pictures obtained from the Web. The system, driven by user feedback, executes an abductive reasoning (based on WordNet synset semantic relations) that is able to iteratively lead to new concepts which progressively represent the cognitive creative user state.Peer ReviewedPostprint (published version

A defeasible logic programming approach to the integration of rules and ontologies

The Semantic Web is a vision of the current Web where resources have exact meaning assigned in terms of ontologies, thus enabling agents to reason about them. As inconsistencies cannot be treated by standard reasoning approaches, we use Defeasible Logic Programming (DeLP) to reason with possibly inconsistent ontologies. In this article we show how to integrate rules and ontologies in the Semantic Web. We present an approach that can be used to suitably extend the SWRL standard by incorporating classical and default negated literals in SemanticWeb rules in the presence of incomplete and possibly inconsistent information. The rules and ontologies will be interpreted as a DeLP program allowing the rules to reason on top of a set of (possibly inconsistent) ontologies.Facultad de Informátic

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

Explanation in the Semantic Web: a survey of the state of the art

Semantic Web applications use interconnected distributed data and inferential capabilities to compute their results. The users of Semantic Web applications might find it difficult to understand how a result is produced or how a new piece of information is derived in the process. Explanation enables users to understand the process of obtaining results. Explanation adds transparency to the process of obtaining results and enables user trust on the process. The concept of providing explanation has been first introduced in expert systems and later studied in different application areas. This paper provides a brief review of existing research on explanation in the Semantic Web

Artificial intelligence as law:Presidential address to the seventeenth international conference on artificial intelligence and law

Information technology is so ubiquitous and AI's progress so inspiring that also legal professionals experience its benefits and have high expectations. At the same time, the powers of AI have been rising so strongly that it is no longer obvious that AI applications (whether in the law or elsewhere) help promoting a good society; in fact they are sometimes harmful. Hence many argue that safeguards are needed for AI to be trustworthy, social, responsible, humane, ethical. In short: AI should be good for us. But how to establish proper safeguards for AI? One strong answer readily available is: consider the problems and solutions studied in AI & Law. AI & Law has worked on the design of social, explainable, responsible AI aligned with human values for decades already, AI & Law addresses the hardest problems across the breadth of AI (in reasoning, knowledge, learning and language), and AI & Law inspires new solutions (argumentation, schemes and norms, rules and cases, interpretation). It is argued that the study of AI as Law supports the development of an AI that is good for us, making AI & Law more relevant than ever

Conceptual Modeling in Law: An Interdisciplinary Research Agenda

The article describes how different approaches from the IS field of conceptual modeling should be transferred to the legal domain to enhance comprehensibility of legal regulations and contracts. It is further described how this in turn would benefit the IS discipline. The findings emphasize the importance of further interdisciplinary research on that topic. A research agenda that synthesizes the presented ideas is proposed based on a framework that structures the research field. Researchers from both disciplines, IS and Law, that are interested in this field should use the research agenda to position their research and to derive new and innovative research questions

Foundations of implementations for formal argumentation

We survey the current state of the art of general techniques, as well as specific software systems for solving tasks in abstract argumentation frameworks, structured argumentation frameworks, and approaches for visualizing and analysing argumentation. Furthermore, we discuss challenges and promising techniques such as parallel processing and approximation approaches. Finally, we address the issue of evaluating software systems empirically with links to the International Competition on Computational Models of Argumentation