Technical Issues in the Development of Knowledge-Based Services for the Semantic Web

The Semantic Web aims to extend the current Web with formal semantics in order to improve how users experience the Web, by ameliorating current activities and supporting the automation of some others. So far, current Semantic Web prototypes mostly aim at collecting and exposing information. Still, a semantic layer can support applying Knowledge-Based Systems techniques to the development of brand-new fully-fledged Knowledge-Based Services for the Web. In this paper, we present the technical issues that have to be faced in the development of such a kind of application by presenting the Online Design of Events Application: a Semantic Web-based design support system that assists event organisers in the process of preparing events such as workshops and conferences, by effectively reasoning over an inter-organisational process across the Web

Foundational Challenges in Automated Data and Ontology Cleaning in the Semantic Web

The application of automated reasoning systems to data cleaning in the Semantic Web raises many challenges on the foundational basis of cleaning agent design. The authors discuss some of them. They finally argue that logic trust in the Semantic Web can only be achieved if it is based on certified reasoning.Junta de Andalucía TIC-13

Rule-Based Intelligence on the Semantic Web: Implications for Military Capabilities

Rules are a key element of the Semantic Web vision, promising to provide a foundation for reasoning capabilities that underpin the intelligent manipulation and exploitation of information content. Although ontologies provide the basis for some forms of reasoning, it is unlikely that ontologies, by themselves, will support the range of knowledge-based services that are likely to be required on the Semantic Web. As such, it is important to consider the contribution that rule-based systems can make to the realization of advanced machine intelligence on the Semantic Web. This report aims to review the current state-of-the-art with respect to semantic rule-based technologies. It provides an overview of the rules, rule languages and rule engines that are currently available to support ontology-based reasoning, and it discusses some of the limitations of these technologies in terms of their inability to cope with uncertain or imprecise data and their poor performance in some reasoning contexts. This report also describes the contribution of reasoning systems to military capabilities, and suggests that current technological shortcomings pose a significant barrier to the widespread adoption of reasoning systems within the defence community. Some solutions to these shortcomings are presented and a timescale for technology adoption within the military domain is proposed. It is suggested that application areas such as semantic integration, semantic interoperability, data fusion and situation awareness provide the best opportunities for technology adoption within the 2015 timeframe. Other capabilities, such as decision support and the emulation of human-style reasoning capabilities are seen to depend on the resolution of significant challenges that may hinder attempts at technology adoption and exploitation within the 2020 timeframe

Personalized Web Page Recommendation Using Ontology

In this network era, Web Page Recommendation and web page Recommendation systems can take advantage of semantic network reasoning-capabilities to overcome common limitations of current systems and improve the recommendations’ quality. This paper presents a personalized-web-recommendation system, a system that makes use of representations of items and user-profiles based on ontology in order to provide semantic applications with personalized services. The recommender uses domain ontology to enhance the personalization: on the other hand, user’s interests are modeled in a more effective and accurate way by applying a domain-based inference method; on the other hand, the stemmer algorithm used by our content-based filtering approach, which provides a measure of the affinity between an item and a user, is enhanced by applying a semantic similarity method. Web Usage Mining plays an important role in web page recommender systems and web personalization system. In this paper, we propose an effective personalized web recommendation system based on ontology and Web Usage Mining. The proposed approach integrates semantic knowledge into Web Usage Mining and personalization processes. DOI: 10.17762/ijritcc2321-8169.15071

A first approach to combining ontologies and defeasible argumentation for the semantic web

The Semantic Web is a project intended to create a universal medium for information exchange by giving semantics to the content of documents on the Web through the use of ontology definitions. Problems for modelling common-sense reasoning (such as reasoning with uncertainty or with incomplete and potentially inconsistent information) are also present when defining ontologies. In recent years, defeasible argumentation has succeeded as an approach to formalize such common-sense reasoning. Agents operating in multi-agent systems in the context of the Semantic Web need to interact with each other in order to achieve the goals stated by their users. In this paper we propose a XML-based language named XDeLP for ontology interchange among agents in the web.Eje: VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Using Semantic Web Services for AI-Based Research in Industry 4.0

The transition to Industry 4.0 requires smart manufacturing systems that are easily configurable and provide a high level of flexibility during manufacturing in order to achieve mass customization or to support cloud manufacturing. To realize this, Cyber-Physical Systems (CPSs) combined with Artificial Intelligence (AI) methods find their way into manufacturing shop floors. For using AI methods in the context of Industry 4.0, semantic web services are indispensable to provide a reasonable abstraction of the underlying manufacturing capabilities. In this paper, we present semantic web services for AI-based research in Industry 4.0. Therefore, we developed more than 300 semantic web services for a physical simulation factory based on Web Ontology Language for Web Services (OWL-S) and Web Service Modeling Ontology (WSMO) and linked them to an already existing domain ontology for intelligent manufacturing control. Suitable for the requirements of CPS environments, our pre- and postconditions are verified in near real-time by invoking other semantic web services in contrast to complex reasoning within the knowledge base. Finally, we evaluate our implementation by executing a cyber-physical workflow composed of semantic web services using a workflow management system.Comment: Submitted to ISWC 202

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

Distributed Reasoning in a Peer-to-Peer Setting: Application to the Semantic Web

In a peer-to-peer inference system, each peer can reason locally but can also solicit some of its acquaintances, which are peers sharing part of its vocabulary. In this paper, we consider peer-to-peer inference systems in which the local theory of each peer is a set of propositional clauses defined upon a local vocabulary. An important characteristic of peer-to-peer inference systems is that the global theory (the union of all peer theories) is not known (as opposed to partition-based reasoning systems). The main contribution of this paper is to provide the first consequence finding algorithm in a peer-to-peer setting: DeCA. It is anytime and computes consequences gradually from the solicited peer to peers that are more and more distant. We exhibit a sufficient condition on the acquaintance graph of the peer-to-peer inference system for guaranteeing the completeness of this algorithm. Another important contribution is to apply this general distributed reasoning setting to the setting of the Semantic Web through the Somewhere semantic peer-to-peer data management system. The last contribution of this paper is to provide an experimental analysis of the scalability of the peer-to-peer infrastructure that we propose, on large networks of 1000 peers

First IJCAI International Workshop on Graph Structures for Knowledge Representation and Reasoning (GKR@IJCAI'09)

International audienceThe development of effective techniques for knowledge representation and reasoning (KRR) is a crucial aspect of successful intelligent systems. Different representation paradigms, as well as their use in dedicated reasoning systems, have been extensively studied in the past. Nevertheless, new challenges, problems, and issues have emerged in the context of knowledge representation in Artificial Intelligence (AI), involving the logical manipulation of increasingly large information sets (see for example Semantic Web, BioInformatics and so on). Improvements in storage capacity and performance of computing infrastructure have also affected the nature of KRR systems, shifting their focus towards representational power and execution performance. Therefore, KRR research is faced with a challenge of developing knowledge representation structures optimized for large scale reasoning. This new generation of KRR systems includes graph-based knowledge representation formalisms such as Bayesian Networks (BNs), Semantic Networks (SNs), Conceptual Graphs (CGs), Formal Concept Analysis (FCA), CPnets, GAI-nets, all of which have been successfully used in a number of applications. The goal of this workshop is to bring together the researchers involved in the development and application of graph-based knowledge representation formalisms and reasoning techniques