Time and defeasibility in FIPA ACL semantics

AbstractInferences about speech acts are often conditional, non-monotonic, and involve the issue of time. Most agent communication languages, however, ignore these issues, due to the difficulty to combine them in a single formalism. This paper addresses such issues in defeasible logic, and shows how to express a semantics for ACLs in order to make non-monotonic inferences on the basis of speech acts

Context-Aware Service Composition in Pervasive Computing Environments

International audienceA major challenge in pervasive computing environments is to provide users with complex, context-sensitive applications, dynamically composed from networked services. In this paper, we present an approach to the dynamic, context-aware composition of services to perform user tasks, i.e., software applications abstractly described on the user's handheld device. Both networked services and user tasks aremodeled as semanticWeb services in OWL-S extended with context information. The distinctive feature of our solution is the ability to compose Web services that expose complex behaviors (conversations) to realize a user task that itself has a complex behavior. Furthermore, the context-related requirements of the task are met by aggregating the context-sensitive behaviors of the individual services

A light-weight concept ontology for annotating digital music.

In the recent time, the digital music items on the internet have been evolving to an enormous information space where we try to find/locate the piece of information of our choice by means of search engine. The current trend of searching for music by means of music consumers' keywords/tags is unable to provide satisfactory search results; and search and retrieval of music may be potentially improved if music metadata is created from semantic information provided by association of end-users' tags with acoustic metadata which is easy to extract automatically from digital music items. Based on this observation, our research objective was to investigate how music producers may be able to annotate music against MPEG-7 description (with its acoustic metadata) to deliver meaningful search results. In addressing this question, we investigated the potential of multimedia ontologies to serve as backbone for annotating music items and prospective application scenarios of semantic technologies in the digital music industry. We achieved with our main contribution under this thesis is the first prototype of mpeg-7Music annotation ontology that establishes a mapping of end-users tags with MPEG-7 acoustic metadata as well as extends upper level multimedia ontologies with end-user tags. Additionally, we have developed a semi-automatic annotation tool to demonstrate the potential of the mpeg-7Music ontology to serve as light weight concept ontology for annotating digital music by music producers. The proposed ontology has been encoded in dominant semantic web ontology standard OWL1.0 and provides a standard interoperable representation of the generated semantic metadata. Our innovations in designing the semantic annotation tool were focussed on supporting the music annotation vocabulary (i.e. the mpeg-7Music) in an attempt to turn the music metadata information space to a knowledgebase

Complex Event Recognition: a comparison between FlinkCEP and the Run-Time Event Calculus

Ο κλάδος της Αναγνώρισης Σύνθετων Γεγονότων πάνω σε ροές από δεδομένα έχει επιδείξει αξιοσημείωτη ανάπτυξη τα τελευταία χρόνια. Τα συστήματα αναγνώρισης σύνθετων γεγονότων περιεργάζονται ροές από δεδομένα με σκοπό τον εντοπισμό σύνθετων φαινομένων, που εκφράζουν σχέσεις ανάμεσα στα δεδομένα εισόδου. Ο αριθμός των συστημάτων που έχουν αναπτυχθεί τα τελευταία χρόνια έχει δημιουργήσει την ανάγκη για μελέτη και σύγκριση των δυνατοτήτων τους. Σε αυτήν την μελέτη επιλέγουμε δύο συστήματα από τις πιο επικρατούσες κατηγορίες. Διαλέγουμε το FlinkCEP από τα συστήματα βασισμένα σε αυτόματα και το RTEC από τα συστήματα που χρησιμοποιούν λογική. Παρουσιάζουμε μια θεωρητική σύγκριση της εκφραστικότητας των δύο συστημάτων, μαζί με μια πειραματική αξιολόγηση της αποδοτικότητας τους, χρησιμοποιώντας πραγματικά δεδομένα.The field of Complex Event Recognition (CER) on streams of data has shown remarkable growth the last few years. CER systems use streaming data in order to detect composite phenomena expressing relations between the input data. The amount of developed CER systems has created the need to examine and compare their capabilities. In this study we have chosen two systems, originating form the most dominant categories. From automata-based approaches we have selected FlinkCEP and from Logic-based systems we have selected RTEC. We present a theoretical comparison of the two systems’ expressiveness, along with an empirical evaluation of the efficiency, using real data

A knowledge based approach to integration of products, processes and reconfigurable automation resources

The success of next generation automotive companies will depend upon their ability to adapt to ever changing market trends thus becoming highly responsive. In the automotive sector, the assembly line design and reconfiguration is an especially critical and extremely complex job. The current research addresses some of the aspects of this activity under the umbrella of a larger ongoing research project called Business Driven Automation (BDA) project. The BDA project aims to carry out complete virtual 3D modeling-based verifications of the assembly line for new or revised products in contrast to the prevalent practice of manual evaluation of effects of product change on physical resources. [Continues.

Scalable Reasoning for Knowledge Bases Subject to Changes

ScienceWeb is a semantic web system that collects information about a research community and allows users to ask qualitative and quantitative questions related to that information using a reasoning engine. The more complete the knowledge base is, the more helpful answers the system will provide. As the size of knowledge base increases, scalability becomes a challenge for the reasoning system. As users make changes to the knowledge base and/or new information is collected, providing fast enough response time (ranging from seconds to a few minutes) is one of the core challenges for the reasoning system. There are two basic inference methods commonly used in first order logic: forward chaining and backward chaining. As a general rule, forward chaining is a good method for a static knowledge base and backward chaining is good for the more dynamic cases. The goal of this thesis was to design a hybrid reasoning architecture and develop a scalable reasoning system whose efficiency is able to meet the interaction requirements in a ScienceWeb system when facing a large and evolving knowledge base. Interposing a backward chaining reasoner between an evolving knowledge base and a query manager with support of trust yields an architecture that can support reasoning in the face of frequent changes. An optimized query-answering algorithm, an optimized backward chaining algorithm and a trust-based hybrid reasoning algorithm are three key algorithms in such an architecture. Collectively, these three algorithms are significant contributions to the field of backward chaining reasoners over ontologies. I explored the idea of trust in the trust-based hybrid reasoning algorithm, where each change to the knowledge base is analyzed as to what subset of the knowledge base is impacted by the change and could therefore contribute to incorrect inferences. I adopted greedy ordering and deferring joins in optimized query-answering algorithm. I introduced four optimizations in the algorithm for backward chaining. These optimizations are: 1) the implementation of the selection function, 2) the upgraded substitute function, 3) the application of OLDT and 4) solving of the owl: sameAs problem. I evaluated our optimization techniques by comparing the results with and without optimization techniques. I evaluated our optimized query answering algorithm by comparing to a traditional backward-chaining reasoner. I evaluated our trust-based hybrid reasoning algorithm by comparing the performance of a forward chaining algorithm to that of a pure backward chaining algorithm. The evaluation results have shown that the hybrid reasoning architecture with the scalable reasoning system is able to support scalable reasoning of ScienceWeb to answer qualitative questions effectively when facing both a fixed knowledge base and an evolving knowledge base

Semantic Selection of Internet Sources through SWRL Enabled OWL Ontologies

This research examines the problem of Information Overload (IO) and give an overview of various attempts to resolve it. Furthermore, argue that instead of fighting IO, it is advisable to start learning how to live with it. It is unlikely that in modern information age, where users are producer and consumer of information, the amount of data and information generated would decrease. Furthermore, when managing IO, users are confined to the algorithms and policies of commercial Search Engines and Recommender Systems (RSs), which create results that also add to IO. this research calls to initiate a change in thinking: this by giving greater power to users when addressing the relevance and accuracy of internet searches, which helps in IO. However powerful search engines are, they do not process enough semantics in the moment when search queries are formulated. This research proposes a semantic selection of internet sources, through SWRL enabled OWL ontologies. the research focuses on SWT and its Stack because they (a)secure the semantic interpretation of the environments where internet searches take place and (b) guarantee reasoning that results in the selection of suitable internet sources in a particular moment of internet searches. Therefore, it is important to model the behaviour of users through OWL concepts and reason upon them in order to address IO when searching the internet. Thus, user behaviour is itemized through user preferences, perceptions and expectations from internet searches. The proposed approach in this research is a Software Engineering (SE) solution which provides computations based on the semantics of the environment stored in the ontological model