Multi-agent systems for power engineering applications - part 2 : Technologies, standards and tools for building multi-agent systems

This is the second part of a 2-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part 1 of the paper examined the potential value of MAS technology to the power industry, described fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications, and presented a comprehensive review of the power engineering applications for which MAS are being investigated. It also defined the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part 2 of the paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented. Given the significant and growing interest in this field, it is imperative that the power engineering community considers the standards, tools, supporting technologies and design methodologies available to those wishing to implement a MAS solution for a power engineering problem. The paper describes the various options available and makes recommendations on best practice. It also describes the problem of interoperability between different multi-agent systems and proposes how this may be tackled

Fusing Automatically Extracted Annotations for the Semantic Web

This research focuses on the problem of semantic data fusion. Although various solutions have been developed in the research communities focusing on databases and formal logic, the choice of an appropriate algorithm is non-trivial because the performance of each algorithm and its optimal configuration parameters depend on the type of data, to which the algorithm is applied. In order to be reusable, the fusion system must be able to select appropriate techniques and use them in combination. Moreover, because of the varying reliability of data sources and algorithms performing fusion subtasks, uncertainty is an inherent feature of semantically annotated data and has to be taken into account by the fusion system. Finally, the issue of schema heterogeneity can have a negative impact on the fusion performance. To address these issues, we propose KnoFuss: an architecture for Semantic Web data integration based on the principles of problem-solving methods. Algorithms dealing with different fusion subtasks are represented as components of a modular architecture, and their capabilities are described formally. This allows the architecture to select appropriate methods and configure them depending on the processed data. In order to handle uncertainty, we propose a novel algorithm based on the Dempster-Shafer belief propagation. KnoFuss employs this algorithm to reason about uncertain data and method results in order to refine the fused knowledge base. Tests show that these solutions lead to improved fusion performance. Finally, we addressed the problem of data fusion in the presence of schema heterogeneity. We extended the KnoFuss framework to exploit results of automatic schema alignment tools and proposed our own schema matching algorithm aimed at facilitating data fusion in the Linked Data environment. We conducted experiments with this approach and obtained a substantial improvement in performance in comparison with public data repositories

Minimizing conservativity violations in ontology alignments: algorithms and evaluation

In order to enable interoperability between ontology-based systems, ontology matching techniques have been proposed. However, when the generated mappings lead to undesired logical consequences, their usefulness may be diminished. In this paper, we present an approach to detect and minimize the violations of the so-called conservativity principle where novel subsumption entailments between named concepts in one of the input ontologies are considered as unwanted. The practical applicability of the proposed approach is experimentally demonstrated on the datasets from the Ontology Alignment Evaluation Initiative

Corporate Smart Content Evaluation

Nowadays, a wide range of information sources are available due to the evolution of web and collection of data. Plenty of these information are consumable and usable by humans but not understandable and processable by machines. Some data may be directly accessible in web pages or via data feeds, but most of the meaningful existing data is hidden within deep web databases and enterprise information systems. Besides the inability to access a wide range of data, manual processing by humans is effortful, error-prone and not contemporary any more. Semantic web technologies deliver capabilities for machine-readable, exchangeable content and metadata for automatic processing of content. The enrichment of heterogeneous data with background knowledge described in ontologies induces re-usability and supports automatic processing of data. The establishment of “Corporate Smart Content” (CSC) - semantically enriched data with high information content with sufficient benefits in economic areas - is the main focus of this study. We describe three actual research areas in the field of CSC concerning scenarios and datasets applicable for corporate applications, algorithms and research. Aspect- oriented Ontology Development advances modular ontology development and partial reuse of existing ontological knowledge. Complex Entity Recognition enhances traditional entity recognition techniques to recognize clusters of related textual information about entities. Semantic Pattern Mining combines semantic web technologies with pattern learning to mine for complex models by attaching background knowledge. This study introduces the afore-mentioned topics by analyzing applicable scenarios with economic and industrial focus, as well as research emphasis. Furthermore, a collection of existing datasets for the given areas of interest is presented and evaluated. The target audience includes researchers and developers of CSC technologies - people interested in semantic web features, ontology development, automation, extracting and mining valuable information in corporate environments. The aim of this study is to provide a comprehensive and broad overview over the three topics, give assistance for decision making in interesting scenarios and choosing practical datasets for evaluating custom problem statements. Detailed descriptions about attributes and metadata of the datasets should serve as starting point for individual ideas and approaches

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

A semantically-enriched goal-oriented requirements engineering framework for systems of systems using the i* framework applied to cancer care

In recent years, monolithic systems are being composed into bigger systems as Systems of Systems (SoSs). This evolution of SoS raises several software engineering key challenges, such as the management of emerging inconsistent goals and requirements, which may occur among the various Constituent Systems (CSs) themselves, as well as between the entire SoS and the participating CSs. Another significant challenge is that Systems of Systems Engineering (SoSE) involves more stakeholders than traditional systems engineering, i.e. stakeholders at the SoS-level and the CS-level, where each CS has its own needs and objectives which establish a complex stakeholder environment. To respond to these challenges, this research is aimed at investigating the implications of applying a goal-oriented requirements engineering approach in identifying, modelling and managing emerging goals and their conflicts in SoS context. The key artefact of this research is the development of a Semantically-Enriched Goal-Oriented Requirements Engineering Framework for Systems of Systems using the i* framework, namely the OntoSoS.GORE framework.The OntoSoS.GORE is a three-layered framework designed, developed, demonstrated and then evaluated through following multiple iterations of the Design Science Research Methodology (DSRM) phases, to accomplish the following main objectives: (1) identifying and modelling the SoS global goals and the CSs local goals at different levels of an SoS using the i* framework, in which a new process to extract i* modelling elements from existing user documentation is proposed; (2) maintaining the consistency and integrity of SoS goals at multiple levels through developing a semantic Goals Referential Integrity (sGRI) model in SoS context which consists of an SoSGRI model and an ontology-based model; and (3) managing any conflicts that may occur amongst goals at both the SoS-level and the CS-level, by developing and applying a new goal conflict management approach in SoS context, which consists of two main processes: goal conflict detection and goal conflict resolution.The research framework has been instantiated and validated by applying a real Cancer Care case study at King Hussein Cancer Center (KHCC), Amman, Jordan. Results revealed the effectiveness of applying the framework compared to the current approach applied at KHCC, in terms of addressing higher consistency, completeness and correctness with regard to goal management and conflict management in SoS context. Moreover, the framework provides automation of the processes of following the satisfaction of goals and goals’ conflict management at multiple SoS levels, instead of the manual approach applied currently at KHCC. This automation is accomplished through developing a strategic goal-oriented management tool that is anticipated to be delivered and utilised at KHCC, as well as applying it to other SoS organisations as a proposed solution for goal and conflict management. Another contribution to the Cancer Care and SoS domains is developing a reference i* goal-oriented model for access to Cancer Care which provides a wider system engineering perspective and offers an accessible level of abstraction about Cancer Care goals and their dependencies for stakeholders and domain experts. The reference model provides standardisation of common generic concepts about the domain, in which other Cancer Care organisations can considerably reuse to facilitate the process of capturing and specifying goals and requirements for their practice and validating choices among alternative designs