Showing papers in "Knowledge Engineering Review in 2015"

Ontology evolution: a process-centric survey

Abstract: Ontology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of Semantic Web techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approaches to ontology evolution are designed as multiple-stage processes combining techniques from a variety of fields (e.g., natural language processing and reasoning). However, the few existing surveys on this topic lack an in-depth analysis of the various stages of the ontology evolution process. This survey extends the literature by adopting a process-centric view of ontology evolution. Accordingly, we first provide an overall process model synthesized from an overview of the existing models in the literature. Then we survey the major approaches to each of the steps in this process and conclude on future challenges for techniques aiming to solve that particular stage.

A survey of qualitative spatial representations

Abstract: Representation and reasoning with qualitative spatial relations is an important problem in artificial intelligence and has wide applications in the fields of geographic information system, computer vision, autonomous robot navigation, natural language understanding, spatial databases and so on. The reasons for this interest in using qualitative spatial relations include cognitive comprehensibility, efficiency and computational facility. This paper summarizes progress in qualitative spatial representation by describing key calculi representing different types of spatial relationships. The paper concludes with a discussion of current research and glimpse of future work.

A survey on text mining in social networks

Abstract: In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites. Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards. Data in social networking websites is inherently unstructured and fuzzy in nature. In everyday life conversations, people do not care about the spellings and accurate grammatical construction of a sentence that may lead to different types of ambiguities, such as lexical, syntactic, and semantic. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several surveys have been conducted to analyze different methods for the information extraction. Most of the surveys emphasized on the application of different text mining techniques for unstructured data sets reside in the form of text documents, but do not specifically target the data sets in social networking website. This survey attempts to provide a thorough understanding of different text mining techniques as well as the application of these techniques in the social networking websites. This survey investigates the recent advancement in the field of text analysis and covers two basic approaches of text mining, such as classification and clustering that are widely used for the exploration of the unstructured text available on the Web.

Evolutionary multi-agent systems

Abstract: The aim of this paper is to give a survey on the development and applications of evolutionary multi-agent systems (EMAS). The paper starts with a general introduction describing the background, structure and behaviour of EMAS. EMAS application to solving global optimisation problems is presented in the next section along with its modification targeted at lowering the computation costs by early removing certain agents based on immunological inspirations. Subsequent sections deal with the elitist variant of EMAS aimed at solving multi-criteria optimisation problems, and the co-evolutionary one aimed at solving multi-modal optimisation problems. Each variation of EMAS is illustrated with selected experimental results.

Planning in BDI agents: a survey of the integration of planning algorithms and agent reasoning

Abstract: Agent programming languages have often avoided the use of automated (first principles or hierarchical) planners in favour of predefined plan/recipe libraries for computational efficiency reasons. This allows for very efficient agent reasoning cycles, but limits the autonomy and flexibility of the resulting agents, oftentimes with deleterious effects on the agent's performance. Planning agents can, for instance, synthesise a new plan to achieve a goal for which no predefined recipe worked, or plan to make viable the precondition of a recipe belonging to a goal being pursued. Recent work on integrating automated planning with belief-desire-intention (BDI)-style agent architectures has yielded a number of systems and programming languages that exploit the efficiency of standard BDI reasoning, as well as the flexibility of generating new recipes at runtime. In this paper, we survey these efforts and point out directions for future work.

Personalization and rule strategies in data-intensive intelligent context-aware systems

Abstract: The concept of personalization in its many forms has gained traction driven by the demands of computer-mediated interactions generally implemented in large-scale distributed systems and ad hoc wireless networks. Personalization requires the identification and selection of entities based on a defined profile (a context); an entity has been defined as a person, place, or physical or computational object. Context employs contextual information that combines to describe an entities current state. Historically, the range of contextual information utilized (in context-aware systems) has been limited to identity, location, and proximate data; there has, however, been advances in the range of data and information addressed. As such, context can be highly dynamic with inherent complexity. In addition, context-aware systems must accommodate constraint satisfaction and preference compliance.This article addresses personalization and context with consideration of the domains and systems to which context has been applied and the nature of the contextual data. The developments in computing and service provision are addressed with consideration of the relationship between the evolving computing landscape and context. There is a discussion around rule strategies and conditional relationships in decision support. Logic systems are addressed with an overview of the open world assumption versus the closed world assumption and the relationship with the Semantic Web. The event-driven rule-based approach, which forms the basis upon which intelligent context processing can be realized, is presented with an evaluation and proof-of-concept. The issues and challenges identified in the research are considered with potential solutions and research directions; alternative approaches to context processing are discussed. The article closes with conclusions and open research questions.

Understanding Agent-Oriented Software Engineering methodologies

Abstract: For many years, the progress in agent-oriented development has focused on tools and methods for particular development phases. This has not been enough for the industry to accept agent technology as we expected. Our hypothesis is that the Agent-Oriented Software Engineering (AOSE) community has not recognized the kind of development methods that industry actually demands. We propose to analyze this hypothesis starting with a more precise definition of what an AOSE methodology should be. This definition is the first step for a review of the current progress of an illustrative selection of methodologies, looking for missing elements and future lines of improvement. The result is an account of how well the AOSE community is meeting the software lifecycle needs. It can be advanced that AOSE methodologies are far from providing all the answers industry requires and that effort has grounded mainly in requirements, design, and implementation phases.

Federated query processing on linked data: A qualitative survey and open challenges

Abstract: A large number of data providers publish and connect their structured data on the Web as linked data. Thus, the Web of data becomes a global data space. In this paper, we initially give an overview of query processing approaches used in this interlinked and distributed environment, and then focus on federated query processing on linked data. We provide a detailed and clear insight on data source selection, join methods and query optimization methods of existing query federation engines. Furthermore, we present a qualitative comparison of these engines and give a complementary comparison of the measured metrics of each engine with the idea of pointing out the major strengths of each one. Finally, we discuss the major challenges of federated query processing on linked data.

Software engineering for self-organizing systems

Abstract: Self-organizing software systems are an increasingly attractive approach to highly distributed, decentralized, dynamic applications. In some domains (such as the Internet), the interaction of originally independent systems yields a self-organizing system de facto, and engineers must take these characteristics into account to manage them. This review surveys current work in this field and outlines its main themes, identifies challenges for future research, and addresses the continuity between software engineering in general and techniques appropriate for self-organizing systems.

Self-organising management of user-generated data and knowledge

Abstract: The proliferation of sensor networks, mobile and pervasive computing has provided the technological push for a new class of participatory-sensing applications, based on sensing and aggregating user-generated content, and transforming it into knowledge. However, given the power and value of both the raw data and the derived knowledge, to ensure that the generators are commensurate beneficiaries, we advocate an open approach to the data and intellectual property rights by treating user-generated content, as well as derived information and knowledge, as a common-pool resource. In this paper, we undertake an extensive review of experimental, commercial and social participatory sensory applications, from which we identify that a decentralised, community-oriented governance model is required to support this approach. Furthermore, we show that Ostrom’s institutional analysis and development framework, in conjunction with a framework for self-organising electronic institutions, can be used to give both an architecture and algorithmic base for the requisite governance model, in terms of operational and collective-choice rules specified in computational logic. This provides, we believe, the foundations for engineering knowledge commons for the next generation of participatory-sensing applications, in which the data generators are also the primary beneficiaries.

XML Clustering: A Review of Structural Approaches

Abstract: With its presence in data integration, chemistry, biological and geographic systems, XML has become an important standard not only in computer science. A common problem among the mentioned applications involves structural clustering of XML documents — an issue that has been thoroughly studied and led to the creation of a myriad of approaches. In this paper, we present a comprehensive review of structural XML clustering. First, we provide a basic introduction to the problem and highlight the main challenges in this research area. Subsequently, we divide the problem into three subtasks and discuss the most common document representations, structural similarity measures, and clustering algorithms. Additionally, we present the most popular evaluation measures, which can be used to estimate clustering quality. Finally, we analyze and compare 23 state-of-the-art approaches and arrange them in an original taxonomy. By providing an up-to-date analysis of existing structural XML clustering algorithms, we hope to showcase methods suitable for current applications and draw lines of future research.

Performance and trends in recent opinion retrieval techniques

Abstract: This paper presents trends and performance of opinion retrieval techniques proposed within the last 8 years. We identify major techniques in opinion retrieval and group them into four popular categories. We describe the state-of-the-art techniques for each category and emphasize on their performance and limitations. We then summarize with a performance comparison table for the techniques on different datasets. Finally, we highlight possible future research directions that can help solve existing challenges in opinion retrieval.

Resolving conflicts in knowledge for ambient intelligence

Abstract: Ambient intelligence (AmI) proposes pervasive information systems composed of autonomous agents embedded within the environment who, in orchestration, complement human activity in an intelligent manner. As such, it is an interesting and challenging application area for many computer science fields and approaches. A critical issue in such application scenarios is that the agents must be able to acquire, exchange, and evaluate knowledge about the environment, its users, and their activities. Knowledge populated between the agents in such systems may be contextually dependent, ambiguous, and incomplete. Conflicts may thus naturally arise, that need to be dealt with by the agents in an autonomous way. In this survey, we relate AmI to the area of knowledge representation and reasoning (KR), where conflict resolution has been studied for a long time. We take a look at a number of KR approaches that may be applied: context modelling, multi-context systems, belief revision, ontology evolution and debugging, argumentation, preferences, and paraconsistent reasoning. Our main goal is to describe the state of the art in these fields, and to draw attention of researchers to important theoretical issues and practical challenges that still need to be resolved, in order to reuse the results from KR in AmI systems or similar complex and demanding applications.

Towards next generation coordination infrastructures

Abstract: Coordination infrastructures play a central role in the engineering of multiagent systems. Since the advent of agent technology, research on coordination infrastructures has produced a significant number of infrastructures with varying features. In this paper, we review the the state-of-the-art coordination infrastructures with the purpose of identifying open research challenges that next generation coordination infrastructures should address. Our analysis concludes that next generation coordination infrastructures must address a number of challenges: (i) to become socially aware, by facilitating human interaction within a MAS; (ii) to assist agents in their decision making by providing decision support that helps them reduce the scope of reasoning and facilitates the achievement of their goals; and (iii) to increase openness to support on-line, fully decentralised design and execution. Furthermore, we identify some promising approaches in the literature, together with the research issues worth investigating, to cope with such challenges.

Computational logics and verification techniques of multi-agent commitments: survey

Abstract: Agent communication languages (ACLs) are fundamental mechanisms that enable agents in multi-agent systems to talk, communicate with each other in order to satisfy their individual and social goals in a cooperative and competitive manner. Social approaches are advocated to overcome the shortcomings of ACL semantics delineated by using mental approaches in the figure of agents’ mental notions. Over the last two decades, social commitments have been the subject of considerable research in some of those social approaches as they provide a powerful representation for modeling and reasoning upon multi-agent interactions in the form of mutual contractual obligations. They particularly provide a declarative, flexible, verifiable, and social semantics for ACL messages while respecting agents’ autonomy, heterogeneity, and openness.In this manuscript, we go through prominent and predominate proposals in the literature to explore the state of the art on how temporal logics can be devoted to define a formal semantics for ACL messages in terms of social commitments and associated actions. We explain each proposal and point out if and how it meets seven crucial criteria, four of them introduced by Munindar P. Singh to have a well-defined semantics for ACL messages. Far from deciding the best proposal, our aim is to present the advantages (strengths) and limitations of those proposals to designers and developers using a concrete running example and to compare between them, so that they can make the best choice with regard to their needs. We explore and evaluate current specification languages and different verification techniques that have been discussed within those proposals to, respectively, specify and verify commitment-based protocols. We also investigate logical languages of actions advocated to specify, model, and execute commitment-based protocols in other contributed proposals. Finally, we suggest some solutions that can contribute to address the identified limitations.

A roadmap to pervasive systems verification

Abstract: The complexity of pervasive systems arises from the many different aspects that such systems possess. A typical pervasive system may be autonomous, distributed, concurrent and context based, and may involve humans and robotic devices working together. If we wish to formally verify the behaviour of such systems, the formal methods for pervasive systems will surely also be complex. In this paper, we move towards being able to formally verify pervasive systems and outline our approach wherein we distinguish four distinct dimensions within pervasive system behaviour and utilize different, but appropriate, formal techniques for verifying each one.

Quality-protected folksonomy maintenance approaches: a brief survey

Abstract: Folksonomy gives liberty to its users to freely assign chosen keywords as tags, and this is the main reason behind its popularity. Apart from freedom, this system also reflects the collective intelligence of the crowd. However, this freedom and liberty can degrade quality of the folksonomy. It is required that quality of the folksonomy must remain consistently excellent and does not degrade with the passage of time. This is a survey paper, in which we present a brief survey of the research efforts intended to maintain a quality-protected folksonomy. We have organized our paper by looking at the problem from four aspects namely selection of quality tags, tag management features provided by folksonomy applications, folksonomy cleaning and interoperability of tags across platforms. We conclude our review with some of the interesting research topics, which need to be explored further. Our conclusion will be relevant and beneficial for engineers and designers who aim to design and maintain a quality-protected folksonomy.

Biometric sensors rapid prototyping on field-programmable gate arrays

Abstract: Biometric user authentication in large-scale distributed systems involves passive scanners and networked workstations and databases for user data acquisition, processing, and encryption. Unfortunately, traditional biometric authentication systems are prone to several attacks, such as Replay Attacks, Communication Attacks, and Database Attacks. Embedded biometric sensors overcome security limits of conventional software recognition systems, hiding its common attack points. The availability of mature reconfigurable hardware technology, such as field-programmable gate arrays, allows the developers to design and prototype the whole embedded biometric sensors. In this work, two strong and invasive biometric traits, such as fingerprint and iris, have been considered, analyzed, and combined in unimodal and multimodal biometric sensors. Biometric sensor performance has been evaluated using the well-known FVC2002, CASIA, and BATH databases.

An introduction to reasoning over qualitative multi-attribute preferences

Abstract: Research on preferences has significantly increased in recent years, as it involves not only many subproblems to be investigated, such as elicitation, representation, and reasoning, but has also been the target of different research areas, for example, artificial intelligence and databases. In particular, much work has focused on qualitative preferences, because these are closer to the way people express their preferences in comparison with quantitative preferences. Against this background, a large number of approaches have been proposed, associated with heterogeneous areas, so that these approaches are usually just compared with those of the same area. In response, we present in this paper a survey of approaches to qualitative multi-attribute preference reasoning, covering different research areas. We introduce selected approaches that propose different techniques and algorithms, which take as input qualitative multi-attribute preference statements following a particular structure specified by the approach. We analyse each approach in a systematic way and discuss their commonalities and limitations.

Domain adaptation strategies in statistical machine translation: a brief overview

Abstract: Statistical machine translation (SMT) is gaining interest given that it can easily be adapted to any pair of languages. One of the main challenges in SMT is domain adaptation because the performance in translation drops when testing conditions deviate from training conditions. Many research works are arising to face this challenge. Research is focused on trying to exploit all kinds of material, if available. This paper provides an overview of research, which copes with the domain adaptation challenge in SMT.

Distributed aggregation of heterogeneous Web-based Fine Art Information: enabling multi-source accessibility and curation

Abstract: The sources of information on the Web relating to Fine Art and in particular to Fine Artists are numerous, heterogeneous and distributed. Data relating to the biographies of an artist, images of their artworks, location of the artworks and exhibition reviews invariably reside in distinct and seemingly unrelated, or at least unlinked, sources. While communication and exchange exists, there is a great deal of independence between major repositories, such as museum, often owing to their ownership or heritage. This increases the individuality in the repository’s own processes and dissemination. It is currently necessary to browse through numerous different websites to obtain information about any one artist, and at this time there is little aggregation of Fine Art Information. This is in contrast to the domain of books and music, where the aggregation and re-grouping of information (usually by author or artist/band name) has become the norm. A Museum API (Application Programming Interface), however, is a tool that can facilitate a similar information service for the domain of Fine Art, by allowing the retrieval and aggregation of Web-based Fine Art Information, whilst at the same time increasing public access to the content of a museum’s collection. In this paper, we present the case for a pragmatic solution to the problems of heterogeneity and distribution of Fine Art Data and this is the first step towards the comprehensive re-presentation of Fine Art Information in a more ‘artist-centric’ way, via accessible Web applications. This paper examines the domain of Fine Art Information on the Web, putting forward the case for more Web services such as generic Museum APIs, highlighting this via a prototype Web application known as the ArtBridge. The generic Museum API is the standardisation mechanism to enable interfacing with specific Museum APIs.

Adopting trust and assurance as indicators for the reassignment of responsibilities in multi-agent systems

Abstract: Multi-agent systems have been widely used in the literature, including for the monitoring of distributed systems. However, one of the unresolved issues in this technology remains in the reassignment of the responsibilities of monitoring agents when some of them become unable to meet their obligations. This paper proposes a new approach for solving this problem based on: (a) the gathering of evidence on whether the agent can or cannot fulfil the tasks it has been assigned and (b) the reassignment of the task to alternative agents using their trust level as a selection parameter. A weather station case study is proposed as an instantiation of the proposed model.

A theory of complex adaptive inquiring organizations: application to continuous assurance of corporate financial information

Abstract: Drawing upon the theories of complexity and complex adaptive systems and the Singerian inquiring system from C. West Churchman's seminal work The Design of Inquiring Systems, the study herein develops a systems design theory for continuous auditing systems. The discussion focuses on the two foundational theories, development of the theory of Complex Adaptive Inquiring Organizations (CAIO) and associated design principles for a continuous auditing system supporting a CAIO, and instantiation of the CAIO theory. The instantiation consists of an agent-based model depicting the marketplace for Frontier Airlines that generates an anticipated market share used as an integral component in a mock auditor going concern opinion for the airline. As a whole, the study addresses the lack of an underlying system design theory and comprehensive view needed to build upon and advance the continuous assurance movement and addresses the question of how continuous auditing systems should be designed to produce knowledge – knowledge that benefits auditors, clients, and society as a whole.

Intelligent computing in large-scale systems

Abstract: Intelligent computing in large-scale systems provides systematic methodologies and tools for building complex inferential systems, which are able to adapt, mine data sets, evolve, and act in a nimble manner within major distributed environments with diverse architectures featuring multiple cores, accelerators, and high-speed networks. We believe that the papers presented in this special issue ought to serve as a reference for students, researchers, and industry practitioners interested in the evolving, interdisciplinary area of intelligent computing in large-scale systems. We very much hope that readers will find in this compendium new inspiration and ideas to enhance their own research.