Digital libraries content and quality of services are badly affected by the author name ambiguity problem in the citations and it is considered as one of the hardest problems faced by the digital library researchers. Several techniques have been proposed in the literature for the author name ambiguity problem. In this paper, we reviewed some recently presented author name disambiguation techniques and give some challenges and future research directions. We analyze the recent advancements in this field and classify these techniques into supervised, unsupervised, semi-supervised, graph-based and heuristic-based techniques according to their problem formulation that is mainly used for the author name disambiguation. A few surveys have been conducted to review different techniques for the author name disambiguation. These surveys highlighted only the methodology adopted for author name disambiguation but did not critically review their shortcomings. This survey provides a detailed review of author name disambiguation techniques available in the literature, makes a comparison of these techniques at an abstract level and discusses their limitations.

In the field of knowledge engineering, dissatisfaction with the rapid-prototyping approach has led to a number of more principled methodologies for the contruction of knowledge-based systems. Instead of immediately implementing the gathered and interpreted knowledge in a given implementation formalism according to the rapid-prototyping approach, many such methodologies centre around the notion of a conceptual model: an abstract, implementation independent description of the relevant problem solving expertise. A conceptual model should describe the task which is solved by the system and the knowledge which is required by it. Although such conceptual models more precisely, and operationally as a means for model evaluation. In this paper, we study a number of such formal and operational languages for specifying conceptual models. To enable a meaningful comparison of such languages, we focus on languages which are all aimed at the same underlying conceptual model, namely that from the KADS method for building KBS. We describe eight formal languages for KADS models of expertise, and compare these languages with respect to their modelling primitives, their semantics, their implementations and their applications, Future research issues in the area of formal and operational specification languages for KBS are identified as the result of studying these languages. The paper also contains an extensive bibliography of research in this area.

Constraint logic programming (CLP) is a generalization of logic programming (LP) where unification, the basic operation of LP languages, is replaced by constraint handling in a constraint system. The resulting languages combine the advantages of LP (declarative semantics, nondeterminism, relational form) with the efficiency of constraint-solving algorithms. For some classes of combinatorial search problems, they shorten the development time significantly while preserving most of the efficiency of imperative languages. This paper surveys this new class of programming languages from their underlying theory, to their constraint systems, and to their applications to combinatorial problems.

The paper describes the methodology used to develop a construction domain ontology, taking into account the wealth of existing semantic resources in the sector ranging from dictionaries to thesauri. Given the characteristics and settings of the construction industry, a modular, architecture-centric approach was adopted to structure and develop the ontology. The paper argues that taxonomies provide an ideal backbone for any ontology project. Therefore, a construction industry standard taxonomy was used to provide the seeds of the ontology, enriched and expanded with additional concepts extracted from large discipline-oriented document bases using information retrieval (IR) techniques.

Starting from the pioneering work on Linda and Gamma, coordination models and languages have gone through an amazing evolution process over the years. From closed to open systems, from parallel computing to multi-agent systems and from database integration to knowledge-intensive environments, coordination abstractions and technologies have gained in relevance and power in those scenarios where complexity has become a key factor. In this paper, we outline and motivate 25 years of evolution of coordination models and languages, and discuss their potential perspectives in the future of artificial systems.

The IEEE Standard Upper Ontology (IEEE, 2001) is an effort to create a large, general-purpose, formal ontology. The ontology will be an open standard that can be reused for both academic and commercial purposes without fee, and it will be designed to support additional domain-specific ontologies. The effort is targeted for use in automated inference, semantic interoperability between heterogeneous information systems and natural language processing applications. The effort was begun in May 2000 with an e-mail discussion list, and since then there have been over 6000 e-mail messages among 170 subscribers. These subscribers include representatives from government, academia and industry in various countries. The effort was officially approved as an IEEE standards project in December 2000. Recently a successful workshop was held at IJCAI 2001 to discuss progress and proposals for this project (IJCAI, 2001).

Agent-Oriented Software Engineering (AOSE) is rapidly emerging in response to urgent needs in both software engineering and agent-based computing. While these two disciplines coexisted without remarkable interaction until some years ago, today there is rich and fruitful interaction among them and various approaches are available that bring together techniques, concepts and ideas from both sides. This article offers a guide to the broad body of literature on AOSE. The guide, which is intended to be of value to both researchers and practitioners, is structured according to key issues and key topics that arise when dealing with AOSE: methods and frameworks for requirements engineering, analysis, design, and implementation; languages for programming, communication and coordination and ontology specification; and development tools and platforms.

This paper presents a model of contrastive explanation using structural casual models. The topic of causal explanation in artificial intelligence has gathered interest in recent years as researchers and practitioners aim to increase trust and understanding of intelligent decision-making. While different sub-fields of artificial intelligence have looked into this problem with a sub-field-specific view, there are few models that aim to capture explanation more generally. One general model is based on structural causal models. It defines an explanation as a fact that, if found to be true, would constitute an actual cause of a specific event. However, research in philosophy and social sciences shows that explanations are contrastive: that is, when people ask for an explanation of an event—the fact—they (sometimes implicitly) are asking for an explanation relative to some contrast case; that is, ‘Why P rather than Q?’. In this paper, we extend the structural causal model approach to define two complementary notions of contrastive explanation, and demonstrate them on two classical problems in artificial intelligence: classification and planning. We believe that this model can help researchers in subfields of artificial intelligence to better understand contrastive explanation.

This paper reviews the Zero Intelligence (ZI) methodology for investigating markets. This approach models individual traders, operating within a market mechanism, who behave without strategy, in order to determine the impact of the market mechanism and consequently the effect of trader behaviour. The paper considers the major contributions and models within this area from both the economics and finance communities before examining the strengths and weaknesses of this methodology.

User simulation is an important research area in the field of spoken dialogue systems (SDSs) because collecting and annotating real human–machine interactions is often expensive and time-consuming. However, such data are generally required for designing, training and assessing dialogue systems. User simulations are especially needed when using machine learning methods for optimizing dialogue management strategies such as Reinforcement Learning, where the amount of data necessary for training is larger than existing corpora. The quality of the user simulation is therefore of crucial importance because it dramatically influences the results in terms of SDS performance analysis and the learnt strategy. Assessment of the quality of simulated dialogues and user simulation methods is an open issue and, although assessment metrics are required, there is no commonly adopted metric. In this paper, we give a survey of User Simulations Metrics in the literature, propose some extensions and discuss these metrics in terms of a list of desired features.

Database researchers have recognized that integrating a production rules facility into a database system provides a uniform mechanism for a number of advanced database features including integrity constraint enforcement, derived data maintenance, triggers, protection, version control, and others. In addition, a database system with rule processing capabilities provides a useful platform for large and efficient knowledge-base and expert systems. Database systems with production rules are referred to as active database systems, and the field of active database systems has indeed been active. This paper summarizes current work in active database systems, and suggests future research directions. Topics covered include database rule languages, rule processing semantics, and implementation issues.

This paper describes an approach to ontology negotiation between agents supporting intelligent information management. Ontologies are declarative (data-driven) expressions of an agent's “world”: the objects, operations, facts and rules that constitute the logical space within which an agent performs. Ontology negotiation enables agents to cooperate in performing a task, even if they are based on different ontologies.

Our objective is to increase the opportunities for “strange agents” – that is, agents not necessarily developed within the same framework or with the same contextual operating assumptions – to communicate in solving tasks when they encounter each other on the web. In particular, we have focused on information search tasks.

We have developed a protocol that allows agents to discover ontology conflicts and then, through incremental interpretation, clarification and explanation, establish a common basis for communicating with each other. We have implemented this protocol in a set of Java classes that can be added to a variety of agents, irrespective of their underlying ontological assumptions. We have demonstrated the use of the protocol, through this implementation, in a test-bed that includes two large scientific archives: NASA's Global Change Master Directory and NOAA's Wind and Sea Index.

This paper presents an overview of different methods for resolving ontology mismatches and motivates the Ontology Negotiation Protocol (ONP) as a method that addresses some problems with other approaches. Much remains to be done. The protocol must be tested in larger and less familiar contexts (for example, numerous archives that have not been preselected) and it must be extended to accommodate additional forms of clarification and ontology evolution.

This paper reviews many of the very varied concepts of uncertainty used in AI. Because of their great popularity and generality “parallel certainty inference” techniques, so-called, are prominently in the foreground. We illustrate and comment in detail on three of these techniques; Bayes' theory (section 2); Dempster-Shafer theory (section 3); Cohen's model of endorsements (section 4), and give an account of the debate that has arisen around each of them. Techniques of a different kind (such as Zadeh's fuzzy-sets, fuzzy-logic theory, and the use of non-standard logics and methods that manage uncertainty without explicitly dealing with it) may be seen in the background (section 5).

The discussion of technicalities is accompanied by a historical and philosophical excursion on the nature and the use of uncertainty (section 1), and by a brief discussion of the problem of choosing an adequate AI approach to the treatment of uncertainty (section 6). The aim of the paper is to highlight the complex nature of uncertainty and to argue for an open-minded attitude towards its representation and use. In this spirit the pros and cons of uncertainty treatment techniques are presented in order to reflect the various uncertainty types. A guide to the literature in the field, and an extensive bibliography are appended.

This paper surveys the verification of expert system knowledge bases by detecting anomalies. Such anomalies are highly indicative of errors in the knowledge base. The paper is in two parts. The first part describes four types of anomaly: redundancy, ambivalence, circularity, and deficiency. We consider rule bases which are based on first-order logic, and explain the anomalies in terms of the syntax and semantics of logic. The second part presents a review of five programs which have been built to detect various subsets of the anomalies. The four anomalies provide a framework for comparing the capabilities of the five tools, and we highlight the strengths and weaknesses of each approach. This paper therefore provides not only a set of underlying principles for performing knowledge base verification through anomaly detection, but also a survey of the state-of-the-art in building practical tools for carrying out such verification. The reader of this paper is expected to be familiar with first-order logic.

Distribution of resources within case-based reasoning (CBR) architectures is beneficial in a variety of application contexts. This article briefly discusses some of the approaches that fall under the heading of distributed CBR, and their general impact.

Case-Based Reasoning (CBR) is a fresh reasoning paradigm for the design of expert systems in domains that may not be appropriate for other reasoning paradigms such as model-based reasoning. As a result of this, and because of its resemblance to human reasoning, CBR has attracted increasing interest both from those experienced in developing expert systems and from novices. Although CBR is a relatively new discipline, there are an increasing number of papers and books being published on the subject. In this context, this bibliographic categorization is an accompanying paper to a review of CBR by the same authors. The objective of this paper is to help researchers quickly identify relevant references.

The design task is especially appropriate for applying, integrating, exploring and pushing the boundaries of case-based reasoning. In this paper, we briefly review the challenges that design poses for case-based reasoning and survey research on case-based design ranging from early explorations to more recent work on innovative design. We also summarize the theoretical contributions this research has made to case-based reasoning itself.

The goal of semantic search is to improve on traditional search methods by exploiting the semantic metadata. In this paper, we argue that supporting iterative and exploratory search modes is important to the usability of all search systems. We also identify the types of semantic queries the users need to make, the issues concerning the search environment and the problems that are intrinsic to semantic search in particular. We then review the four modes of user interaction in existing semantic search systems, namely keyword-based, form-based, view-based and natural language-based systems. Future development should focus on multimodal search systems, which exploit the advantages of more than one mode of interaction, and on developing the search systems that can search heterogeneous semantic metadata on the open semantic Web.

Bagging and boosting are two of the most well-known ensemble learning methods due to their theoretical performance guarantees and strong experimental results. Since bagging and boosting are an effective and open framework, several researchers have proposed their variants, some of which have turned out to have lower classification error than the original versions. This paper tried to summarize these variants and categorize them into groups. We hope that the references cited cover the major theoretical issues, and provide access to the main branches of the literature dealing with such methods, guiding the researcher in interesting research directions.

This paper focuses upon the contribution which adequate use of the latest development in IT can make to the enhancement, development and improvement of professional expertise in the construction domain. The paper is based on the author's personal expertise and involvement in several UK- and European-funded research projects. First, the paper gives an overview of the benefits that can be gained from improved information and knowledge management in the construction industry. The latest developments in information and communication technologies are then described and presented as an enabler for effective knowledge management. Finally, the paper reviews the most promising techniques for information and knowledge management in the construction domain, highlighting the advantages and disadvantages of each of them, as well as future directions for knowledge management in construction.