This paper describes and compares the most relevant approaches for associating tags with semantics in order to make explicit the meaning of those tags. We identify a common set of steps that are usually considered across all these approaches and frame our descriptions according to them, providing a unified view of how each approach tackles the different problems that appear during the semantic association process. Furthermore, we provide some recommendations on (a) how and when to use each of the approaches according to the characteristics of the data source, and (b) how to improve results by leveraging the strengths of the different approaches.

The paper provides a review of knowledge management (KM) literature with a focus on recent value creation trends of the KM discipline. The review spans a large spectrum of KM research ranging from the ‘soft’ (socio-organizational) to technical dimensions of KM, published in the academic and trade literature. An interpretive stance is adopted so as to provide a holistic understanding and interpretation of organizational KM research and models. Value creation is grounded in the appropriate combination of human network, social capital, intellectual capital, and technology assets, facilitated by a culture of change. It is argued that to be effective organizations need not only to negotiate their migration from a knowledge sharing to a knowledge creation culture, but also to create sustained organizational and societal values. The latter form the foundation of the proposed ‘knowledge value creation’ concept and represent key organizational and societal challenges faced by modern organizations.

This document provides the specification of the Process Interchange Format (PIF) version 1.2. The goal of this work is to develop an interchange format to help automatically exchange process descriptions among a wide variety of business process modelling and support systems such as workflow software, flow charting tools, planners, process simulation systems and process repositories. Instead of having to write ad hoc translators for each pair of such systems each system will only need to have a single translator for converting process descriptions in that system into and out of the common PIF format. Then any system will be able to automatically exchange basic process descriptions with any other system. This document describes the PIF-CORE 1.2, i.e. the core set of object types (such as activities, agents and prerequisite relations) that can be used to describe the basic elements of any process. The document also describes a framework for extending the core set of object types to include additional information needed in specific applications. These extended descriptions are exchanged in such a way that the common elements are interpretable by any PIF translator, and the additional elements are interpretable by any translator that knows about the extensions. The PIF format was developed by a working group including representatives from several universities and companies, and has been used for experimental automatic translations among systems developed independently at three of these sites. This document is being distributed in the hopes that other groups will comment upon the interchange format proposed here, and that this format (or future versions of it) may be useful to other groups as well. The PIF Document 1.0 was released in December 1994, and the current document reports the revised PIF that incorporate the feedback received since then.

Over the last ten years a community that is interested in context has emerged. Brézillon (1999) gave a survey of the literature on context in artificial intelligence. There is now a series of conferences on context, a website and a mailing list. The number of web pages with the word “context” has increased tenfold in the last five years. Being among the instigators of the use of context in real-world applications, I present in this paper the evolution of my thoughts over the last years and the results that have been obtained, including a representation formalism based on contextual graphs and the use of this formalism in a real-world application called SART. I present how procedures, practices and context are intertwined, as identified in the SART application and in different domains. I root my view of context in the artificial intelligence area and give a general presentation of my view of context under the three aspects – external knowledge, contextual knowledge and proceduralised context – with the implementation of this view in contextual graphs. I discuss how reasoning is carried out, based on procedure and practices, in the formalism of contextual graphs and show how incremental acquisition of practices is integrated in this formalism.

Diagnosis was among the first subjects investigated when digital computers became available. It still remains an important research area, in which several new developments have taken place in the last decade. One of these new developments is the use of detailed domain models in knowledge-based systems for the purpose of diagnosis, often referred to as “model-based” diagnosis. Typically, such models embody knowledge of the normal or abnormal structure and behaviour of the modelled objects in a domain. Models of the structure and workings of technical devices, and causal models of disease processes in medicine are two examples. In this article, the most important notions of diagnosis and their formalisation are reviewed and brought in perspec tive. In addition, attention is focused on a number of general frameworks of diagnosis, which offer sufficient flexibility for expressing several types of diagnosis.

Optimization and constraint satisfaction methods are complementary to a large extent, and there has been much recent interest in combining them. Yet no generally accepted principle or scheme for their merger has evolved. We propose a scheme based on two fundamental dualities: the duality of search and inference, and the duality of strengthening and relaxation. Optimization as well as constraint satisfaction methods can be seen as exploiting these dualities in their respective ways. Our proposal is that rather than employ either type of method exclusively, one can focus on how these dualities can be exploited in a given problem class. The resulting algorithm is likely to contain elements from both optimization and constraint satisfaction, and perhaps new methods that belong to neither.

There is a growing awareness in the multiagent systems research community that the environment plays a prominent role in multiagent systems. Originating from research on behavior-based agent systems and situated multiagent systems, the importance of the environment is now gradually being accepted in the multiagent system community in general. In this paper, we put forward the environment as a first-order abstraction in multiagent systems. This position is motivated by the fact that several aspects of multiagent systems that conceptually do not belong to agents themselves should not be assigned to, or hosted inside the agents. Examples are infrastructure for communication, the topology of a spatial domain or support for the action model. These and other aspects should be considered explicitly. The environment is the natural candidate to encapsulate these aspects. We elaborate on environment engineering, and we illustrate how the environment plays a central role in a real-world multiagent system application.

The automatic assessment of students’ free-text answers has recently received much attention, due to the necessity of exploring and taking advantage of new and more complex computer-based assessment methods. In this paper, a review of the state-of-art of the field is presented, focusing on the techniques that underpin these systems and their evaluation metrics. Although there is still a long way to go so as to reach the ideal system, the fact that the existing systems are already being used commercially and as a second opinion in exams such as GMAT proves the uptake of this field.

We briefly examine case-based planning starting with the seminal work of Hammond. Derivational analogy represents an important shift of technical emphasis that helped mature the techniques. The choice of abstraction level is equally important. We conclude by discussing theoretical underpinnings and by providing some pointers to current directions.

This paper presents an Agent-based Process Management System (APMS) architecture that combines recent developments in autonomous agent technology and distributed computing platforms. It argues that a service-oriented view is required in order to meet the requirements of open distributed enterprises, now and in the future. This approach embraces and fully supports the decentralisation of responsibility and local autonomy, within and across enterprises. The paper begins by summarising existing approaches to workflow. It describes the APMS approach which extends workflow with the abilities to anticipate process requirements, to resource processes dynamically, and to adapt to exceptions. An important feature of APMS is the absence of a centralised business process representation. Finally, the paper describes an implementation of the APMS architecture, and discusses the engineering issues related to realising such a system within a large organisation.

Case-based reasoning (CBR) systems reason from experience: they solve new problems by retrieving relevant prior cases and adapting them to fit new situations. In 1988 the first case-based reasoning workshop, sponsored by DARPA, identified theoretical foundations and fundamental issues for case-based reasoning research. Since then, much investigation has examined the CBR process itself, the validity of CBR as a cognitive model, and the application of CBR technology. The results of that work include refinements in theories of the case-based reasoning process, psychological evidence for human case-based reasoning, and the fielding of over 100 CBR applications.

In this paper, we identify and analyse a set of characteristics that increasingly distinguish today's complex software systems from ‘‘traditional” ones. Several examples in different areas show that these characteristics are not limited to a few application domains but are widespread. Then, we discuss how these characteristics are likely to impact dramatically on the very way software systems are modelled and engineered. In particular, we appear to be on the edge of a radical shift of paradigm, which is about to change our very attitudes in software systems modelling and engineering.

Semantic relatedness (SR) is a form of measurement that quantitatively identifies the relationship between two words or concepts based on the similarity or closeness of their meaning. In the recent years, there have been noteworthy efforts to compute SR between pairs of words or concepts by exploiting various knowledge resources such as linguistically structured (e.g. WordNet) and collaboratively developed knowledge bases (e.g. Wikipedia), among others. The existing approaches rely on different methods for utilizing these knowledge resources, for instance, methods that depend on the path between two words, or a vector representation of the word descriptions. The purpose of this paper is to review and present the state of the art in SR research through a hierarchical framework. The dimensions of the proposed framework cover three main aspects of SR approaches including the resources they rely on, the computational methods applied on the resources for developing a relatedness metric, and the evaluation models that are used for measuring their effectiveness. We have selected 14 representative SR approaches to be analyzed using our framework. We compare and critically review each of them through the dimensions of our framework, thus, identifying strengths and weaknesses of each approach. In addition, we provide guidelines for researchers and practitioners on how to select the most relevant SR method for their purpose. Finally, based on the comparative analysis of the reviewed relatedness measures, we identify existing challenges and potentially valuable future research directions in this domain.

This article revises the state of the art of the application of agent technology within the scope of surveillance systems. Thus, the potential of the practical use of the concepts and technologies of the agent paradigm can be identified and evaluated in this domain. Current surveillance systems are noted for using several devices, heterogeneous in many instances, distributed along the observed scenario, while incorporating a certain degree of intelligence to alert the operator proactively to what is going on in the observed scenario and prevent the operator from having to observe the monitors continuously. The basic characteristics of the agents (autonomy, reactivity, proactiveness and social ability), along with multiagent systems’ characteristics (distributed data management, low coupling, robustness, communication and coordination between autonomous entities), suggest that the agency is a good choice for solving problems which appear and are dealt with in surveillance systems.

During recent years, the development of new techniques for constraint satisfaction, planning, and scheduling has received increased attention, and substantial effort has been invested in trying to exploit such techniques to find solutions to real-life problems. In this paper, we present a survey on constraint satisfaction, planning, and scheduling from the Artificial Intelligence point of view. In particular, we present the main definitions and techniques, and discuss possible ways of integrating such techniques. We also analyze the role of constraint satisfaction in planning and scheduling, and hint at some open research issues related to planning, scheduling, and constraint satisfaction.

As computer scientists, our goals are motivated by the desire to improve computer systems in some way: making them easier to design and implement, more robust and less prone to error, easier to use, faster, cheaper, and so on. In the field of multi-agent systems, our goal is to build systems capable of flexible autonomous decision making, with societies of such systems cooperating with one-another. There is a lot of formal theory in the area but it is often not obvious what such theories should represent and what role the theory is intended to play. Theories of agents are often abstract and obtuse and not related to concrete computational models.

Recently, the number of ontology matching techniques and systems has increased significantly. This makes the issue of their evaluation and comparison more severe. One of the challenges of the ontology matching evaluation is in building large-scale evaluation datasets. In fact, the number of possible correspondences between two ontologies grows quadratically with respect to the numbers of entities in these ontologies. This often makes the manual construction of the evaluation datasets demanding to the point of being infeasible for large-scale matching tasks. In this paper, we present an ontology matching evaluation dataset composed of thousands of matching tasks, called TaxME2. It was built semi-automatically out of the Google, Yahoo, and Looksmart web directories. We evaluated TaxME2 by exploiting the results of almost two-dozen of state-of-the-art ontology matching systems. The experiments indicate that the dataset possesses the desired key properties, namely it is error-free, incremental, discriminative, monotonic, and hard for the state-of-the-art ontology matching systems.

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.

Many solutions to AI problems require the task to be represented in one of a multitude of rigorous mathematical formalisms. The construction of such mathematical models forms a difficult problem which is often left to the user of the problem-solver. This void between problem-solvers and their problems is studied by the eclectic field of automated modelling. Within this field, compositional modelling, a knowledge-based methodology for system-modelling, has established itself as a leading approach. In general, a compositional modeller organises knowledge in a structure of composable fragments that relate to particular system components or processes. Its embedded inference mechanism chooses the appropriate fragments with respect to a given problem, instantiates and assembles them into a consistent system model. Many different types of compositional modeller exist, however, with significant differences in their knowledge representation and approach to inference. This paper examines compositional modelling. It presents a general framework for building and analysing compositional modellers. Based on this framework, a number of influential compositional modellers are examined and compared. The paper also identifies the strengths and weaknesses of compositional modelling and discusses some typical applications.

This commentary summarizes case-based reasoning (CBR) research applied to image processing. It includes references to the systems, workshops, and landmark publications. Image processing includes a variety of image formats, from computer tomography images to remote sensing and spatial data sets.