This paper is intended to serve as a comprehensive introduction to the emerging field concerned with the design and use of ontologies. We observe that disparate backgrounds, languages, tools, and techniques are a major barrier to effective communication among people, organisations, and/or software systems. We show how the development and implementation of an explicit account of a shared understanding (i.e. an `ontology') in a given subject area, can improve such communication, which in turn, can give rise to greater reuse and sharing, inter-operability, and more reliable software. After motivating their need, we clarify just what ontologies are and what purposes they serve. We outline a methodology for developing and evaluating ontologies, first discussing informal techniques, concerning such issues as scoping, handling ambiguity, reaching agreement and producing de nitions. We then consider the bene ts of and describe, a more formal approach. We re-visit the scoping phase, and discuss the role of formal languages and techniques in the specification, implementation and evaluation of ontologies. Finally, we review the state of the art and practice in this emerging field,

Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption

Requirements analysis includes a preliminary acquisition step where a global model for the specification of the system and its environment is elaborated. This model, called requirements model, involves concepts that are currently not supported by existing formal specification languages, such as goals to be achieved, agents to be assigned, alternatives to be negotiated, etc. The paper presents an approach to requirements acquisition which is driven by such higher-level concepts. Requirements models are acquired as instances of a conceptual meta-model. The latter can be represented as a graph where each node captures an abstraction such as, e.g., goal, action, agent, entity, or event, and where the edges capture semantic links between such abstractions. Well-formedness properties on nodes and links constrain their instances - that is, elements of requirements models. Requirements acquisition processes then correspond to particular ways of traversing the meta-model graph to acquire approp...

of the Hypertext Transfer Protocol and Uniform Resource Identifiers. We describe the software engineering principles guiding REST and the interaction constraints chosen to retain those principles, contrasting them to the constraints of other architectural styles. We then compare the abstract model to the currently

in subsequent simulations, including an attractor network that reproduces the naming latency data directly in its time to settle on a response. Further analyses of the network's ability to reproduce data on impaired reading in surface dyslexia support a view of the reading system that incorporates a graded

of assertions- the Web of Data. In this article we present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. We describe progress to date in publishing Linked Data on the Web, review applications that have been developed

field studies. A set of principles for the conduct and evaluation of interpretive field research in information systems is proposed, along with their philosophical rationale. The usefulness of the principles is illustrated by evaluating three published interpretive field studies drawn from

This is the original version of Principles and Practice, as published in 1982, with only minor changes. It is gratifying to point out that many of the predictions made in this book were confirmed by subsequent research, for example, the superiority of comprehensible-input based methods

The rising abuse of computers and increasing threat to personal privacy through data banks have stimulated much interest m the techmcal safeguards for data. There are four kinds of safeguards, each related to but distract from the others. Access controls regulate which users may enter the system

that performs random testing to simulate the most general environment the program can operate in; and (3) dynamic analysis of how the program behaves under random testing and automatic generation of new test inputs to direct systematically the execution along alternative program paths. Together, these three