Knowledge Acquisition最新文献

This paper presents a knowledge-based software engineering tool, DASERT (Detection of Anomalies in Software Engineering Requirements Texts), to acquire and validate functional requirements in natural language. The user describes the functional specifications through informal methods, using graphics with comments in natural language. During this elaboration step the system validates the document by processing the comments semantically to detect ambiguities or inconsistencies. To do so it uses natural language processing and knowledge base engineering.

DASERT's kernel is a KL-ONE-like semantic network, which helps the semantic parsing of the comments and their semantic representation. This knowledge base is first initialized by the acquisition of the lexical domain knowledge, then progressively enriched with the domain terminology given by the user and with the requirements knowledge extracted from the user's graphics and texts.

During initialization and enrichment, the network manager validates the knowledge structurally. This ensures the logical consistency of the base which is then checked for inconsistencies and ambiguities specific to the domain of software requirements.

From a software engineering point of view, the originality of DASERT is that it provides a semantic checking of an informal specification by interpreting the natural language comments. From a knowledge acquisition point of view, DASERT allows acquisition from texts to build the kernel of a knowledge base which is then used to guide the semantic parsing of texts during the acquisition of the specification itself. Moreover, the representation formalism provides a unified view of acquisition and validation.

This paper presents the foundations for a methodology for the construction of medical knowledge based systems (KBS). To date, the lack of a methodology that takes into account the typical demands posed by medical environments has hindered the practical application of knowledge technology in medical settings. Our approach views the development process as comprising two activities: the construction of a knowledge level model and the subsequent translation of the model into a computational model in such a way that the connections between the computational structures and their knowledge level counterparts are maintained. The availability of these connections enables a KBS to communicate with domain experts in knowledge level terminology while it can use efficient reasoning techniques for the actual computations. To support the methodology a number of tools have been developed which are described. The approach is illustrated with a scenario in the area of treatment of acute myeloid leukemia.

This paper reports on the development of a realistic knowledge-based application using the MOBAL system. Some problems and requirements resulting from industrial-caliber tasks are formulated. A step-by-step account of the construction of a knowledge base for such a task demonstrates how the interleaved use of several learning algorithms in concert with an inference engine and a graphical interface can fulfill those requirements. Design, analysis, revision, refinement and extension of a working model are combined in one incremental process. This illustrates the balanced cooperative modelling approach. The case study is taken from the telecommunications domain and more precisely deals with security management in telecommunications networks. MOBAL would be used as part of a security management tool for acquiring, validating and refining a security policy. The modeling approach is compared with other approaches, such as KADS and stand-alone machine learning.