Knowledge management support for enterprise distributed systems

Explosion of information and increasing demands on semantic processing web applications have software systems to their limits. To address the problem we propose a semantic based formal framework (ADP) that makes use of promising technologies to enable knowledge generation and retrieval. We argue that this approach is cost effective, as it reuses and builds on existing knowledge and structure. It is also a good starting point for creating an organisational memory and providing knowledge management functions

Enterprise engineering using semantic technologies

Modern Enterprises are facing unprecedented challenges in every aspect of their businesses: from marketing research, invention of products, prototyping, production, sales to billing. Innovation is the key to enhancing enterprise performances and knowledge is the main driving force in creating innovation. The identification and effective management of valuable knowledge, however, remains an illusive topic. Knowledge management (KM) techniques, such as enterprise process modelling, have long been recognised for their value and practiced as part of normal business. There are plentiful of KM techniques. However, what is still lacking is a holistic KM approach that enables one to fully connect KM efforts with existing business knowledge and practices already in IT systems, such as organisational memories. To address this problem, we present an integrated three-dimensional KM approach that supports innovative semantics technologies. Its automated formal methods allow us to tap into modern business practices and capitalise on existing knowledge. It closes the knowledge management cycle with user feedback loops. Since we are making use of reliable existing knowledge and methods, new knowledge can be extracted with less effort comparing with another method where new information has to be created from scratch

A Case-Based Reasoning Framework for Enterprise Model Building, Sharing and Reusing

Submitted to ECAI'2000 June, Berlin.Enterprise model development is essentially a labour-intensive exercise. Human experts depend heavily on prior experience when they are building new models making it a natural domain to apply Case Based Reasoning techniques. Through the provision of model building knowledge, automatic testing and design guidance can be provided by rule-based facilities. Exploring these opportunities requires us not only to determine which forms of knowledge are generic and therefore re-usable, but also how this knowledge can be used to provide useful model building support. This paper presents our experiences in identifying and classifying the knowledge which exists in IBM's BSDM Business Models and applying AI techniques, CBR and Rule-Based reasoning together with a symbolic simulator, to provide more complete support throughout the enterprise model development life cycle

Formal Support for an Informal Business Modelling Method

Originally published in the International Journal of Software Engineering and Knowledge Engineering, Feb 2000.Business modelling methods are popular but, since they operate primarily in the early stages of software lifecycles, most are informal. This paper describes how we have used a conventional formal notation (first order predicate logic) in combination with automated support tools to replicate the key components of an established, informal, business modelling method: IBM's Business System Development Method (BSDM). We describe the knowledge which we represent formally at each stage in the method and explain how the move from informal to formal representation allows us to provide guidance and consistency checking during the development lifecycle of the model. It also allows us to extend the original method to a model execution phase which is not described in the original informal method. The role of the formal notation in this case is not to provide a formal semantics for BSDM but to provide a framework for sharing the information supplied at different modelling stages and which we can supplement with simple forms of automated analysis

A scientific information extraction dataset for nature inspired engineering

Nature has inspired various ground-breaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices. However, accessing the information captured in scientific biology texts is a time-consuming and hard task that requires domain-specific knowledge. Improving access for outsiders can help interdisciplinary research like Nature Inspired Engineering. This paper describes a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text, such as trade-offs and correlations. The arguments of these relations can be Multi Word Expressions and have been annotated with modifying phrases to form non-projective graphs. The dataset allows for training and evaluating Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers.Comment: Published in Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020). Updated dataset statistics, results unchange