Learning from AI : new trends in database technology

Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

On Ontological Foundations of Conceptual Modelling

Failures in information systems development have frequently been attributed to the application of insufficient or faulty information systems development methods. Some of these methods are concerned with the creation of conceptual models, which are used for multiple purposes throughout the information systems development process. Criticism of conceptual modelling methods usually targets their lacking of theoretical foundations. In response to such criticism, various approaches towards theoretical foundations of conceptual modelling have been proposed. They can be differentiated by the reference disciplines they are drawing on. Though the relevance of the philosophical discipline ontology for data modelling had already been recognised in the late 1950s, approaches that have been built on ontology are a rather recent phenomenon. It was not before 1986 that Wand and Weber commenced working on an ontological foundation of conceptual modelling—drawing on the scientific ontology by Mario Bunge, thus later named Bunge-Wand- Weber (BWW) ontology. Twenty years after its inception, claims regarding the validity of theoretical foundations of conceptual modelling based on the BWW ontology have not been scrutinised yet. In an attempt to open up a debate, a critical review of the BWW ontology and its application in the context of theoretical foundations of conceptual modelling is being offered

Current and Future Challenges in Knowledge Representation and Reasoning

Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade

Special Session on Industry 4.0

No abstract available

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

Design Research and Domain Representation

While diverse theories about the nature of design research have been proposed, they are rarely considered in relation to one another across the broader disciplinary field. Discussions of design research paradigms have tended to use overarching binary models for understanding differing knowledge frameworks. This paper focuses on an analysis of theories of design research and the use of Web 3 and open content systems to explore the potential of building more relational modes of conceptual representation. The nature of this project is synthetic, building upon the work of other design theorists and researchers. A number of theoretical frameworks will be discussed and examples of the analysis and modelling of key concepts and information relationships, using concept mapping software, collaborative ontology building systems and semantic wiki technologies will be presented. The potential of building information structures from content relationships that are identified by domain specialists rather than the imposition of formal, top-down, information hierarchies developed by information scientists, will be considered. In particular the opportunity for users to engage with resources through their own knowledge frameworks, rather than through logically rigorous but largely incomprehensible ontological systems, will be explored in relation to building resources for emerging design researchers. The motivation behind this endeavour is not to create a totalising meta-theory or impose order on the ‘ill structured’ and ‘undisciplined’, domain of design. Nor is it to use machine intelligence to ‘solve design problems’. It seeks to create dynamic systems that might help researchers explore design research theories and their various relationships with one another. It is hoped such tools could help novice researchers to better locate their own projects, find reference material, identify knowledge gaps and make new linkages between bodies of knowledge by enabling forms of data-poesis - the freeing of data for different trajectories. Keywords: Design research; Design theory; Methodology; Knowledge systems; Semantic web technologies.</p