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

Using Ontologies for Semantic Data Integration

While big data analytics is considered as one of the most important paths to competitive advantage of today’s enterprises, data scientists spend a comparatively large amount of time in the data preparation and data integration phase of a big data project. This shows that data integration is still a major challenge in IT applications. Over the past two decades, the idea of using semantics for data integration has become increasingly crucial, and has received much attention in the AI, database, web, and data mining communities. Here, we focus on a specific paradigm for semantic data integration, called Ontology-Based Data Access (OBDA). The goal of this paper is to provide an overview of OBDA, pointing out both the techniques that are at the basis of the paradigm, and the main challenges that remain to be addressed

Tailored retrieval of health information from the web for facilitating communication and empowerment of elderly people

A patient, nowadays, acquires health information from the Web mainly through a “human-to-machine” communication process with a generic search engine. This, in turn, affects, positively or negatively, his/her empowerment level and the “human-to-human” communication process that occurs between a patient and a healthcare professional such as a doctor. A generic communication process can be modelled by considering its syntactic-technical, semantic-meaning, and pragmatic-effectiveness levels and an efficacious communication occurs when all the communication levels are fully addressed. In the case of retrieval of health information from the Web, although a generic search engine is able to work at the syntactic-technical level, the semantic and pragmatic aspects are left to the user and this can be challenging, especially for elderly people. This work presents a custom search engine, FACILE, that works at the three communication levels and allows to overcome the challenges confronted during the search process. A patient can specify his/her information requirements in a simple way and FACILE will retrieve the “right” amount of Web content in a language that he/she can easily understand. This facilitates the comprehension of the found information and positively affects the empowerment process and communication with healthcare professionals

Natural language understanding: instructions for (Present and Future) use

In this paper I look at Natural Language Understanding, an area of Natural Language Processing aimed at making sense of text, through the lens of a visionary future: what do we expect a machine should be able to understand? and what are the key dimensions that require the attention of researchers to make this dream come true

From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness (Part 3)

This third paper locates the synthetic neurorobotics research reviewed in the second paper in terms of themes introduced in the first paper. It begins with biological non-reductionism as understood by Searle. It emphasizes the role of synthetic neurorobotics studies in accessing the dynamic structure essential to consciousness with a focus on system criticality and self, develops a distinction between simulated and formal consciousness based on this emphasis, reviews Tani and colleagues' work in light of this distinction, and ends by forecasting the increasing importance of synthetic neurorobotics studies for cognitive science and philosophy of mind going forward, finally in regards to most- and myth-consciousness

WHAT IS INFORMATION SUCH THAT THERE CAN BE INFORMATION SYSTEMS?

Information systems, as a discipline, is concerned with the generation, storage and transmission of information, generally by technological means. As such, it would seem to be fundamental that it has a clear and agreed conceptualization of its core subject matter – namely “information”. Yet, we would claim, this is clearly not the case. As McKinney and Yoos point out, in a recent survey of the term information within information systems: “This is the IS predicament – using information as a ubiquitous label whose meaning is almost never specified. Virtually all the extant IS literature fails to explicitly specify meaning for the very label that identifies it.” We live in an information age and the vast majority of information (whatever it may be) is made available through a wide range of computer systems and one would expect therefore that information systems would in fact be one of the leading disciplines of the times rather than one that appears to hide itself in the shadows. Governments nowadays routinely utilize many academic experts to advise them in a whole range of areas but how many IS professors ever get asked? So, the primary purpose of this paper is to stimulate a debate within IS to discuss, and try to establish, a secure foundation for the discipline in terms of its fundamental concept – information. The structure of the paper is that we will firstly review the theories of information used (generally implicitly) within IS. Then we will widen the picture to consider the range of theories available more broadly within other disciplines. We will then suggest a particular approach that we consider most fruitful and discuss some of the major contentious issues. We will illustrate the theories with examples from IS

A broad-coverage distributed connectionist model of visual word recognition

In this study we describe a distributed connectionist model of morphological processing, covering a realistically sized sample of the English language. The purpose of this model is to explore how effects of discrete, hierarchically structured morphological paradigms, can arise as a result of the statistical sub-regularities in the mapping between word forms and word meanings. We present a model that learns to produce at its output a realistic semantic representation of a word, on presentation of a distributed representation of its orthography. After training, in three experiments, we compare the outputs of the model with the lexical decision latencies for large sets of English nouns and verbs. We show that the model has developed detailed representations of morphological structure, giving rise to effects analogous to those observed in visual lexical decision experiments. In addition, we show how the association between word form and word meaning also give rise to recently reported differences between regular and irregular verbs, even in their completely regular present-tense forms. We interpret these results as underlining the key importance for lexical processing of the statistical regularities in the mappings between form and meaning