Metasynthetic computing for solving open complex problems

Complex systems, in particular, open complex giant systems have become one of major challenges to many current disciplines such as system sciences, cognitive sciences, intelligence sciences, computer sciences, and information sciences. An appropriate methodology for dealing with them is the theory of qualitative-to-quantitative metasynthesis. From the perspective of engineering, we propose the concept of metasynthetic computing. This paper discusses the theoretical frame-work, problem-solving process and intelligence emergence of metasynthetic computing from both engineering and cognition perspectives. These efforts can help one understand complex systems and design effective problem-solving systems. © 2008 IEEE

Intelligence metasynthesis and knowledge processing in intelligent systems

Intelligence and Knowledge play more and more important roles in building complex intelligent systems, for instance, intrusion detection systems, and operational analysis systems. Knowledge processing in complex intelligent systems faces new challenges from the increased number of applications and environment, such as the requirements of representing domain and human knowledge in intelligent systems, and discovering actionable knowledge on a large scale in distributed web applications. In this paper, we discuss the main challenges of, and promising approaches to, intelligence metasynthesis and knowledge processing in open complex intelligent systems. We believe (1) ubiquitous intelligence, including data intelligence, domain intelligence, human intelligence, network intelligence and social intelligence, is necessary for OCIS, which needs to be meta-synthesized; and (2) knowledge processing should pay more attention to developing innovative and workable methodologies, techniques, tools and systems for representing, modelling, transforming, discovering and servicing the uncertain, large-scale, deep, distributed, domain-oriented, human-involved, and actionable knowledge highly expected in constructing open complex intelligent systems. To this end, the meta-synthesis of ubiquitous intelligence is an appropriate way in designing complex intelligent systems. To support intelligence meta-synthesis, m-interaction can play as the working mechanism to form rn-spaces as problem-solving systems. In building such m-spaces, advancement in knowledge processing is necessary. © J.UCS

Reinventing the Social Scientist and Humanist in the Era of Big Data

This book explores the big data evolution by interrogating the notion that big data is a disruptive innovation that appears to be challenging existing epistemologies in the humanities and social sciences. Exploring various (controversial) facets of big data such as ethics, data power, and data justice, the book attempts to clarify the trajectory of the epistemology of (big) data-driven science in the humanities and social sciences

A Personal Research Agent for Semantic Knowledge Management of Scientific Literature

The unprecedented rate of scientific publications is a major threat to the productivity of knowledge workers, who rely on scrutinizing the latest scientific discoveries for their daily tasks. Online digital libraries, academic publishing databases and open access repositories grant access to a plethora of information that can overwhelm a researcher, who is looking to obtain fine-grained knowledge relevant for her task at hand. This overload of information has encouraged researchers from various disciplines to look for new approaches in extracting, organizing, and managing knowledge from the immense amount of available literature in ever-growing repositories. In this dissertation, we introduce a Personal Research Agent that can help scientists in discovering, reading and learning from scientific documents, primarily in the computer science domain. We demonstrate how a confluence of techniques from the Natural Language Processing and Semantic Web domains can construct a semantically-rich knowledge base, based on an inter-connected graph of scholarly artifacts – effectively transforming scientific literature from written content in isolation, into a queryable web of knowledge, suitable for machine interpretation. The challenges of creating an intelligent research agent are manifold: The agent's knowledge base, analogous to his 'brain', must contain accurate information about the knowledge `stored' in documents. It also needs to know about its end-users' tasks and background knowledge. In our work, we present a methodology to extract the rhetorical structure (e.g., claims and contributions) of scholarly documents. We enhance our approach with entity linking techniques that allow us to connect the documents with the Linked Open Data (LOD) cloud, in order to enrich them with additional information from the web of open data. Furthermore, we devise a novel approach for automatic profiling of scholarly users, thereby, enabling the agent to personalize its services, based on a user's background knowledge and interests. We demonstrate how we can automatically create a semantic vector-based representation of the documents and user profiles and utilize them to efficiently detect similar entities in the knowledge base. Finally, as part of our contributions, we present a complete architecture providing an end-to-end workflow for the agent to exploit the opportunities of linking a formal model of scholarly users and scientific publications

Elucidating the Relationship between Chinese Medicine and Systems Biology: A Multi-Sited Ethnography

Ever since Chinese medicine encountered modern science in the late nineteenth century, the relationship between the two traditions has been extremely one-sided. At best, scientists perceived Chinese medicine as an archive of primitive knowledge from which potentially useful drugs could be extracted. Chinese medicine practitioners themselves, meanwhile, began a long struggle throughout the twentieth century to modernise their medicine with the help of Western theories and technology. At the turn of the twenty-first century, the involvement of systems biologists in Chinese medicine research created a new encounter, however, that, at least in the rhetoric of its actors, promised a very different kind of relationship: a match of two systems brought together by a shared interest in understanding life, health, illness and medicine as intrinsically complex and not amenable to the reductionist approaches of mainstream science. This research empirically investigates the nature of this relationship and how it emerged. It aims to contribute to the contemporary history of Chinese medicine by exploring the relationship between Chinese medicine and systems biology. This thesis argues that a heterogeneous network evolved, which is composed of human and nonhuman actors and their interactions created globally distributed research projects on Chinese medicine and systems biology. For the purpose of this research, a multi-sited ethnography was conducted over a period of eleven months and a literature survey was employed to trace the start and the development of this heterogeneous network. Ethnographic data reveals in four chapters on the rhetoric and perceptions of the actors, their involvement in Chinese medicine research, their laboratory practice, and the networks and political ties, which developed into a heterogeneous network of Chinese medicine and systems biology research. This research concludes that in the 2000s, a heterogeneous network emerged through the shared ideologies of systems thinking and holism. The shared ideologies set the groundwork for systems biologists to engage with Chinese medicine on its own terms, and created scientific practices, co-operation and funding opportunities between Europe and China