Information networks – concept, classification and application

An information network is a structure used for transmitting various forms and types of information. In basic structure, it consists of branches that connect certain nodes. Many scientists and researchers have dealt with the problem of defining an information network, depending on the functional organization and data transmission, and also with the classification of information networks.  Information networks have a very wide application in almost all scientific disciplines. A large number of researches are carried out on the application of information networks (e.g. bisociative, deep information network, heterogeneous information network, and space information network) in the field of medicine for easier detection of diseases, drug development, etc, and other needs to support real-time communication, massive data transmission, and data processing. In accordance with the above mentioned, the aim of this paper is to offer different approaches in defining and classifying general forms of information networks and to notice their wide application in different research disciplines

Embedding a Creativity Support Tool within Computer Graphics Research

We describe the Dr Inventor creativity support tool that aims to support and even enhance the creativity of active research scientists, by discovering un-noticed analogical similarities between publications. The tool combines text processing, lexical analysis and computational cognitive modelling to find comparisons with the greatest potential for a creative impact on the system users. A multi-year corpus of publications is used to drive the creativity of the system, with a central graph matching algorithm being adapted to identify the best analogy between any pair of papers. Dr Inventor has been developed for use by computer graphics researchers, with a particular focus on publications from the SIGGRAPH conference series and it uses this context in three main ways. Firstly, the pragmatic context of creativity support requires the identification of comparisons that are unlike pre-existing information. Secondly, the suggested inferences are assessed for quality within the context of a corpus of graphics publications. Finally, expert users from this discipline were asked to identify the qualities of greatest concern to them, which then guided the subsequent evaluation task

In Search of a Common Thread: Enhancing the LBD Workflow with a view to its Widespread Applicability

Literature-Based Discovery (LBD) research focuses on discovering implicit knowledge linkages in existing scientific literature to provide impetus to innovation and research productivity. Despite significant advancements in LBD research, previous studies contain several open problems and shortcomings that are hindering its progress. The overarching goal of this thesis is to address these issues, not only to enhance the discovery component of LBD, but also to shed light on new directions that can further strengthen the existing understanding of the LBD work ow. In accordance with this goal, the thesis aims to enhance the LBD work ow with a view to ensuring its widespread applicability. The goal of widespread applicability is twofold. Firstly, it relates to the adaptability of the proposed solutions to a diverse range of problem settings. These problem settings are not necessarily application areas that are closely related to the LBD context, but could include a wide range of problems beyond the typical scope of LBD, which has traditionally been applied to scientific literature. Adapting the LBD work ow to problems outside the typical scope of LBD is a worthwhile goal, since the intrinsic objective of LBD research, which is discovering novel linkages in text corpora is valid across a vast range of problem settings. Secondly, the idea of widespread applicability also denotes the capability of the proposed solutions to be executed in new environments. These `new environments' are various academic disciplines (i.e., cross-domain knowledge discovery) and publication languages (i.e., cross-lingual knowledge discovery). The application of LBD models to new environments is timely, since the massive growth of the scientific literature has engendered huge challenges to academics, irrespective of their domain. This thesis is divided into five main research objectives that address the following topics: literature synthesis, the input component, the discovery component, reusability, and portability. The objective of the literature synthesis is to address the gaps in existing LBD reviews by conducting the rst systematic literature review. The input component section aims to provide generalised insights on the suitability of various input types in the LBD work ow, focusing on their role and potential impact on the information retrieval cycle of LBD. The discovery component section aims to intermingle two research directions that have been under-investigated in the LBD literature, `modern word embedding techniques' and `temporal dimension' by proposing diachronic semantic inferences. Their potential positive in uence in knowledge discovery is veri ed through both direct and indirect uses. The reusability section aims to present a new, distinct viewpoint on these LBD models by verifying their reusability in a timely application area using a methodical reuse plan. The last section, portability, proposes an interdisciplinary LBD framework that can be applied to new environments. While highly cost-e cient and easily pluggable, this framework also gives rise to a new perspective on knowledge discovery through its generalisable capabilities. Succinctly, this thesis presents novel and distinct viewpoints to accomplish five main research objectives, enhancing the existing understanding of the LBD work ow. The thesis offers new insights which future LBD research could further explore and expand to create more eficient, widely applicable LBD models to enable broader community benefits.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

Towards Discovery of Subgraph Bisociations

Abstract. The discovery of surprising relations in large, heterogeneous information repositories is gaining increasing importance in real world data analysis. If these repositories come from diverse origins, forming different domains, domain bridging associations between otherwise weakly connected domains can provide insights into the data that are not accomplished by aggregative approaches. In this paper, we propose a first formalization for the detection of such potentially interesting, domaincrossing relations based purely on structural properties of a relational knowledge description.