Total Technology Space Map as a Digital Platform

A strand of recent studies utilized complete patent databases and classification systems to construct large network maps of patent technology classes, which might approximate the total technology space. It has been argued that such maps are useful for competitive intelligence analysis, technology road mapping, innovation decision support, and so on in the literature. In this paper, we illustrate the InnoGPS system to integrate such a map with various map-based visual analytic functions for technology navigation, positioning, neighborhood exploration, path finding and information retrieval. These analytics are either descriptive, predictive or prescriptive. During the process of developing InnoGPS, we have conceived a wide spectrum of other potential applications of the total technology space map for consumers, business, education and so on. These possibilities together with the difficulty to construct an accurate technology space representation suggest the strategic value to develop the total technology space map as a digital platform for any applications to discover, manage or represent any data, information and knowledge related to technologies, and to nurture an ecosystem of developers and users

Journal Maps, Interactive Overlays, and the Measurement of Interdisciplinarity on the Basis of Scopus Data (1996-2012)

Using Scopus data, we construct a global map of science based on aggregated journal-journal citations from 1996-2012 (N of journals = 20,554). This base map enables users to overlay downloads from Scopus interactively. Using a single year (e.g., 2012), results can be compared with mappings based on the Journal Citation Reports at the Web-of-Science (N = 10,936). The Scopus maps are more detailed at both the local and global levels because of their greater coverage, including, for example, the arts and humanities. The base maps can be interactively overlaid with journal distributions in sets downloaded from Scopus, for example, for the purpose of portfolio analysis. Rao-Stirling diversity can be used as a measure of interdisciplinarity in the sets under study. Maps at the global and the local level, however, can be very different because of the different levels of aggregation involved. Two journals, for example, can both belong to the humanities in the global map, but participate in different specialty structures locally. The base map and interactive tools are available online (with instructions) at http://www.leydesdorff.net/scopus_ovl.Comment: accepted for publication in the Journal of the Association for Information Science and Technology (JASIST

Data-Driven Network Visualization for Innovation and Competitive Intelligence

Technology positions of a firm may determine its competitive advantages and innovation opportunities. While a tangible understanding of the technology positions of a firm, i.e., the set of technologies the firm has mastered, can inform innovation and competitive intelligence, yet such positions are heterogeneous, intangible and difficult to analyze. Herein, we present a data-driven network visualization methodology to locate the knowledge positions of a firm as a subspace of the total technology space for innovation and competitive intelligence analytics. The total technology space is empirically constructed as a network map of all patent technology classes and can be overlaid with the knowledge positions of a firm according to its patent records. This paper demonstrates how to use the system to conduct historical, comparative and predictive analyses of the technology positions of individual and different firms. The methodology has been implemented into a cloud-based data-driven visual analytics system – InnoGPS

A Review of Theory and Practice in Scientometrics

Scientometrics is the study of the quantitative aspects of the process of science as a communication system. It is centrally, but not only, concerned with the analysis of citations in the academic literature. In recent years it has come to play a major role in the measurement and evaluation of research performance. In this review we consider: the historical development of scientometrics, sources of citation data, citation metrics and the “laws" of scientometrics, normalisation, journal impact factors and other journal metrics, visualising and mapping science, evaluation and policy, and future developments

The development of local solar irradiance for outdoor computer graphics rendering

Atmospheric effects are approximated by solving the light transfer equation, LTE, of a given viewing path. The resulting accumulated spectral energy (its visible band) arriving at the observer’s eyes, defines the colour of the object currently on the line of sight. Due to the convenience of using a single rendering equation to solve the LTE for daylight sky and distant objects (aerial perspective), recent methods had opt for a similar kind of approach. Alas, the burden that the real-time calculation brings to the foil had forced these methods to make simplifications that were not in line with the actual world observation. Consequently, the results of these methods are laden with visual-errors. The two most common simplifications made were: i) assuming the atmosphere as a full-scattering medium only and ii) assuming a single density atmosphere profile. This research explored the possibility of replacing the real-time calculation involved in solving the LTE with an analytical-based approach. Hence, the two simplifications made by the previous real-time methods can be avoided. The model was implemented on top of a flight simulator prototype system since the requirements of such system match the objectives of this study. Results were verified against the actual images of the daylight skies. Comparison was also made with the previous methods’ results to showcase the proposed model strengths and advantages over its peers

Embedding Knowledge Graph of Patent Metadata to Measure Knowledge Proximity

Knowledge proximity refers to the strength of association between any two entities in a structural form that embodies certain aspects of a knowledge base. In this work, we operationalize knowledge proximity within the context of the US Patent Database (knowledge base) using a knowledge graph (structural form) named PatNet built using patent metadata, including citations, inventors, assignees, and domain classifications. We train various graph embedding models using PatNet to obtain the embeddings of entities and relations. The cosine similarity between the corresponding (or transformed) embeddings of entities denotes the knowledge proximity between these. We compare the embedding models in terms of their performances in predicting target entities and explaining domain expansion profiles of inventors and assignees. We then apply the embeddings of the best-preferred model to associate homogeneous (e.g., patent-patent) and heterogeneous (e.g., inventor-assignee) pairs of entities