This study proposes an approach for visualizing a knowledge structure, the proposed approach creates a three-dimensional “Research
focused parallelship network”, a “Keyword Co-occurrence Network”, and a two-dimensional knowledge map to facilitate visualization
of the knowledge structure created by journal papers from different perspectives. The networks and knowledge maps can be depicted
differently by choosing different information as the network actor, e.g. author, institute or country keyword, to reflect
knowledge structures in micro-, meso-, and macro-levels, respectively. Technology Foresight is selected as an example to illustrate
the method proposed in this study. A total of 556 author keywords contained in 181 Technology Foresight related papers have
been analyzed. European countries, China, India and Brazil are located at the core of Technology Foresight research. Quantitative
ways of mapping journal papers are investigated in this study to unveil emerging elements as well as to demonstrate dynamics
and visualization of knowledge. The quantitative method provided in this paper shows a possible way of visualizing and evaluating
knowledge structure; thus a computerized calculation is possible for potential quantitative applications, e.g. R&D resource
allocation, research performance evaluation, science map, etc.
Authors:Pei-Chun Lee, Hsin-Ning Su, and Te-Yi Chan
This study proposes an empirical way for determining probability of network tie formation between network actors. In social
network analysis, it is a usual problem that information for determining whether or not a network tie should be formed is
missing for some network actors, and thus network can only be partially constructed due to unavailability of information.
This methodology proposed in this study is based on network actors’ similarities calculations by Vector-Space Model to calculate
how possible network ties can be formed. Also, a threshold value of similarity for deciding whether or not a network tie should
be generated is suggested in this study. Four ontology-based knowledge networks, with journal paper or research project as
network actors, constructed previously are selected as the targets of this empirical study: (1) Technology Foresight Paper
Network: 181 papers and 547 keywords, (2) Regional Innovation System Paper Network: 431 papers and 1165 keywords, (3) Global
Sci-Tech Policy Paper Network: 548 papers and 1705 keywords, (4) Taiwan’s Sci-Tech Policy Project Network: 143 research projects
and 213 keywords. The four empirical investigations allow a cut-off threshold value calculated by Vector-Space Model to be
suggested for deciding the formation of network ties when network linkage information is unavailable.
Authors:Hsin-Ning Su, Carey Ming-Li Chen, and Pei-Chun Lee
This study aims to propose an early precaution method which allows predicting probability of patent infringement as well as evaluating patent value. To obtain the purposes, a large-scale analysis on both litigated patents and non-litigated patents issued between 1976 and 2010 by USPTO are conducted. The holistic scale analysis on the two types of patents (3,878,852 non-litigated patents and 31,992 litigated patents in total) issued by USPTO from 1976 to 2010 has not been conducted in literatures and need to be investigated to allow patent researchers to understand the overall picture of the USPTO patents. Also, by comparing characteristics of all litigated patents to that of non-litigated patents, a precaution method for patent litigation can be obtained. Both litigated patents and non-litigated patents are analyzed to understand the differences between the two types of patents in terms of different variables. It is found that there are statistically significant differences for the two types of patents in the following 11 variables: (1) No. of Assignee, (2) No. of Assignee Country, (3) No. of Inventor, (4) Inventor Country, (5) No. of Patent Reference, (6) No. of Patent Citation Received, (7) No. of IPC, (8) No. of UPC, (9) No. of Claim, (10) No. of Non-Patent Reference, and (11) No. of Foreign Reference. Finally, logistic regression is used for predicting the probability of occurrence of a patent litigation by fitting the 11 characteristics of 3,910,844 USPTO patents to a logistic function curve.