Abstract
Technology analysis is a process which uses textual analysis to detect trends in technological innovation. Co-word analysis (CWA), a popular method for technology analysis, encompasses (1) defining a set of keyword or key phrase patterns which are represented in technology-dependent terms, (2) generating a network that codifies the relations between occurrences of keywords or key phrases, and (3) identifying specific trends from the network. However, defining the set of keyword or key phrase patterns heavily relies on effort of experts, who may be expensive or unavailable. Furthermore defining keyword or key phrase patterns of new or emerging technology areas may be a difficult task even for experts. To solve the limitation in CWA, this research adopts a property-function based approach. The property is a specific characteristic of a product, and is usually described using adjectives; the function is a useful action of a product, and is usually described using verbs. Properties and functions represent the innovation concepts of a system, so they show innovation directions in a given technology. The proposed methodology automatically extracts properties and functions from patents using natural language processing. Using properties and functions as nodes, and co-occurrences as links, an invention property-function network (IPFN) can be generated. Using social network analysis, the methodology analyzes technological implications of indicators in the IPFN. Therefore, without predefining keyword or key phrase patterns, the methodology assists experts to more concentrate on their knowledge services that identify trends in technological innovation from patents. The methodology is illustrated using a case study of patents related to silicon-based thin film solar cells.
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| Scientometrics | |
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