SAO2Vec: Development of an algorithm for embedding the subject-action-object (SAO) structure using Doc2Vec.

In natural-language processing, the subject-action-object (SAO) structure is used to convert unstructured textual data into structured textual data comprising subjects, actions, and objects. This structure is suitable for analyzing the key elements of technology, as well as the relationships between these elements. However, analysis using the existing SAO structure requires a substantial number of manual processes because this structure does not represent the context of the sentences. Thus, we introduce the concept of SAO2Vec, in which SAO is used to embed the vectors of sentences and documents, for use in text mining in the analysis of technical documents. First, the technical documents of interest are collected, and SAO structures are extracted from them. Then, sentence vectors are extracted through the Doc2Vec algorithm and are updated using word vectors in the SAO structure. Finally, SAO vectors are drawn using an updated sentence vector with the same SAO structure. In addition, document vectors are derived from the document's SAO vectors. The results of an experiment in the Internet of things field indicate that the SAO2Vec method produces 3.1% better accuracy than the Doc2Vec method and 115.0% better accuracy than SAO frequency alone. This proves that the proposed SAO2Vec algorithm can be used to improve grouping and similarity analysis by including both the meanings and the contexts of technical elements.

Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network.

As technological convergence has recently become a mainstream innovation trend, technological opportunities need to be explored in heterogeneous technology fields. Most of the previous convergence studies have taken a retrospective view in measuring the degree of convergence and monitoring the converging trends. This paper proposes a quantitative future-oriented approach to technological opportunity discovery for convergence using patent information. In a future-oriented approach, technological opportunities for convergence are suggested by predicting potential technological knowledge flows (TKFs) between heterogeneous fields. The potential TKFs are predicted by a link prediction method in a directed network, which is suggested in this paper to represent the direction of the predicted TKFs by adapting the concept of bibliographic coupling and edge-betweenness centrality. Converging technological opportunities are proposed as incremental and radical technological opportunities by extracting the potential increased knowledge flow links and emerging knowledge flow links. Moreover, the direction and themes of the predicted potential TKFs are provided as technological opportunities for convergence. As an illustration of the proposed method, the technological opportunities between biotechnology (BT) and information technology (IT) are explored. Firms and researchers can use the proposed method to seek out new technological opportunities from various technologies so that R&D policymakers can plan new R&D projects on technological convergence.