Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Modeling the Structure of MIMO-Agents and Their Interactions Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Towards Automated Identification of Technological Trajectories

Authors : Sergey S. Volkov, Dmitry A. Devyatkin, Ilia V. Sochenkov, Ilya A. Tikhomirov, Natalia V. Toganova

Published in: Artificial Intelligence

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The paper presents a text mining approach to identifying technological trajectories. The main problem addressed is the selection of documents related to a particular technology. These documents are needed to identify a trajectory of the technology. Two different methods were compared (based on word2vec and lexical-morphological and syntactic search). The aim of developed approach is to retrieve more information about a given technology and about technologies that could affect its development. We present the results of experiments on a dataset containing over 4.4 million of documents as a part of USPTO patent database. Self-driving car technology was chosen as an example. The result of the research shows that the developed methods are useful for automated information retrieval as the first stage of the analysis and identification of technological trajectories.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Ontology-Driven Processing of Unstructured Text
next chapter Development of the Lexicon of Argumentation Indicators
Literature
1.
Dosi, G.: Technological paradigms and technological trajectories: a suggested interpretation of the determinants and directions of technical change. Res. Policy 11(3), 147–162 (1982)CrossRef
2.
Liu, X., et al.: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database. J. Am. Soc. Inf. Sci. Technol. 61(6), 1105–1119 (2010)
3.
Niemann, H., Moehrle, M.G., Frischkorn, J.: The use of a new patent text-mining and visualization method for identifying patenting patterns over time: concept, method and test application. Technol. Forecast. Soc. Change 115, 210–220 (2017)CrossRef
4.
Ozcan, S., Islam, N.: Patent information retrieval: approaching a method and analysing nanotechnology patent collaborations. Scientometrics 111(2), 941–970 (2017)CrossRef
5.
Sochenkov, I.V.: Metod sravneniya textov dlya resheniya poiskovo-analiticheskikh zadatch (Text comparison method for solving search and analytical tasks). Intellectualniy poisk informacii (Intelligent information retrieval), vol. 2, pp. 32–43 (2013)
6.
Möller, A., Moehrle, M.G.: Complementing keyword search with semantic search—introducing an iterative semiautomatic method for near patent search based on semantic similarities. Scientometrics 102(1), 77–96 (2015)CrossRef
7.
Korobkin, D.M., et al.: Prior art candidate search on base of statistical and semantic patent analysis. In: Multi Conference on Computer Science and Information Systems 2017, pp. 231–238 (2017)
8.
Alves, T., Rodrigues, R., Costa, H., Rocha, M.: Development of text mining tools for information retrieval from patents. In: Fdez-Riverola, F., Mohamad, M., Rocha, M., De Paz, J., Pinto, T. (eds.) PACBB 2017. AISC, vol. 616, pp. 66–73. Springer, Cham (2017). https://​doi.​org/​10.​1007/​978-3-319-60816-7_​9
9.
Osipov, G., Smirnov, I., Tikhomirov, I., Sochenkov, I., Shelmanov, A.: Exactus expert—search and analytical engine for research and development support. In: Hadjiski, M., Kasabov, N., Filev, D., Jotsov, V. (eds.) Novel Applications of Intelligent Systems. SCI, vol. 586, pp. 269–285. Springer, Cham (2016). https://​doi.​org/​10.​1007/​978-3-319-14194-7_​14CrossRef
10.
Osipov, G.S., et al.: Exactus patent–sistema patentnogo poiska i analiza (Exactus Patent–patent search and analysis system)
11.
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATH
12.
Okamoto, M., Shan, Z., Orihara, R.: Applying information extraction for patent structure analysis. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 989–992. ACM (2017)
13.
Mikolov, T., et al.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
14.
Smirnov, I.V., et al.: Semantic-syntactic analysis of natural languages. Part II. Method for semantic-syntactic analysis of texts. In: Iskusstvenny intellekt i prinyatie resheniy–Artificial Intelligence and Decision Making, vol. 1, pp. 11–24 (2014)
15.
16.
Suvorov, R.E., Sochenkov, I.V.: Opredelenie svyazannosti nauchno-technicheskikh dokumentov na osnove kharakteristiki tematicheskoy znachimosti (Determination of the connectedness of scientific and technical documents based on the characteristics of thematic significance). Iskusstvenniy intellect I prinyatie resheniy (Artificial intelligence and making decisions)
17.
18.
Sochenkov, I.V., Suvorov, R.E.: Servisy polnotekstovogo poiska v informacionno-analiticheskoy sisteme (chast 1) (Full-text search services in the information and analytical system). In: Informatsionnie tekhnologii i vichislitelnie sistemy (information technologies and computing systems), no. 2, p. 69 (2013)
Metadata
Title
Towards Automated Identification of Technological Trajectories
Authors
Sergey S. Volkov
Dmitry A. Devyatkin
Ilia V. Sochenkov
Ilya A. Tikhomirov
Natalia V. Toganova
Copyright Year
2019
Book
Artificial Intelligence

Print ISBN: 978-3-030-30762-2

Electronic ISBN: 978-3-030-30763-9

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-30763-9_12

Premium Partner

    Image Credits