Technologies such as algorithms, applications and formats are an important part of the knowledge produced and reused in the research process. Typically, a technology is expected to originate in the context of a research area and then spread and contribute to several other fields. For example, Semantic Web technologies have been successfully adopted by a variety of fields, e.g., Information Retrieval, Human Computer Interaction, Biology, and many others. Unfortunately, the spreading of technologies across research areas may be a slow and inefficient process, since it is easy for researchers to be unaware of potentially relevant solutions produced by other research communities. In this paper, we hypothesise that it is possible to learn typical technology propagation patterns from historical data and to exploit this knowledge i) to anticipate where a technology may be adopted next and ii) to alert relevant stakeholders about emerging and relevant technologies in other fields. To do so, we propose the Technology-Topic Framework, a novel approach which uses a semantically enhanced technology-topic model to forecast the propagation of technologies to research areas. A formal evaluation of the approach on a set of technologies in the Semantic Web and Artificial Intelligence areas has produced excellent results, confirming the validity of our solution.

Osborne, F., Mannocci, A., & Motta, E. (2017). Forecasting the spreading of technologies in research communities. In Proceedings of the Knowledge Capture Conference, K-CAP 2017. Association for Computing Machinery, Inc [10.1145/3148011.3148030].

Forecasting the spreading of technologies in research communities

Osborne F;
2017

Abstract

Technologies such as algorithms, applications and formats are an important part of the knowledge produced and reused in the research process. Typically, a technology is expected to originate in the context of a research area and then spread and contribute to several other fields. For example, Semantic Web technologies have been successfully adopted by a variety of fields, e.g., Information Retrieval, Human Computer Interaction, Biology, and many others. Unfortunately, the spreading of technologies across research areas may be a slow and inefficient process, since it is easy for researchers to be unaware of potentially relevant solutions produced by other research communities. In this paper, we hypothesise that it is possible to learn typical technology propagation patterns from historical data and to exploit this knowledge i) to anticipate where a technology may be adopted next and ii) to alert relevant stakeholders about emerging and relevant technologies in other fields. To do so, we propose the Technology-Topic Framework, a novel approach which uses a semantically enhanced technology-topic model to forecast the propagation of technologies to research areas. A formal evaluation of the approach on a set of technologies in the Semantic Web and Artificial Intelligence areas has produced excellent results, confirming the validity of our solution.
Si
paper
Scientifica
Ontology; Scholarly Data; Semantic Web; Technology;
English
9th International Conference on Knowledge Capture, K-CAP 2017 - 4 December 2017 through 6 December 2017
978-1-4503-5553-7
Osborne, F., Mannocci, A., & Motta, E. (2017). Forecasting the spreading of technologies in research communities. In Proceedings of the Knowledge Capture Conference, K-CAP 2017. Association for Computing Machinery, Inc [10.1145/3148011.3148030].
Osborne, F; Mannocci, A; Motta, E
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/381531
Citazioni
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 6
Social impact