| 51198 |
Creator |
c9aa7f2e582d191ed728ad414c5ea711 |
| 51198 |
Creator |
21e3abf33e3daaa89c07ea7d5da24bb0 |
| 51198 |
Creator |
72695c730cacd1f727d9ea1eed1aa934 |
| 51198 |
Date |
2017-11 |
| 51198 |
Date |
2017-12-04 |
| 51198 |
Is Part Of |
repository |
| 51198 |
Is Part Of |
pc1bf5692b7a99803d0a809572583f15e |
| 51198 |
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. |
| 51198 |
authorList |
authors |
| 51198 |
presentedAt |
ext-d77367f831ca456cfb3b64d689184d01 |
| 51198 |
status |
peerReviewed |
| 51198 |
uri |
http://data.open.ac.uk/oro/document/634259 |
| 51198 |
uri |
http://data.open.ac.uk/oro/document/634260 |
| 51198 |
uri |
http://data.open.ac.uk/oro/document/634261 |
| 51198 |
uri |
http://data.open.ac.uk/oro/document/634262 |
| 51198 |
uri |
http://data.open.ac.uk/oro/document/634263 |
| 51198 |
uri |
http://data.open.ac.uk/oro/document/634264 |
| 51198 |
uri |
http://data.open.ac.uk/oro/document/660605 |
| 51198 |
type |
AcademicArticle |
| 51198 |
type |
Article |
| 51198 |
label |
Osborne, Francesco ; Mannocci, Andrea and Motta, Enrico (2017). Forecasting the
Spreading of Technologies in Research Communities. In: Proceedings of the Knowledge
Capture Conference on - K-CAP 2017, ACM, New York, article no. 1. |
| 51198 |
label |
Osborne, Francesco ; Mannocci, Andrea and Motta, Enrico (2017). Forecasting
the Spreading of Technologies in Research Communities. In: Ninth International Conference
on Knowledge Capture (K-CAP 2017), 4-6 Dec 2017, Austin, Texas, United States.
|
| 51198 |
Publisher |
ext-2af1883e4bbfa0356fcedb366171cb38 |
| 51198 |
Title |
Forecasting the Spreading of Technologies in Research Communities |
| 51198 |
in dataset |
oro |