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.

1. Francesco Osborne, Andrea Mannocci, Enrico Motta
Knowledge Media Institute, The Open University, United Kingdom
K-CAP 2017
Forecasting the Spreading of Technologies
in Research Communities

2. Standing on the Shoulder of Giants (and Technologies)
• We constantly reuse ideas, technologies, methods and materials.
• Technologies will usually appear in a research community and then spread
to other research areas in the following years.
– e.g., SW technologies were created in the field of AI, KBS, WWW and
then they spread to Information Retrieval, HCI, Biology and so on.
• This process is often inefficient and may take several years.
• Currently there are no methods to predict technology spreading across
research areas.
2

3. Standing on the shoulder of giants (and technologies)
• We constantly reuse ideas, technologies, methods and materials.
• Technologies will usually appear in a research community and then spread
to other research areas in the following years.
– e.g., SW technologies were created in the field of AI, KBS, WWW and
then they spread to Information Retrieval, HCI, Biology and so on.
• This process is often inefficient and may take several years.
• Currently there are no methods to predict technology spreading across
research areas.
How can we improve the technology transfer?
How can we help researchers to track down relevant
technologies?
3

4. Predict Technology Spreading
Technology-Topic Framework (TTF) is a novel approach for
predicting the technologies that will be adopted in a research field.
• It is based on the hypothesis that technologies that exhibit similar
spreading patterns will be adopted by similar communities.
4

5. Input Knowledge Bases
TTF takes as input three knowledge bases:
1) A dataset of research papers, described by means of their titles,
abstracts, and keywords;
– A dump of the Scopus database in the 1990-2013 period, containing
about 16 million papers
5

6. Input Knowledge Bases
TTF takes as input three knowledge bases:
1) A dataset of research papers, described by means of their titles,
abstracts, and keywords;
– A dump of the Scopus database in the 1990-2013 period, containing
about 16 million papers
2) A list of input technologies, associated to the relevant
publications in the research paper dataset;
– 1,118 technologies extracted with TechMiner and from Wikipedia which
appeared in > 10 publications in the Scopus dataset.
– We focused on three categories:
• algorithms/approaches (e.g., Support Vector Machines, Particle Swarm
Optimisation, Latent Semantic Analysis)
• formats (e.g., Rule Interchange Format, OWL 2, Systems Modeling Language)
• applications (e.g., OntoClean, Taverna, Annotea).
6

7. Input Knowledge Bases 2
3) An ontology of research areas, describing topics and their
relationships.
• Computer Science Ontology (CSO), automatically created by Klink-2
algorithm and currently trialled by Springer Nature to classify
proceedings. Includes 15K concepts and 70K relationships.
7
Osborne, F. and Motta, E.: Klink-2: integrating multiple web sources to generate
semantic topic networks. In ISWC 2015. (2015).

8. Generation of Technology-Topic Matrices
• We associated each paper to relevant areas in CSO exploiting
the skos:broaderGeneric and relatedEquivalent relationships.
– e.g., a publication associated with SPARQL will be tagged with topics such
as RDF, Linked Data, SW, WWW, and Computer Science.
• We produced a sequence of matrices that contain the number of
publications of a technology in a topic in a given year.
8

9. Technology Propagation Forecasting
It is treated as ! separate classification problems, one for each
topic.
9

10. Evaluation
• We evaluated TTF on 1,118 technologies and 173 topics in the
field of Computer Science during the 1990-2013 period.
• Two main goals:
– Confirming that it is possible to forecast technology propagation
– Comparing the performance of several ML algorithms
• We tested six ML algorithms: Logistic Regression, Random Forest,
Decision Tree, Support Vector Machine, Neural Network, and
Gradient Boosting.
• Each topic classifier was trained on average on 5,136 240
examples and was evaluated on 679 90 examples.
10

11. Precision
11

12. Recall
12

13. Best research areas - Random Forest - F1 score
13

14. Example of forecasted topics
14

15. Conclusions
• It is possible to forecast technology spreading, at least for
some categories of topics.
• TTF performed well, but it would be interesting to evaluate it
with a larger set of technologies and topics.
• We could use this technology for alerting researchers about
promising new technologies relevant to their research and
shorten the technology transfer time.
15

16. Next Steps
• Collecting data about more technologies to perform larger
scale experiments.
• Creating an ontology of technologies and incorporate it in the
analysis.
• Enriching the forecasting model by considering text generated
features and external knowledge bases.
• Expanding the scope of our work by including other research
fields, such as Biology, Social Science, and Engineering.
16

17. Francesco
Osborne
Andrea
Mannocci
Enrico
Motta
Email: francesco.osborne@open.ac.uk
Twitter: FraOsborne
Site: people.kmi.open.ac.uk/francesco
Osborne, F., Mannocci, A. and Motta, E. (2017)
Forecasting the Spreading of Technologies in Research Communities
K-CAP 2017, Austin, Texas, USA.