Being aware of new research topics is an important asset for anybody involved in the research environment, including researchers, academic publishers and institutional funding bodies. In recent years, the amount of scholarly data available on the web has increased steadily, allowing the development of several approaches for detecting emerging research topics and assessing their trends. However, current methods focus on the detection of topics which are already associated with a label or a substantial number of documents. In this paper, we address instead the issue of detecting embryonic topics, which do not possess these characteristics yet. We suggest that it is possible to forecast the emergence of novel research topics even at such early stage and demonstrate that the emergence of a new topic can be anticipated by analysing the dynamics of pre-existing topics. We present an approach to evaluate such dynamics and an experiment on a sample of 3 million research papers, which confirms our hypothesis. In particular, we found that the pace of collaboration in sub-graphs of topics that will give rise to novel topics is significantly higher than the one in the control group.

1. Detection of Embryonic Research Topics
by Analysing Semantic Topic Networks
Angelo Antonio Salatino, Enrico Motta
@angelosalatino
SAVE-SD @ WWW2016

2. Detecting Topic Trends
• In a recognised research area we can find
two main stages:
– initial stage
– recognised
• Can we intervene before?

3. Hypothesis
• We hypothesise the existence of an earlier
embryonic phase:
– The topic itself has no label, but
– We theorize that they can be detected by
analysing the dynamics of already established
research areas

4. Experiment
• Dataset
– Semantically-enhanced co-occurrence graph
• Selection Phase
– Debutant topics vs. Control group
• Analysis Phase
– Statistical analysis of the two populations

5. Experiment: Dataset
• From the topic network
we selected two groups
of topics:
– debutant group: topics
that made their debut in
the period between
2000 and 2010
– control group: already
existing in the decade
2000-10Semantic Topic Networks using Klink-2 by
Osborne et al. @ ISWC 2015
semantic
web
technology
semantic
web
semantic
web
technologies
ontology
mapping
ontology
matching
case
study
knowledge
management
systems
knowledge
management
system
linked
datum
linked
data
fast
implementation

6. Experiment: Selection Phase
For each testing topic we have:

7. Experiment: Analysis Phase
Clique metric:
• Harmonic
mean
• Arithmetic
mean
Timeline metric:
• Linear regression of the time series
• Difference between the extreme
values
𝛼 slope

8. Findings
• We performed two evaluations over 3 million
publications
• Preliminary Evaluation:
– 2 topics in the debutant group (Semantic Web
and Cloud Computing)
– Tested all the combination of the mentioned
techniques
• Evaluation:
– 50 topic in both debutant and non-debutant group

9. Findings: Preliminary Evaluation
• AM-N: arithmetic mean and the
difference between the two
extreme values;
• AM-CF: arithmetic mean and the
linear interpolation;
• HM-N: harmonic mean and the
difference between the first and
the last values;
• HM-CF: harmonic mean and the
linear interpolation.
Splittedbyyear
p-value = 7.0280•10-12Semantic Web Cloud Computing

10. Findings: Interesting Insights

11. Findings: Evaluation
• We used different
sizes of the subgraph
associated to each
testing topic
p-values ≤ 1.28•10-51

12. Conclusion
• Our findings confirm the initial hypothesis
• Next step:
– Automatic detection of embryonic topics by
analysing the topic network and identify sub-
graps exhibiting such dynamics
– Analyse dynamics in other networks (e.g.,
authors and venues)