The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t+1 , using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.

1. Amparo Elizabeth Cano Basave1, Francesco
Osborne2, Angelo Salatino2
1 Aston University, United Kingdom
2 KMi, The Open University, United Kingdom
EKAW 2016
Ontology Forecasting in Scientific Literature:
Semantic Concepts Prediction based on
Innovation-Adoption Priors

2. 22
Osborne, F., Motta, E. and Mulholland, P.
Exploring scholarly data with Rexplore.
International Semantic Web Conference 2013
technologies.kmi.open.ac.uk/rexplore/

3. The Computer Science Ontology 1
• Not fine-grained enough.
– E.g., only 2 topics are classified under Semantic Web
• Static, manually defined, hence prone to get obsolete very
quickly.
3
Standard research areas taxonomies/classifications/ontologies
such as ACM are not apt to the task.
ACM 2012

4. The Computer Science Ontology (CSO) was automatically
created and updated by applying the Klink-2 algorithm.
Osborne, F. and Motta, E.: Klink-2: integrating multiple web sources to generate
semantic topic networks. In ISWC 2015. (2015)
The Computer Science Ontology 2

5. • We automatically generated a version of CSO consisting of about
15,000 topics linked by about 70,000 semantic relationships.
• It included very granular and low level research areas and it can be
regularly updated by running Klink-2 on a new set of publications.
• We also have different versions of CSO obtained by running Klink-2
on the set of documents up to a certain year.
5
The Computer Science Ontology 3
5
CSO 2012 CSO 2013 CSO 2014 CSO 2015
[…]

6. A shared conceptualization
“Ontologies are a formal, explicit specification of a shared
conceptualization” (Studer et al., 1998)
“The conceptualization should express a shared view between
several parties, a consensus rather than an individual view“
(Guarino at al, 2009)
“Ontologies are us: inseparable from the context of the
community in which they are created and used.” (Mika, 2005)
“Ontology Evolution is the timely adaptation of an ontology to
the arisen changes and the consistent propagation of these
changes to dependent artefacts.” (Stojanovic, 2004)
6

7. But what if we cannot wait for shared consensus?
These ontologies reflect the past, and can only contain concepts
that are already popular enough to be selected by experts or
automatic methods.
Hence, they hardly support tasks which involve the ability to
describe emerging concepts, e.g.:
• Exploring the forefront of research;
• Trend detection;
• Horizon scanning;
• Producing smart analytics to inform business decision.
77

8. Ontology Forecasting
Given an ontology in time t, a team of experts and/or a software
consider a number of relevant knowledge sources and update
the ontology by also including new concepts on which there will
be (probably) a shared consensus in time t+1.
For example, a forecasted ontology of research topics in 2000 may already
include a new topic associated to the dynamics preluding to the “Semantic
Web” (new collaborations between Knowleged Base Systems, AI and WWW)
8
[…]
t-n t-1 t t+1

9. Contributions – a first step towards ontology forecasting
1. We approach the novel task of ontology forecasting by
predicting semantic concepts in the research domain.
2. We introduce metrics to analyse the linguistic and semantic
progressiveness in scholarly data.
3. We propose Semantic Innovation Forecast (SIF) a novel
weakly-supervised approach for the forecasting of emerging
semantic concepts.
4. We evaluate our approach in a dataset of over 1 million
documents in the Computer Science domain.
– The proposed framework offers competitive boosts in mean average
precision at ten for forecasts over 5 years.
9

10. Scopus (Computer Science) - # of publications
10
0
50000
100000
150000
200000
250000
1 9 9 5 1 9 9 7 1 9 9 9 2 0 0 1 2 0 0 3 2 0 0 5 2 0 0 7
NUMBE R OF ARTICLES
YEAR

11. Scopus (Computer Science) – vocabulary size
11
0
20000
40000
60000
80000
100000
120000
140000
160000
1 9 9 5 1 9 9 7 1 9 9 9 2 0 0 1 2 0 0 3 2 0 0 5 2 0 0 7
VOCABULARY SIZE
YEAR

12. Klink-2 Computer Science Ontology - # of classes
12

13. Linguistic Progressiveness
Language innovation in a corpus refers to the introduction of
novel patterns of language.
We generate a language model per year using Katz back-off
smoothing language model and analyzed differences between
consecutive years by using the perplexity metric.
13
0
2E+10
4E+10
6E+10
8E+10
1E+11
1.2E+11
1.4E+11
1 9 9 5 1 9 9 7 1 9 9 9 2 0 0 1 2 0 0 3 2 0 0 5 2 0 0 7
PERPLEXITY
YEAR

14. Linguistic Progressiveness
We also perform a progressive analysis based on lexical
innovation and lexical adoption.
A large number of new words appear each year, but only few of
them are adopted (i.e., still used in the following year).
14
0
10000
20000
30000
40000
50000
60000
70000
1 9 9 7 1 9 9 9 2 0 0 1 2 0 0 3 2 0 0 5 2 0 0 7
NUMBER OF WORDS
YEAR
# of new words per year
# of adopted words per year

15. Measure Linguistic Progressiveness
15
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
1 9 9 7 1 9 9 9 2 0 0 1 2 0 0 3 2 0 0 5 2 0 0 7
LINGUISTIC PROGRESSIVENESS
YEAR
We introduce the linguistic progressiveness metric:
𝑳𝑷 𝒕 =
𝑳𝑨 𝒕
𝑳𝑰 𝒕

16. Innovation-Adoption Priors
We assume that emerging topics will be associated with novel
words, thus we compute priors in time t by considering
innovative (LI) and adopted words (LA).
A word prior is a probability distribution that expresses a word
relevance to - in this case - being characteristic of innovative
topics.
We build the prior matrix by assigning a weight to each term in
this vocabulary.
– 0.7 if w ∈ LIt−2 and 0.9 if w ∈ LAt−1. Because our analysis shows that
recently adopted words (LA) are more often associated with emerging
topics than new words (LI).
16

17. Semantic Innovation Forecast (SIF) model
SIF is a generative probabilistic topic model that takes in input a
set of documents at year t and a set of historical priors and
forecast topic word distributions representing new concepts in
the ontology Ot+1.
17

18. Semantic Innovation Forecast (SIF) model
18
We use Collapsed Gibbs Sampling to infer the model parameters
and topic assignments for a corpus at year t + 1 given observed
documents at year t.

19. Evaluation
We perform this task by applying our framework on the Scopus
dataset for Computer Science ( > 1M publications).
Each collection of documents in a year is randomly partitioned
into three subsets: 20% is used to derive innovation priors, 40%
training set, 40% testing set.
We train a SIF model on year t using innovative priors computed
for the two previous years (t-1 and t-2) and we use the SIF
model to forecast semantic concepts at year t + 1.
We then measure compute the cosine similarity between the
predicted semantic concepts for t + 1 and the gold standard
concepts for that year. We consider a concept correctly
forecasted if the similarity with a GS concept is higher than 0.5. 19

20. Evaluation - Baselines
We compare SIF against four baselines. For a year t forecasting
for year t + 1:
1. LDA Topics (LDA) on the full training set. This setting makes
no assumption over innovative/adopted lexicons.
2. LDA Innovative Topics (LDA-I); computes topics based on
documents containing at least one word appearing in LIt.
3. LDA Adopted Topics (LDA-A); computes topics based only
on documents containing at least one word appearing in LAt.
4. LDA Innovation/Adoption Topics (LDA-IA); computes topics
based only on documents containing at least one word
appearing in LIt or LAt.
20

21. Evaluation - Mean Average Precision @ 10
21
Year SIF LDA LDA-A LDA-I LDA-IA
2000 0.70 0.12 0.48 0 0.41
2002 0.87 0 0.82 0.64 0.75
2004 0.91 0 0.58 0.57 0.63
2006 0.87 0.31 0.78 0.84 0.69
2008 0.99 0.40 0.68 0.57 0.70
AVG 0.87 0.17 0.67 0.52 0.64

22. Conclusion
It is possible to forecast reliably emerging semantic
concepts if the ontology is associated with a large
collection of document.
The next challenge is to forecast new version of an
ontology, that is to produce an ontology that includes
all concepts and relationships that will be (probably)
included in the new version.
22

23. Future works
• Integration of explicit and latent semantics;
• Including graph-structure information into the
model;
• Understanding how research topics are created and
forecast topic trends.
23
Salatino, A.A., Osborne, F., Motta, E. (2016) How are topics
born? Understanding the research dynamics preceding the
emergence of new areas. PeerJ Preprints

24. Francesco Osborne Angelo SalatinoAmparo Cano Basave
Elizabeth Cano-Basave, A. E., Osborne, F., Salatino, A.A.
(2016) Ontology Forecasting in Scientific Literature: Semantic
Concepts Prediction based on Innovation-Adoption Priors.
EKAW 2016, Bologna, Italy
Email: francesco.osborne@open.ac.uk
Twitter: FraOsborne
Site: people.kmi.open.ac.uk/francesco