=Paper=
{{Paper
|id=None
|storemode=property
|title=The Topics They are a-Changing - Characterising Topics with Time-Stamped Semantic Graphs
|pdfUrl=https://ceur-ws.org/Vol-1272/paper_124.pdf
|volume=Vol-1272
|dblpUrl=https://dblp.org/rec/conf/semweb/CanoHA14a
}}
==The Topics They are a-Changing - Characterising Topics with Time-Stamped Semantic Graphs==
https://ceur-ws.org/Vol-1272/paper_124.pdf
The Topics they are a-Changing — Characterising
Topics with Time-Stamped Semantic Graphs
A. Elizabeth Cano,1 Yulan He,2 and Harith Alani1
1
Knowledge Media Institute, Open University, UK
ampaeli@gmail.com, h.alani@open.ac.uk
2
School of Engineering and Applied Science, Aston University, UK
y.he@cantab.net
Abstract. DBpedia has become one of the major sources of structured knowl-
edge extracted from Wikipedia. Such structures gradually re-shape the represen-
tation of Topics as new events relevant to such topics emerge. Such changes make
evident the continuous evolution of topic representations and introduce new chal-
lenges to supervised topic classification tasks, since labelled data can rapidly be-
come outdated. Here we analyse topic changes in DBpedia and propose the use
of semantic features as a more stable representation of a topic. Our experiments
show promising results in understanding how the relevance of features to a topic
changes over time.
Keywords: social media, topic detection, DBpedia, concept drift, feature rele-
vance decay
1 Introduction
Supervised topic classifiers which depend on labelled data can rapidly become outdated
since new information regarding these topics emerge. This challenge becomes apparent
when applying topic classifiers to streaming data like Twitter. The continuous change of
vocabulary – in many cases event-dependent– makes the task of retraining such classi-
fiers with fresh topic-label annotations a costly one. In event-dependent topics not only
new lexical features re-characterise the topic but also existing features can potentially
become irrelevant to the topic (e.g., Jan25 being relevant to violence in the Egyptian
revolution is now less relevant to current representations of the topic violence). In dy-
namic environments the expectation that the progressive feature drifts of topics to be in
the same feature space is not normally met.
The incorporation of new event-data to a topic representation leads to a linguis-
tic evolution of a topic, but also to a change on its semantic structure. To the best of
our knowledge, none of the existing approaches for topic classification using seman-
tic features [4][2][5][7], has focused on the epoch-based transfer learning task. In this
paper we aim to disseminate our work presented in [1] by summarising our proposed
transfer learning approach for the epoch-based topic classification ot tweets. In [1] we
investigate whether the use of semantic features as opposed to lexical features can pro-
vide a more stable representation of a topic. Here we extend our work by representing
cross-epoch settings gain in F-measure for both lexical and semantic feature with in-
fographics. This enables us to highlight the relevance of the studied semantic features
over the lexical ones.
1.1 Evolving Topics
DBpedia is periodically updated to incorporate any additions and modification in Wikipedia.
This enables us to track how specific resources evolve over time, by comparing these re-
sources over subsequent DBpedia editions. For example, changes to the semantic graph
for the concept Barack Obama can be derived from snapshots of this resource’s seman-
tic graph from different DBpedia dumps 3 . E.g., in Figure 1, although some of the triples
remain unchanged in consecutive dumps, new triples provide further information on the
resource.
DBPEDIA
3.8 dbo:wikiPageWikiLink dbp:Budget_Control_Act_of_2011
3.7 dbp:Al-Qaeda skos:subject dbo:wikiPageWikiLink category:United_States_presidential_candidates,_2012
rdfs:subClassOf
3.6 dbo:birthPlace rdf:type
dbp:Hawaii dbp:Barack_Obama yago:PresidentOfTheUnitedStates dbo:Person
Fig. 1. Triples of the Barack Obama resource extracted from different DBpedia dumps (3.6 to
3.8). Each DBpedia dump presents a snapshot in time of factual information of a resource.
Changes regarding a resource are exposed both through new semantic features (i.e
triples) and new lexical features –appearing on changes in a resource’s abstract–. In
DBpedia a topic can be represented by the collection of resources belonging to both
the main topic (e.g. cat:War) and resources (e.g dbp:Combat assessment) belong-
ing to subcategories (e.g. cat:Military operations) of the main Topic. Therefore
a topic’s evolution can be easily tracked by tracking changes in existing and new re-
sources belonging to it.
2 Topic Classification with Time-Stamped Semantic Graphs
In [1], we propose a novel transfer learning [6][3] approach to address the classification
task of new data when the only available labelled data belongs to a previous epoch.
This approach relies on the incorporation of knowledge from DBpedia graphs. This
approach is summarised in Figure 2 and consists of the following stages: 1) Extraction
of lexical and semantic features from tweets; 2) Time-dependent content modelling;
3) Strategy for weighting topic-relevant features with DBpedia; and 4) Construction of
time-dependent topic classifiers based on lexical, semantic and joint features.
Our analysis involves the use of two main feature types: lexical and semantic fea-
tures. The semantic features consist on Class, Property, Category, and Resource. The
semantic feature representation of a document therefore is build upon the collection of
such features derived from the document’s entities mapped to a DBpedia resource. The
mapping targets the available DBpedia dump when the document was generated. In [1],
we proposed different weightening strategies some of which made use of graph prop-
erties of a Topic in a DBpedia graph. Such strategies incorporated statistics of the topic
graph representation considering a DBpedia graph at time t.
2.1 Construction of Time-Dependent Topic Classifiers
We focus on the binary topic classification in epoch-based scenarios, where the classi-
fier that we train on a corpus from epoch t − 1, is tested on a corpus on epoch t. Our
3
The DBpedia dumps correspond to Wikipedia articles at different time periods as fol-
lows: DBp3.6 generated on 2010-10-11; DBpedia 3.7 on 2011-07-22, DBp3.8 on 2012-
06-01, DBp3.9 on late April. DBpedia have them available to download at DBpedia
http://wiki.dbpedia.org/Downloads39
2010 Concept Topic
2010
2011
2013
2011 Enrichment Labelled
2013 Microposts
Resource SemGraph
Backtrack Mapping Snapshots
Build Cross-Epoch
3.6 2010 Topic Classifiers
3.7 2011
DBpedia Semantic
3.8 2013 Feature Weighting
Fig. 2. Architecture for backtrack mapping of resources to DBpedia dumps and deriving topic-
relevance based features for epoch-dependent topic classification.
analysis targeted our hypothesis that, as opposed to lexical features which are situation-
dependent and can change progressively in time, semantic structures – including onto-
logical classes and properties – can provide a more stable representation of a Topic.
Following the proposed weighting strategies the semantic feature representations of
the t − 1 corpus and the t corpus, are both generated from the DBpedia graph available
at t − 1. For example when applying a classifier trained on data from 2010, the feature
space of a target test set from 2011 is computed based on the DBpedia version used
for training the 2010-based classifier. This is in order to simulate the availability of
resources in a DBpedia graph at a given time. The semantic feature f in a document
x is weighted based on the frequency of a semantic feature f in a document x with
Laplace smoothing and the topic-relevance of the feature in the DB t graph:
[Nx (f )DB t + 1
Wx (f )DB t = [ P ] ∗ (WDB t (f ))1/2 (1)
|F | + f 0 ∈F Nx (f 0 )DB t
where Nx (f ) is the number of times feature f appears in all the semantic meta-
graphs associated with document x derived from the DB t graph ; F is the semantic
features’ vocabulary of the semantic feature type and WDB t (f ) is the weighting func-
tion corresponding to the semantic feature type computed based on the DB t graph.
This weighting function captures the relative importance of a document’s semantic fea-
tures against the rest of the corpus and incorporates the topic-relative importance of
these features in the DB t graph.
3 Experiments
We evaluated our approach using two collections: DBpedia and Twitter datasets. The
DBpedia collection comprises four DBpedia dumps (3.6 to 3.9)4 . The Twitter datasets
consist of a collection of Violence-related topics: Disaster Accident, Law Crime and
War Conflict. Each of these datasets comprises three epoch-based collections of tweets,
corresponding to 2010, 2011, and 2013. The Twitter dataset contained 12,000 annotated
tweets 5 . To compare the overall benefit of the use of the proposed weighting strategies
against the baselines on this three topics, we averaged the P, R and F-measure of these
three cross-epoch settings for each topic. Table 1 presents a summarised version of our
results in [1], showing only the best performing features. We can see that in average
the Class-based semantic features improve upon the bag of words (BoW) features in
F measures. This reveals that the use of ontological classes is a more stable option for
the representation of a topic. In order to analyse the differences in gain in F measure
for each topic in each of the examined features we used the radar plots in Figure 3. In
4
General statistics of these dumps are available at http://wiki.dbpedia.org/Downloads39
5
Further information about this dataset is available at [1]
this figure a positive value indicates an improvement on the classifier. While semantic
features improve upon lexical feature in the three topics, the weighted features for re-
source, class and category exhibit a positive improvement on these scenarios. Moreover
the class based features consistently outperform the BoW in all three topics.
B OW Catsf f Catsf g Catj Ressf f Ressf g Resj Clssf f Clssf g Clsj Semsf f Semsf g Semj
P 0.808 0.719 0.784 0.775 0.764 0.775 0.777 0.692 0.691 0.705 0.708 0.751 0.75
R 0.429 0.433 0.434 0.383 0.438 0.426 0.408 0.649 0.638 0.640 0.438 0.373 0.404
F 0.536 0.524 0.550 0.501 0.544 0.529 0.517 0.660 0.658 0.665 0.525 0.490 0.518
Table 1. Average results for the cross-epoch scenarios for all three topics.
BOW BOW
BOW
AllJoint 0.1 CatSFF Disaster_Accident AllJoint 0.2 CatSFF Law_Crime AllJoint 0
CatSFF War_Conflict
0 SemJoint -‐0.1
PropSFF
SemJoint PropSFF SemJoint 0 PropSFF
-0.1 -‐0.2
-0.2 -0.2 ClsJoint -‐0.3
ResSFF
ClsJoint ResSFF ClsJoint ResSFF
-0.3 -0.4 -‐0.4
-0.4
-0.6 -‐0.5
ResJoint -0.5 ClsSFF ResJoint ClsSFF ResJoint ClsSFF
-0.6 -0.8 -‐0.6
-0.7 -1 -‐0.7
PropJoint SemFF PropJoint SemFF PropJoint SemFF
2010-2011
CatJoint AllSFF CatJoint AllSFF 2010-2013 CatJoint AllSFF
2010-2011 2011-2013 2010-2011
AllSFG CatSFG AllSFG CatSFG AllSFG CatSFG
2010-2013 2010-2013
SemSFG PropSFG SemSFG PropSFG
2011-2013 ClsSFG ResSFG SemSFG PropSFG 2011-2013
ClsSFG ResSFG ClsSFG ResSFG
Fig. 3. Summary of performance decays for each feature for each Topic on the three cross-epoch
scenarios.
4 Conclusions
Our results showed that Class-based semantic features are much slower to decay than
other features, and that they can improve performance upon traditional BoW-based clas-
sifiers in cross-epoch scenarios. These results demonstrate the feasibility of the use of
semantic features in epoch-based transfer learning tasks. This opens new possibilities
for the research of concept drift tracking for transfer learning based on existing Linked
Data sources.
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