=Paper=
{{Paper
|id=Vol-2475/paper11
|storemode=property
|title=Ontological Analysis of E-News: A Case for Terrorism Domain
|pdfUrl=https://ceur-ws.org/Vol-2475/paper11.pdf
|volume=Vol-2475
|authors=Svetlana Sheremetyeva,Anastasiia Zinoveva
}}
==Ontological Analysis of E-News: A Case for Terrorism Domain==
https://ceur-ws.org/Vol-2475/paper11.pdf
Ontological Analysis of E-News: A Case for Terrorism
Domain
Svetlana Sheremetyeva1, Anastasia Zinoveva2
South Ural State University
1
sheremetyevaso@susu.ru
2
zinovevaaiu@bk.ru
Abstract. This paper reports on an on-going project aimed at developing
a model of multilingual ontological analysis of e-news. The research
methodology is data-driven and involves several interwoven stages directed
from analysis to representation: extraction and semantic classification of lexical
units from three comparable corpora of e-news on terrorism in the English,
French, and Russian languages, construction of a core ontology and its
application to the ontological analysis of terrorist e-news. The development
procedures are described through the terrorist domain case study. Special
attention is paid to ontological concept metrics that can facilitate
disambiguation in lexical-ontological mappings. The findings are illustrated by
applying the developed multilingual model to the ontological analysis of e-news
on terrorism in the French language.
Keywords: ontological analysis, domain ontology, e-news, terrorism
1 Introduction
With the advent of the public Internet, electronic news on terrorism has been a
subject of electronic management as an essential part of counter-terrorism. Nowadays,
most techniques for classification, search, information extraction, question answering,
content analysis, etc. applied to e-news mainly rely on shallow text mining or parsing,
without deep linguistic analysis due to the complexity of the latter. However, as it is
widely recognized, high-quality solutions for information processing tasks can only
be obtained with proper meaning understanding, for which ontological analysis is
recognized to be the most promising [4].
Ontological analysis is defined as the study of content as such, or more
specifically, as the process of eliciting content knowledge on the entities involved in a
certain domain. In practice, ontological analysis consists in mapping lexical units of
textual information into an ontology followed by formalizing and interpreting the
results of such mapping depending on the particular task in question.
Independently of whether ontological analysis is done manually or involves
automation (which is a separate problem), it has serious limitations. The first one is
the availability of an appropriate pre-defined and well-established ontology. Though
___________________________
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tion 4.0 International (CC BY 4.0).
In: P. Sosnin, V. Maklaev, E. Sosnina (eds.): Proceedings of the IS-2019 Conference, Ulyanovsk, Russia,
24-27 September 2019, published at http://ceur-ws.org
131
quite a number of ontological libraries are currently publicly available, their suitabil-
suitability for every particular R&D project involving ontological analysis is, as a
rule, problematic. Therefore, in most works on ontological analysis, the first task (that
can also be the goal of the research) is to build a domain- and/or application-tuned
ontology. The second major limitation lies in the practical realization of ontological
analysis as such with the focus on the bi-directional mapping of the textual elements
with the ontological concepts. The shortcomings here are well-known and pertain to
both the process of ontology building and subsequent application of the ontology to
document analysis. It is the difficulty of clearly specifying the boundaries of the
analysis as well as the limited consideration of relationships between the ontological
concepts. Text elements can be missing in the ontology mapping or one-to-many,
many-to-one or many-to-many relationships exist, which leaves ambiguities
unresolved. Then, the procedure of the ontological analysis initially done by humans
based on objective judgments can influence the results of the analysis [4]. There is no
universal recipe for ideal ontological analysis and, as a rule, in every practical project,
specific approaches are developed to deal with the problems.
In this paper, we describe our experience in developing a model of multilingual
ontological analysis that is data-driven and investigate ontological metrics. We
illustrate our findings by applying the developed model to the ontological analysis of
e-news on terrorism in the French language. The rest of the paper is organized as
follows. Section 2 gives an overview of major trends in ontological analysis of e-
news. Section 3 describes our methodology. Section 4 presents our multilingual
ontological resource tuned to the terrorism domain. In section 5, the workflow of
ontological analysis and ontology metrics findings are described on the example of
French-language e-news on terrorism. We conclude with a summary and future work.
2 Related work
Ontological analysis involves the comparison of unstructured text with ontologies,
followed by the semantic annotation of text elements. The number of works on
ontologies and ontological analysis has drastically increased since 2001 when the
Semantic Web was popularized [2] with its promise of data interoperability at the
semantic level. Quite a number of research projects concentrate on ontology-based
techniques for e-news classification systems. For example, the ePaper system reported
in [11] uses the ontology as a common language for content-based personalized e-
news filtering, while in the NEWS system, ontological knowledge is meant to support
content-based classification in three languages: English, Spanish and Italian [3].
Ontological analysis of terrorism domain that has already acquired a large body of
studies was boosted by proliferation of e-news on terrorism online from thousands of
different sources. The scope of R&D in this field ranges from linguistic and
methodological issues to tools and actual knowledge bases that are mainly
application-specific and focus on certain limited aspects of the domain. For instance,
the PiT (Profiles in Terror) ontology [6] is designed to represent knowledge about the
terrorist network, which includes a set of individuals and organizations, as well as
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numerous relations between them. Another terrorism ontology, AIT (Adversary–
Intent–Target) is designed to predict terror attacks based on data on terrorist
organizations, their intentions, and weapons [12]. In [5], ontological analysis based on
built-in-house Terrorism Ontology for terrorism event extraction from Thai e-news is
described. The development of the core of the RiskTrack domain ontology, which
defines the radicalization indicators and incorporates important information about
existing terrorist organizations and groups, is presented in [1]. The work [7] is devot-
ed to ontology-related research for the prediction of the terrorist threat on the basis of
semantic association acquisition and complex network evolution.
3 Methodology
Our research methodology is data-driven and involves several interwoven stages
directed from analysis to representation. The road map for this research is shown in
Fig. 1. First, the data set of this study — three-language (Russian, French, and
English) corpora of the e-news articles from the web — were acquired and divided
into two parts for training and testing. Next, the terrorism-domain-relevant lexical
items from the training corpora were extracted for semantic classification and
decision on the set of ontology concepts encompassing all the three languages. Then,
the upper-level ontology and representation formalism were decided on followed by
the development of the seed e-news terrorism ontology and lists of lexical items from
the training corpora that map into ontology concepts.
Fig. 1. The road map of e-news ontological analysis
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The seed ontology is applied to the analysis of the testing corpora and is refined in-
to the core ontology for the terrorist e-news. We then calculate language-dependent
ranks of ontology concepts that can be used for semantic tags disambiguation, trend
mining and ontology results interpretation, e.g., for the identification of the terrorism
perception national specificity. We base our research on the following methodological
assumptions:
An ontology is a reusable language-independent source, hence a good
intermediary between multilingual lexicons.
Domain-specific knowledge is an integral part of general world knowledge.
Therefore, a domain ontology should be linked to an upper ontology.
A mixed ontology knowledge acquisition technique is the most appropriate for
our task, as we define key concepts first based on corpus lexical data and then
specify and/or generalize them to obtain more detailed or abstract concepts.
The boundaries of the analysis and the limited consideration of the sets of the
ontological concepts and relationships between them are data-driven.
4 Building ontology for e-news on terrorism
4.1 Data set analysis
Our data set consists of three domain corpora of e-news in the French, English, and
Russian languages ca. 500,000 words each acquired from the web. The scope of
topics covers e-news about terror attacks all over the world. The corpora were further
divided into training and testing corpora.
The acquisition and analysis of the corpora were performed semi-automatically
with the use of built-in-house tools, such as a web crawler, an automatic extractor of
multiword typed expressions [10], and manual application of component analysis,
opposition analysis, and text template analysis, etc., see [9] for the details. We thus
obtained seed sets of multicomponent typed phrases (NPs, VPs, AdjPs, etc.) of up to
ten-component length. The extracted phrases were grouped into semantic classes and
subclasses based on their semantic similarity in the corpora. Note that attributing
some of the phrases to a certain semantic class was purely corpus-based, which was
the case, for example, with the word attaque that does not generally imply a terrorist
meaning without a terrorism-domain attribute, e.g., terroriste. However, in the
corpora, the word attaque alone was frequently used to refer to a terror attack
specifically. This does not exclude lexical ambiguity even in the domain corpora,
which leads to the overlapping lists of different classes. For example, the French
named entity Charlie Hebdo can mean both OBJECT OF ATTACK (its office was
targeted by terrorists in 2015) and SOURCE (it is a weekly newspaper).
This stage of analysis resulted in the set of key object concepts and subconcepts
of the domain with their main attributes and relationships. The pool of concepts and
relations was further augmented by analyzing the corpora with text templates (or
patterns). For instance, in the French-language corpus, the RELATION concept IS-A can
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be detected (though not exclusively) by means of the following text templates: A est
B, B comme A, A et autres B, wherein B stands for a parent concept, while A is a
child concept. The RELATION concept INSTRUMENT can be manifested in the following
French-language templates: attaque / attentat avec / á / au moyen de A, wherein A
stands for a weapon type.
Table 1 shows a fragment of the list of upper-level concepts acquired for the seed
terrorism ontology with their definitions illustrated by French lexemes linked to the
concepts. To give examples of subclasses, the concept TERROR ATTACK was
subdivided into BOMB ATTACK, SUICIDE ATTACK, VEHICLE-RAMMING ATTACK,
ARMED ATTACK, CHEMICAL ATTACK, HOSTAGE-TAKING, PSYCHOLOGICAL PRESSURE,
and ARSON concepts, while the concept CONSEQUENCES was subclassified into
CONSEQUENCES FOR PEOPLE, POSITIVE CONSEQUENCES FOR TERRORISTS, NEGATIVE
CONSEQUENCES FOR TERRORISTS, and DAMAGE FOR BUILDINGS.
For the representation of our ontological knowledge, we decided on the formalism
of the Mikrokosmos ontology [8] and used it as our upper-level ontology following
the Mikrokosmos division of the reality into OBJECTS, EVENTS, and PROPERTIES
(RELATIONS and ATTRIBUTES) linking our domain concepts to the appropriate
Mikrokosmos parent nodes. We also keep concept labels worded in English. The
resulting resource is called the terrorism-domain seed ontology, a fragment of which
is shown in Fig. 2.
Fig. 2. A fragment of the terrorism-domain seed ontology
Based on the lexical-ontological knowledge acquired at this stage, we have
developed a platform with flexible settings that allow knowledge administration and
different analysis depth to automate tagging texts with ontological concepts.
135
Table 1. A fragment of the list of the seed ontology concepts
Concept Definition Lexical examples
ADVERSARY’S Intended activities of a terrorist or a Planification d’attentat
PLANS. terrorist group. terroriste
AGENT The perpetrator of the attack. Terroriste, combattant,
femme kamikaze
ASSUMPTION Assumptions of “good guys” about a Attribué, présumé, suspecté
probable terrorist group behind the
attack or a suspect.
CONSEQUENCES All the results of the terrorist attack, Femme, homme, personne,
such as human victims, destroyed policier turc, terroriste,
objects, terrorists’ destiny, and the blessé, mort, otage, tué,
condition of those. neutralisé
CHARACTER OF The concept indicates whether the Meurtier, sanglant, tuerie,
ATTACK victims of the attack were numerous carnage, assassiné
and one person was the only target.
GOAL OF The goal terrorists are trying to Renverser le
ATTACK achieve by committing the attack. It gouvernement, assassiner
can also be used to indicate the des juifs, causer un grand
reason for the attack as sometimes it nombre de victimes, venger
is hard to distinguish between them. le drame de la ville d’Alep
LOCATION The country, region, city, district, or À environ 5 km du
geographical entity where the attack bâtiment de la police,
took place. Afghanistan, Etat du
Minnesota, frontière
syrienne
MEANS OF The weapons or weapon-like objects Arme à feu, camion,
ATTACK (e. g., a truck) used to commit the ceinture, couteau, véhicule
attack, also functional weapon parts,
such as explosives, bullets, etc.
OBJECT OF The animate or inanimate object the Convoi militaire,
ATTACK attack is directed to, which is hurt or discothèque, école, église,
damaged in the attack. endroit très fréquenté,
femme
SOURCE The sources of the message about Agence de presse Reuters,
the attack, such as newspapers, TV Al-Jazeera, ambulanciers,
channels, news agencies, or autorités israéliennes, CNN
authorities. Türk, témoins
TERRORIST The organization responsible for the Al Qaïda, Daech, Faucons
ORGANIZATION attack or any terrorist organization de la liberté du Kurdistan,
mentioned in the text. talibans
TIME The time and date of the attack. À la veille du Nouvel An,
au cours de la nuit
TYPE OF The type of attack, such as an Acte terroriste, attentat,
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ATTACK explosion, kidnapping, arson, etc. attaque au camion belier,
explosion
4.2 Ontology refinement
At this stage, we automatically tagged the testing part of our corpora with the seed
ontology concept tags and analyzed a list of lexical units left untagged. Terrorism-
related lexical items were further mapped to either the existing ontology concepts or
to new ones that were added to the ontology following the results of the analysis.
Some of the newly added concepts with the examples of French lexical units mapped
to them are shown in Table 2.
Table 2. A fragment of the list of newly added concepts
Concept Definition Lexical examples
CLAIM To claim responsibility for an attack. Revendiquer, prendre la
RESPONSIBILITY responsibilité
DECLARATION To say, to declare, to announce (the Ajouter, citer, commenter,
concept is normally linked to verbs dire, indiquer, rapporter,
and adverbial phrases that mean the selon
transfer of information).
DIRECTION OF To target smth. or smb. Viser, être la cible, cibler,
ATTACK touché
HAVE MEANS To have a weapon or a weapon-like Armé, chargé
OF ATTACK object (the concept is normally
linked to verbal phrases that mean
the process of application of MEANS
OF ATTACK).
NATION The origin of terrorist and victims; it Turc, kurde, russe, franco-
should not be confused with tunisienne, de nationalité
LOCATION, which only covers the française
places where particular attacks were
committed.
OTHER Types of terrorist activities that are Guerre syrienne,
TERRORIST not literary terror attacks, e.g., combattre, financer le
ACTIVITIES terrorism financing, recruiting, terrorisme
involvement in war conflicts, etc.,
but appear sporadically in terrorism
domain e-news and are therefore
considered relevant.
The multilingual e-news terrorism-domain core ontology was thus created, which
currently contains 107 OBJECT and EVENT concepts, 20 RELATION concepts, and 7
ATTRIBUTE concepts. Created also were the terrorism-domain-related lexicons
in French, English, and Russian, mapped to the ontology concepts. Fragments of
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multilingual lexical lists mapped to the TERROR ATTACK concept are shown in Table 3
(absolute frequencies in the corpora are provided in brackets).
The new data obtained at this stage were added to the knowledge base of the
ontological tagging platform.
Table 3. Fragments of multilingual lexical lists mapped to the TERROR ATTACK concept
English (F) French (F) Russian (F)
attack (1218) attentat (2447) теракт (2839)
terrorist attack (202) attentat terroriste (369) стрельба (210)
bombing (136) attaque terroriste (345) террористический акт (179)
terror attack (129) attentat-suicide (128) террористическая атака (93)
act of terrorism (59) fusillade (126) акт терроризма (30)
shooting (39) acte terroriste (110) двойной теракт (21)
gun attack (23) attentat suicide (58) захват заложников (15)
terrorist act (16) attentat à la bombe (37) взрыв бомбы (11)
knife-attack (16) prise des otages (29) поджог (6)
lone-wolf attack (10) acte de terrorisme (16) угон самолета (5)
act of terror (8) attentat à l’explosif (6) атака смертника (3)
terror act (3) agression terroriste (3) акт террора (2)
5 Language-dependent concept ranking
In this section, we describe the feasibility study of the language-dependent concept
ranking procedure as exemplified by its application to the 20,000-wordform French e-
news corpus that was randomly cut out of the initial corpus used to build the core
ontology and the French terrorism-related concept-mapped lexicon. The lexicon, so
far, contains 1,334 lexical units (single words and multicomponent phrases) and 21
high-level concepts of our core ontology, which we at this stage use in our
calculations.
In general, this stage of research was motivated by our hypothesis that the extent,
to which multilingual ontology concepts are used to code (tag) lexical meanings in the
domain texts, differs, and it was worth investigating this issue as applied to every
national corpus. The idea is that these findings might contribute to solving the major
information processing problem, — ambiguity, in particular, concept-mapping
ambiguity that is relevant, e.g., for information extraction or question answering.
The concept-mapping ambiguity problem in the French terrorism domain can be
illustrated by the word policier (police officer) that maps to the following concepts:
OBJECT OF ATTACK: Un policier est tué. (A police officer was killed.)
SOURCE: Selon des policiers, l'homme aurait crié « Allah akbar ». (According to
police, the man shouted “Allahu akbar”.)
COUNTER-TERRORISM: Après les explosions, les autorités ont déployé des policiers.
(After the explosions, the authorities deployed police officers.)
138
AGENT: L'ambassadeur de Russie est assassiné à Ankara par un policier (Russia's
ambassador is assassinated in Ankara by a policeman.)
This means that on tagging, this word will be assigned four concept tags, hence the
need for disambiguation. The straightforward solution will be to use the sentence
context; however, it requires a lot of knowledge and might not always give correct
results. The situation can be improved by means of quantitative parameters. We
attempted just this. The feasibility study corpus was automatically concept-tagged
with the following tags: P = CONSEQUENCES, L = LOCATION, Z = OBJECT OF ATTACK,
T = TYPE OF ATTACK, S = SOURCE, B = TIME, D = DECLARATION, A = AGENT, R =
COUNTER-TERRORISM, U = TERRORIST ORGANIZATION, C = MEANS OF ATTACK, CR =
CLAIM RESPONSIBILITY, N = NATION, DA = DIRECTION OF ATTACK, I = ASSUMPTION,
M = CHARACTER OF ATTACK, O = OTHER, E = OTHER TERRORIST ACTIVITIES, X =
GOAL OF ATTACK, HA = HAVE MEANS OF ATTACK.
Then, the concept frequencies (CF), i.e. the numbers of occurrences of every
concept tag in this corpus, were calculated. Fig. 3 shows the frequency distribution of
the key ontology concepts in the French terrorism corpus.
We then calculated the frequency of concept multitags (the number of words that
were assigned more than one concept tag) and discovered that multiple tags amount to
9.67% of the total concept tag frequency, which shows that the concept ambiguity rate
as applied to the French corpus is quite high. The frequency distribution of multiple
concept tags is shown in Fig. 4 and can directly be used to set priorities when
developing disambiguation procedures.
20.00%
15.00%
Concept Frequency
10.00%
5.00%
0.00%
P L Z T S B D A R U C CR N DA I M O E X HA
Concept
Fig. 3. Distribution of concept frequency in the French corpus; 100% is the total number
of ontology-mapped lexical units in the corpus in question
139
30.00%
25.00%
Multitag Ambiguity Rate
20.00%
15.00%
10.00%
5.00%
0.00%
A‐P
N‐P
Z‐A‐R
A‐N‐P
S‐U
A‐U
A‐P‐T
M‐T
A‐C
Z‐R‐S
Z‐L
L‐S
P‐S
Z‐P‐S
A‐P‐S
Z‐L‐S
R‐S
Z‐P
O‐P
Z‐R
P‐R
E‐R
P‐T
Multitag
Fig. 4. Distribution of the multilingual concept multitags in the French corpus; 100% is
the total number of multitags
To have a larger feature space for finer grain ontology concept ranking and
disambiguation, two more measures were introduced, — the ratio of concept fillers
(RCF) and the concept usage relevancy (CUR). The ratio of concept fillers accounts
for the variety of lexical units mapped into a concept and is calculated as follows:
RCF = n/N,
where n is the number of the ontology-linked unilingual (French in our example)
lexicon items mapped into a particular concept and N is the total number of items in
the ontology-linked unilingual (French in our example) lexicon.
The concept usage relevancy measure in our research is considered to depend on
the ratio of concept fillers and the concept frequency when applied to code (tag) a
lexeme sense in a unilingual concept corpus. The empirical formula we used at the
current stage of research is given below.
CUR = (RCF*10+CF)/T,
where CUR is a concept usage relevancy, RCF is a ratio of concept fillers, CF is
a concept frequency and T is the number of wordforms in a unilingual corpus (French
in our example).
Ranking ontology concepts according to their CUR measure can be helpful in
developing heuristics for concept multitags disambiguation by taking into account the
140
CUR values calculated for every ontological concept as related to a particular unilin-
unilingual corpus. The higher a concept CUR value, the more prioritized its tag can be
in the set of the other ones assigned to the same lexical unit. Table 4 shows the values
of the suggested measures for the concepts whose tags are included in the first three
most frequent multitags shown in Fig. 4. According to the calculated CUR values, the
multitags A-P, Z-P, Z-R-S can most probably be disambiguated as P, P and S,
correspondingly. We are fully aware that much more research should be done in this
direction and in practice a number of different disambiguating parameters might need
to be used, but it follows from our findings that the CUR measure could definitely be
at least one of them.
Table 4. Values of RCF, CF and CUR measures for selected concepts
Concept RCF CF CUR
A (AGENT) 0.03 0.05 1.56
P (CONSEQUENCES) 1.02 0.17 6.68
R (COUNTER-TERRORISM) 0.05 0.04 2.84
S (SOURCE) 1.04 0.10 7.66
Z (OBJECT OF ATTACK) 1.19 0.13 6.47
6 Conclusions
We have presented an ongoing project aimed at the development of an ontological
analysis model for multilingual e-news on terrorism. The research covers the
acquisition of ontological knowledge and its formal representation based on the data
extracted from the multilingual corpus of English, French, and Russian e-news on
terrorism. The proposed methodology for ontology development is based on
extracting multicomponent lexical units from unilingual corpora, grouping them into
semantic classes and using textual templates to enlarge the ontology-related
knowledge. Language-dependent knowledge thus obtained is further accumulated into
a single ontological resource with language-dependent ontology-mapped lexicons.
The methodology can most likely be used on the material of a broader set of
languages that would, of course, include the development of corresponding language-
dependent textual templates.
We have made an attempt to contribute to solving the lexical-concept mapping
ambiguity problem by calculating frequency-related parameters of ontology concepts
as applied to the ontological analysis of a unilingual corpus. Two new quantitative
measures, a ratio of concept fillers and a concept usage relevancy, were introduced.
Our findings show that these measures could definitely be used as at least one of the
disambiguating parameters, though we are fully aware that much more research
should be done in this direction. We, therefore, see it as our future work.
141
We will also proceed with enlarging both the depth and the breadth of the ontology
and the size of language-dependent ontology-mapped lexicons as well as refining the
ontological analysis model.
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