=Paper=
{{Paper
|id=Vol-2599/CKG2019_paper_1
|storemode=property
|title=Modeling and Contextualizing Claims
|pdfUrl=https://ceur-ws.org/Vol-2599/CKG2019_paper_1.pdf
|volume=Vol-2599
|authors=Katarina Boland,Pavlos Fafalios,Andon Tchechmedjiev,Konstantin Todorov,Stefan Dietze
|dblpUrl=https://dblp.org/rec/conf/semweb/BolandFTTD19
}}
==Modeling and Contextualizing Claims==
https://ceur-ws.org/Vol-2599/CKG2019_paper_1.pdf
Modeling and Contextualizing Claims
Katarina Boland1 , Pavlos Fafalios2 , Andon Tchechmedjiev3 ,
Konstantin Todorov4 , Stefan Dietze1,5
1
GESIS - Leibniz Institute for the Social Sciences, Germany
2
Institute of Computer Science, FORTH-ICS, Greece
3
LGI2P, IMT Mines-Ales, France
4
LIRMM / University of Montpellier / CNRS, France
5
Heinrich-Heine-University Düsseldorf, Germany
katarina.boland@gesis.org, fafalios@ics.forth.gr,
andon.tchechmedjiev@mines-ales.fr, konstantin.todorov@lirmm.fr
stefan.dietze@gesis.org
Abstract. Understanding societal debates on the Web and how they
are impacted by the spread of biased narratives and falsehoods are be-
coming increasingly important issues. The notion of a claim is central
in a number of related studies into fake-news propagation or computa-
tional fact-checking. While the understanding of this notion varies from
one field to another, there are few studies that have focused on the con-
ceptual modeling of claims and their context. We attempt to contribute
to this area by proposing a novel conceptual model for claims and re-
lated notions, such as attitudes, reviews and annotations, that aims to
take into consideration the claims inherent complexity, distinguishing
between their meaning, linguistic representation and context. We pro-
vide an example of an implementation of this model by using established
vocabularies, such as schema.org, Open Annotation and PROV-O, and
discuss the challenges related to this work. 1
Keywords: Claims; Conceptual Modeling; Claim Context; Societal Debates;
Fact-checking
1 Introduction
The spread of biased narratives and falsehoods on the Web and the analyses
of online discourse have become increasingly important issues [1, 13] that led to
a wide range of interdisciplinary research involving a variety of scientific disci-
plines. Such works include investigations, for instance, into the spreading pattern
of false claims on Twitter [13], or the development of computational methods,
such as pipelines for detecting the stance of claim-relevant Web documents [14],
classifying sources of news, such as Web pages, PLDs, users or posts [10], or for
fake news detection [12] and automatic fact-checking [4].
1
Copyright c 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
2 K. Boland, P. Fafalios, A. Tchechmedjiev, K. Todorov, S. Dietze
Whereas techniques for knowledge graph construction and augmentation of-
ten deploy methods strongly related to the aforementioned computational meth-
ods related to claims, e.g., when aiming to verify facts from the Web for aug-
menting knowledge bases [2,15], the notion of a claim is fundamentally different
from the notion of a fact as an atomic assertion in the first-order-logic sense.
This is due to the inherent complexity of a claim, where its interpretation usu-
ally is strongly dependent on its context, such as its source, timing, or location.
Moreover, a claim often carries a variety of intentional or unintended meanings,
where subtle changes in the wording or context can have significant effects on
its validity [3]. Ambiguity also arises with respect to claims involving quotations
(“X reported that Y said Z”), where often fact-checking results remain vague
about what part such a nested claim actually has been validated.
In order to facilitate the advancement of tasks such as claim verification
or fact checking, it is crucial to capture the complexity of a claim in a way
which enables unambiguous interpretation by both humans and machines. How-
ever, both the used terminology and the underlying conceptual models are still
strongly diverging in academic literature (Sect. 2) as well as in the conceptual
models deployed by fact-checking sites.
Therefore, capturing the meaning of a claim requires both the preservation
of the actual claim utterance as natural language text, often carrying a range
of statements and sentiments embedded in complex sentences which are easy
to process by humans but hard to interpret by machines, as well as structured
knowledge about a claim, its context and constituents, which enables machine-
interpretation, discoverability and reuse of claims, for instance, to facilitate re-
search in the aforementioned areas.
This paper makes the following main contributions: i) a conceptual model
and corresponding terminology of claims and their constituents and context,
grounded in both the scientific state-of-the-art in related fields such as argu-
mentation mining as well as the actual practices of representing and sharing
claims on the Web, for instance, as part of fact-checking sites; ii) an RDF imple-
mentation of the proposed conceptual model that uses W3C standards for data
sharing, namely RDFS, and is informed by established vocabularies, such as
schema.org, Open Annotation, and the PROV data model, in order to facilitate
Web-scale sharing, discovery and reuse of claims and their context, for instance
through semi-structured Web page markup or as part of dedicated knowledge
graphs such as ClaimsKG [11].
2 Background
While the analysis of claims plays a crucial role for a number of fields, the
definition of the very concept of a claim is often left to the intuition of the reader.
Existing definitions vary considerably across and also within fields.
Modeling and Contextualizing Claims 3
According to the Oxford English Dictionary, a claim is a statement or asser-
tion that something is the case, typically without providing evidence or proof.2
Platforms dedicated to journalistic fact-checking refer to claims as statements
supported by (a group of) people or organizations that appear newsworthy, sig-
nificant and verifiable.3 An RDFS-based model for such fact-checked claims is
introduced in [11].
In argumentation mining, claims denominate the conclusion of an argument
or the assertion the argument aims to prove [6, 7]. A variety of additional defi-
nitions can be found for specific tasks in other fields like information retrieval,
e.g. a statement formulating a problem together with a concrete solution [8] or
a sentence in a scientific document that relates two entities given in a query [9].
Thus, what is identified as “claim” in a particular work may or may not be
called “claim” in another. While it is the belief of a person about a fact that
is called “claim” in argumentation mining, it is the fact itself that is coined
“claim” in the fact-checking community. Similarly, the belief and opinion about
certain consequences are the argumentative “claim”, while fact-checking may
verify whether the anticipated consequences would indeed follow an action. State-
ments expressing the position of a person towards a proposition or target are not
susceptible to fact-checking (unless the correctness of the quotation is to be ver-
ified) but are a prevalent claim type in argumentation mining. Moreover, what
is used as premise or evidence in an argument is often selected as check-worthy
“claim” by fact-checking sites. Generally, the distinction of argumentative units
such as claims and evidence is based on the statements’ usage in an argument
while fact-checking classifies statements as claims depending primarily on fea-
tures inherent to the statement itself.
In an effort to reconcile these different understandings of the concept of a
“claim”, we propose a model considering requirements from various research
fields. While in argumentation mining, the meaning of a claim in the context of
the current discourse is the significant part, many tasks from the fact-checking
community, e.g. those aiming at matching unchecked statements to fact-checked
claims [5], focus on the surface form. Thus, going beyond the model introduced
in [11], we propose differentiating between the meaning or proposition of a claim
and its utterance, representation and context.
3 Conceptual Model
Overview. We distinguish three main components of a claim, represented by
three central classes: (1) claim proposition, (2) claim utterance, and (3) claim
context. A claim proposition is the meaning of a statement or assertion that
something is the case. It is usually related to a controversial topic and can be
2
https://www.lexico.com/en/definition/claim
3
https://www.truthorfiction.com/about/
https://checkyourfact.com/about-us
https://www.politifact.com/truth-o-meter/article/2018/feb/12/
principles-truth-o-meter-politifacts-methodology-i/
4 K. Boland, P. Fafalios, A. Tchechmedjiev, K. Todorov, S. Dietze
ŝƐƚŚĞƉƌŽƉŽƐŝƚŝŽŶŽĨ ŚĂƐĐŽŶƚĞdžƚ
;ŚĂƐƉƌŽƉŽƐŝƚŝŽŶͿ ;ŝƐƚŚĞĐŽŶƚĞdžƚŽĨͿ
ůĂŝŵWƌŽƉŽƐŝƚŝŽŶ ůĂŝŵhƚƚĞƌĂŶĐĞ ůĂŝŵŽŶƚĞdžƚ
ϭ Ύ Ύ ϭ
ϭ Ύ
ŚĂƐƌĞƉƌĞƐĞŶƚĂƚŝŽŶ Ύ ŚĂƐĂƚƚŝƚƵĚĞ
;ƌĞƉƌĞƐĞŶƚƐͿ ŚĂƐƌĞǀŝĞǁ ;ŝƐƚŚĞĂƚƚŝƚƵĚĞŽĨͿ
;ƌĞǀŝĞǁƐͿ
Ύ Ύ Ύ
ƚŽƉŝĐ
;ĂƚƚŝƚƵĚĞͿ
ZĞƉƌĞƐĞŶƚĂƚŝŽŶ ZĞǀŝĞǁ ƚƚŝƚƵĚĞ dŽƉŝĐ
Ύ ϭ
Fig. 1: The main concepts related to a claim proposition.
factual or subjective (expressing an opinion). A claim proposition can be ex-
pressed in many different ways and in different contexts, thus it has one or more
claim utterances. For example, it may be expressed in different languages, using
different words in the same language, or uttered by different persons and/or in
different points in time. On the contrary, a specific claim utterance can be associ-
ated to only one proposition, i.e., it has a single meaning. The claim proposition
can be represented in different ways, for example, by selecting a representative
utterance or through a more formal model. Each claim utterance is related to
a specific claim context, like the author of the claim or its date. It provides
the means to interpret the claim utterance and thus understand its proposition.
Below, we provide details and the main properties of each of these three main
classes (without repeating the associations among them).
Claim Proposition. A claim proposition reflects the meaning of one or more
semantically equivalent claim utterances expressed in different linguistic forms
or contexts. A claim proposition is associated with i) one or more preferred
representations, ii) one or more reviews, and iii) one or more attitudes (Fig.
1). A representation can have the form of free text, e.g., a sentence that best
describes the proposition (like the text of one of the corresponding utterances),
or be more complex, e.g,. a first-order logic model. A review is a resource (e.g.,
a document) that analyzes one or more check-worthy claim propositions and
provides a verdict about their veracity or trustworthiness. An example of such
a review is an article published by a fact-checking organization. Note that not
all claims have a review or verdict. For instance, the claim “the presence of
a gun makes a conflict more likely to become violent” represents a hypothesis
and is difficult to be associated with a correctness score (there may be mixed
evidence supporting and contradicting it). An attitude is an opinion on a given
topic (e.g., a viewpoint), which often underlies a set of specific values, beliefs or
principles. For instance, pro-Brexit and pro-Remain are two different attitudes
for the Brexit topic. A claim proposition can be associated with several attitudes
for different topics. For example, the claim “immigrants are taking our jobs”
supports both the against immigration attitude (for the Immigration topic) and
the pro-Brexit attitude (for the Brexit topic).
Claim Utterance. A claim utterance is the act of expressing a claim proposi-
tion in a specific natural language and form (like text or speech). Among other
things, it may be something said by a politician during an interview, a text within
Modeling and Contextualizing Claims 5
ŝƐƚŚĞƉƌŽƉŽƐŝƚŝŽŶŽĨ ŚĂƐĐŽŶƚĞdžƚ
;ŚĂƐƉƌŽƉŽƐŝƚŝŽŶͿ ;ŝƐƚŚĞĐŽŶƚĞdžƚŽĨͿ
ůĂŝŵWƌŽƉŽƐŝƚŝŽŶ ůĂŝŵhƚƚĞƌĂŶĐĞ ůĂŝŵŽŶƚĞdžƚ
ϭ Ύ Ύ ϭ
ϭ ϭ Ύ
ŚĂƐƌĞƉƌĞƐĞŶƚĂƚŝŽŶ ŚĂƐůŝŶŐƵŝƐƚŝĐƌĞƉƌĞƐĞŶƚĂƚŝŽŶ ŚĂƐƐŽƵƌĐĞ
;ƌĞƉƌĞƐĞŶƚƐͿ ;ŝƐƚŚĞůŝŶŐƵŝƐƚŝĐƌĞƉƌĞƐĞŶƚĂƚŝŽŶŽĨͿ ;ŝƐƚŚĞƐŽƵƌĐĞŽĨͿ
Ύ Ύ ŚĂƐĂŶŶŽƚĂƚŝŽŶ Ύ
ƐƵďĐůĂƐƐŽĨ >ŝŶŐƵŝƐƚŝĐ ;ŝƐĂŶŶŽƚĂƚŝŽŶŽĨͿ
ZĞƉƌĞƐĞŶƚĂƚŝŽŶ ŶŶŽƚĂƚŝŽŶ ^ŽƵƌĐĞ
ZĞƉƌĞƐĞŶƚĂƚŝŽŶ ϭ Ύ
Fig. 2: The main concepts related to a claim utterance.
a news article written by a journalist, or a tweet posted by a celebrity about a
controversial topic. It is associated with i) one or more linguistic representations
(subclass of representation in Fig. 1), and ii) one or more sources (Fig. 2). A
linguistic representation can be, for example, a text in a specific language that
best imprints the claim as it was said/appeared, or a sound excerpt from some-
one’s speech. A source provides evidence of the claim existence. For instance, it
can be the URL of an interview video, a news article, or a tweet. A linguistic
representation can have one or more annotations which provide formal linguistic
characteristics, like an entity or date mentioned in the text of the claim utter-
ance, the polarity of this text (e.g., positive, negative, neutral), or the linguistic
tone of a speech (like irony). The annotation can enable advanced exploration of
the claims (e.g., based on mentioned entities) and can be manually provided by
a domain expert or automatically produced using a NLP or speech processing
tool (like an entity linking tool for the case of entity annotation in text).
Claim Context. The claim context provides background information about the
claim utterance (Fig. 3). Together with the linguistic representation of the claim
utterance, it can provide an answer to the Five W’s: i) what was said (linguistic
representation of claim utterance), ii) who said it (author of the claim), iii) when
it was said (date the claim was said), iv) where it was said (location the claim
was said), and v) why it was said (event or activity in the context of which
the claim was said). The claim context provides the necessary information for
interpreting the claim utterance (and thus understanding its proposition), and
can be extended with more concepts that allow describing additional context
information about the claim utterance (like the topic of the underlying discourse
or the medium used for uttering the claim).
ŝƐƚŚĞƉƌŽƉŽƐŝƚŝŽŶŽĨ ŚĂƐĐŽŶƚĞdžƚ
;ŚĂƐƉƌŽƉŽƐŝƚŝŽŶͿ ;ŝƐƚŚĞĐŽŶƚĞdžƚŽĨͿ
ůĂŝŵWƌŽƉŽƐŝƚŝŽŶ ůĂŝŵhƚƚĞƌĂŶĐĞ ůĂŝŵŽŶƚĞdžƚ
ϭ Ύ Ύ ϭ
ŚĂƐĂƵƚŚŽƌ Ύ Ύ Ύ Ύ ŚĂƐĞǀĞŶƚ
;ŝƐƚŚĞĂƵƚŚŽƌŽĨͿ ŚĂƐĚĂƚĞ ŚĂƐůŽĐĂƚŝŽŶ ;ŝƐƚŚĞĞǀĞŶƚŽĨͿ
;ŝƐƚŚĞĚĂƚĞŽĨͿ ;ŝƐƚŚĞůŽĐĂƚŝŽŶŽĨͿ
Ϭ͘͘ϭ Ϭ͘͘ϭ Ϭ͘͘ϭ Ϭ͘͘ϭ
ƵƚŚŽƌ ĂƚĞ >ŽĐĂƚŝŽŶ ǀĞŶƚ
Fig. 3: The main concepts related to a claim context.
6 K. Boland, P. Fafalios, A. Tchechmedjiev, K. Todorov, S. Dietze
Instantiation Example. Fig. 4 depicts an instantiation example of the pro-
posed conceptual model. The example shows information for two claim utter-
ances (in pink background): i) one said by David Dimbleby during a topical
debate in Dover (“We are going to be paying until 2064, apparently”), and ii)
one extracted from a news article of The Independent (“UK will be paying Brexit
‘divoce bill’ until 2064”). Both utterances correspond to the same claim propo-
sition (in green background) and each one has its own context information (in
yellow background). The linguistic representation of the first claim utterance has
been annotated with one date annotation (2064) and that of the second claim
utterance with one entity annotation (UK). The claim proposition has two rep-
resentations, a textual one (“Britain will be paying its Brexit bill for 45 years af-
ter leaving the EU”) and a formal one (“cost = {of=Brexit, for=UK amount=?,
until=2064}”), and supports the against-Brexit attitude for the Brexit topic. In
addition, there is a review of this claim proposition with verdict “true”, pub-
lished by Full Fact (UK’s independent fact-checking organisation). We can also
see the URL of the review article as well as a reference to a PDF file which
provides evidence for its correctness. The context of each claim utterance pro-
vides additional metadata about the claim. For example, we see that the first
utterance was said by David Dimbleby on 15.03.2018, in the context of a debate
about Brexit which took place in Dover.
4 RDF Implementation
We introduce an RDF/S implementation of the proposed conceptual model
using established vocabularies, in particular schema.org,4 the Open Annotation
(OA) Data Model,5 the Marl Ontology,6 the NLP Interchange Format (NIF),7
and the PROV Data Model.8 The selection of these vocabularies was based on
the following three main objectives: i) relying on stable term identifiers and
persistent hosting, ii) being supported by a community, iii) being extensible.
Fig. 5 depicts the proposed schema. For representing the main concepts of
our conceptual model, we exploit classes and properties of schema.org, a collab-
orative, community activity with a mission to maintain and promote a common
schema for structured data on the Web and beyond. We make use of the class
schema:Claim (currently under integration in schema.org) to describe a claim
utterance. According to schema.org, this class represents a specific, factually-
oriented claim. For the claim proposition, we use the class schema:Intangible,
a utility class that serves as the umbrella for a number of ‘intangible’ things.
Although this class does not sufficiently reflect the semantics of a claim propo-
sition, it appears to be the most reasonable term for representing a proposition.
For the same reason, we use schema:Intangible to describe a claim context.
4
https://schema.org/
5
http://www.openannotation.org/
6
http://www.gsi.dit.upm.es/ontologies/marl/
7
https://persistence.uni-leipzig.org/nlp2rdf/
8
https://www.w3.org/TR/prov-dm/
Modeling and Contextualizing Claims 7
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͞tĞĂƌĞŐŽŝŶŐƚŽďĞ ƚLJƉĞ
ĚďƉĞĚŝĂ͗ƌĞdžŝƚ ůĂďĞů
ƚŽƉŝĐ ͞ĂŐĂŝŶƐƚͲƌĞdžŝƚ͟ ƉĂLJŝŶŐƵŶƚŝůϮϬϲϰ͕ ƐĂŵĞĂƐ
ĚďƉĞĚŝĂ͗ŽǀĞƌ
ĂƉƉĂƌĞŶƚůLJ͟ΛĞŶ ŚĂƐĂŶŶŽƚĂƚŝŽŶ
ůĂŝŵZĞǀŝĞǁ ͗ƌĞǀŝĞǁϱϴ
ƚLJƉĞ ŚĂƐƌĞǀŝĞǁ ǀĞŶƚ
ĂƚĞ ͗ĂŶŶŽƚϭϮϯ ŚĂƐĞǀĞŶƚ
͞dZh ͟ ĂƵƚŚŽƌ ŶŶŽƚĂƚŝŽŶ ƚLJƉĞ ͗ĞďĂƚĞ
͞&Ƶůů&ĂĐƚ͟ ƚLJƉĞ
ǀĞƌĚŝĐƚ Ƶƌů
ƐĂŵĞĂƐ
ŚƚƚƉƐ͗ͬͬĨƵůůĨĂĐƚ͘ŽƌŐͬĞƵƌŽƉĞͬďƌĞdžŝƚͲĚŝǀŽƌĐĞͲďŝůůͲϮϬϲϰͬ ƉŽƐŝƚŝŽŶ ƐƵƌĨĂĐĞĨŽƌŵ LJĞĂƌ ĐŽŶĨŝĚĞŶĐĞ ĚďƉĞĚŝĂ͗ĞďĂƚĞ
ĐŝƚĂƚŝŽŶ
ϯϯ ͞ϮϬϲϰ͟ ϮϬϲϰ Ϭ͘ϵϱ ƚŽƉŝĐ
ŚƚƚƉƐ͗ͬͬĐĚŶ͘Žďƌ͘ƵŬͬ &KͲDĂZĐŚͺϮϬϭϴ͘ƉĚĨ ĚďƉĞĚŝĂ͗ƌĞdžŝƚ
Fig. 4: Instantiation example of the conceptual model.
An alternative solution is to bypass the claim context class and directly link an
instance of schema:Claim to instances of the four classes connected to the claim
context (author, date, location, event). These four classes are described through
corresponding schema.org classes: schema:Thing (e.g., a person, an organiza-
tion, a blog, etc.), schema:Date, schema:Place, schema:Event. For connecting a
schema:Claim to a schema:Intangible, we can use the property schema:about
or its inverse schema:subjectOf.
For representing a source, we use the class schema:CreativeWork (or one of
its sub-classes). Thereby, we take advantage of its properties and can describe
additional information about the source, such as headline, language, keywords,
publisher, etc. The linguistic representation of a claim utterance, as well as the
(preferred) representation of a claim proposition, can be described through the
class schema:Text (for textual representations) or schema:MediaObject (for
image, audio or video representations). For describing annotations, we make use
of the widely-used OA and NIF data models, while provenance information is
represented though the PROV data model. NIF allows us to include detailed
information about the outcome of an NLP process on textual representations
(like begin/end indexes and confidence scores). The review of a claim proposition
8 K. Boland, P. Fafalios, A. Tchechmedjiev, K. Todorov, S. Dietze
ƐĐŚĞŵĂ͗ƐƵďũĞĐƚKĨ ƐĐŚĞŵĂ͗ĂďŽƵƚ
ƐĐŚĞŵĂ͗/ŶƚĂŶŐŝďůĞ ƐĐŚĞŵĂ͗ůĂŝŵ
ůĂŝŵWƌŽƉŽƐŝƚŝŽŶ ƐĐŚĞŵĂ͗ĂďŽƵƚ ůĂŝŵhƚƚĞƌĂŶĐĞ
ƐĐŚĞŵĂ͗/ŶƚĂŶŐŝďůĞ
ůĂŝŵŽŶƚĞdžƚ
ƐĐŚĞŵĂ͗ŝƚĞŵZĞǀŝĞǁĞĚ ŵĂƌů͗ŚĂƐKƉŝŶŝŽŶ ƐĐŚĞŵĂ͗ ƐĐŚĞŵĂ͗ĂƉƉĞĂƌĂŶĐĞ ƐĐŚĞŵĂ͗ĂƵƚŚŽƌ
ƌĞĂƚŝǀĞtŽƌŬ ƐĐŚĞŵĂ͗dŚŝŶŐ
^ŽƵƌĐĞ ͗KƌŐĂŶŝƐĂƚŝŽŶ
ƐĐŚĞŵĂ͗ůĂŝŵZĞǀŝĞǁ ŵĂƌů͗KƉŝŶŝŽŶ
ZĞǀŝĞǁ ƚƚŝƚƵĚĞ ͗WĞƌƐŽŶ
ŵĂƌů͗ĚĞƐĐƌŝďĞƐKďũĞĐƚ
ƐĐŚĞŵĂ͗ƌĞǀŝĞǁZĂƚŝŶŐ ŵĂƌů͗ĚĞƐĐƌŝďĞƐ&ĞĂƚƵƌĞ ƐĐŚĞŵĂ͗ĚĂƚĞƌĞĂƚĞĚ
͙ ƐĐŚĞŵĂ͗ĂƚĞ
ƐĐŚĞŵĂ͗ZĂƚŝŶŐ ͙ ͙ ŵĂƌů͗ŚĂƐƉŽůĂƌŝƚLJ
ŵĂƌů͗ƉŽůĂƌŝƚLJsĂůƵĞ ƐĐŚĞŵĂ͗ůŽĐĂƚŝŽŶ
ƐĐŚĞŵĂ͗WůĂĐĞ
ŶŝĨ͗ĂŶŶŽƚĂƚŝŽŶ ŶŝĨ͗ĂŶŶŽƚĂƚŝŽŶ ƐĐŚĞŵĂ͗ƚĞdžƚ ƐĐŚĞŵĂ͗ĞǀĞŶƚ
ŶŝĨ͗ŶŶŽƚĂƚŝŽŶ ƐĐŚĞŵĂ͗ ǀĞŶƚ
ƐĐŚĞŵĂ͗ǀŝĚĞŽͬĂƵĚŝŽ
ƐĐŚĞŵĂ͗dĞdžƚ ƐĐŚĞŵĂ͗dĞdžƚ
ƐĐŚĞŵĂ͗ƚĞdžƚ ZĞƉƌĞƐĞŶƚĂƚŝŽŶ ŽĂ͗ŚĂƐdĂƌŐĞƚ >ŝŶŐƵŝƐƚŝĐZĞƉƌĞƐĞŶƚĂƚŝŽŶ
ƐĐŚĞŵĂ͗ǀŝĚĞŽͬĂƵĚŝŽ ƐĐŚĞŵĂ͗ ͙ ƐĐŚĞŵĂ͗ ƐĐŚĞŵĂ͗ŚƚƚƉƐ͗ͬͬƐĐŚĞŵĂ͘ŽƌŐͬ
DĞĚŝĂKďũĞĐƚ ŽĂ͗ŚĂƐdĂƌŐĞƚ ŽĂ͗ŚĂƐdĂƌŐĞƚ DĞĚŝĂKďũĞĐƚ ŽĂ͗ŚƚƚƉ͗ͬͬǁǁǁ͘ǁϯ͘ŽƌŐͬŶƐͬŽĂη
ŵĂƌů͗ŚƚƚƉ͗ͬͬƉƵƌů͘ŽƌŐͬŵĂƌůͬŶƐη
ƉƌŽǀ͗ ŶƚŝƚLJ ŽĂ͗ŶŶŽƚĂƚŝŽŶ ƉƌŽǀ͗ŚƚƚƉ͗ͬͬǁǁǁ͘ǁϯ͘ŽƌŐͬŶƐͬƉƌŽǀη
ƉƌŽǀ͗ǁĂƐ'ĞŶĞƌĂƚĞĚLJ ŶŝĨ͗ŚƚƚƉ͗ͬͬƉĞƌƐŝƐƚĞŶĐĞ͘ƵŶŝͲůĞŝƉnjŝŐ͘ŽƌŐͬŶůƉϮƌĚĨͬŽŶƚŽůŽŐŝĞƐͬŶŝĨͲĐŽƌĞη
Fig. 5: An RDF implementation of the conceptual model.
is described through the class schema:ClaimReview, which in turn is connected
to a schema:Rating for assigning a rating score about the veracity of the claim
proposition. Finally, we exploit the Marl ontology to represent attitudes. Marl is a
data schema designed to annotate and describe subjective opinions, and provides
the attributes that enable to connect opinions with contextual information.
5 Concluding Remarks
We propose a conceptual model and an implementation based on existing
RDF vocabularies to represent and contextualize claims and related entities.
Our work is meant to advance a shared understanding of claims and related ter-
minology across communities, and the semi-structured representation of claims
and their contexts to foster transparency, reproducibility and a joint advance-
ment of research in related fields.
An open challenge is the detection and representation of the inherent re-
lations between claims as well as their relations to other entities or resources.
In particular, the semantic relatedness of claims is reflected by the relations
between their proposition components. Establishing relations across the model
classes, e.g., relating an utterance to a proposition, allows the uncovering of para-
phrased claims with identical meaning, while topical relations between claims are
of crucial importance to enable retrieval and search of claims.
Other challenges concern information extraction techniques geared towards
the extraction of utterances from text or audio together with the attitude to-
wards a particular topic, as well as additional contextual information, such as
authors or sources. From a knowledge representation perspective, we emphasize
the need for a formal representation of propositions (e.g. by applying dynamic
predicate logics), or for extending the Marl model in order to represent specifi-
cally viewpoints instead of general opinions on objects. This reveals the need for
the development of a dedicated ontology for claims representation.
Modeling and Contextualizing Claims 9
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