=Paper=
{{Paper
|id=Vol-1951/PrivOn2017_paper_7
|storemode=property
|title=On Modeling Political Systems to Support the Trust Process
|pdfUrl=https://ceur-ws.org/Vol-1951/PrivOn2017_paper_7.pdf
|volume=Vol-1951
|authors=Carlos Laufer,Daniel Schwabe
|dblpUrl=https://dblp.org/rec/conf/semweb/LauferS17
}}
==On Modeling Political Systems to Support the Trust Process==
https://ceur-ws.org/Vol-1951/PrivOn2017_paper_7.pdf
On Modeling Political Systems to
Support the Trust Process
Carlos Laufer1[+552135271500] and Daniel Schwabe1 [+552135271500]
1
Dept. of Informatics, PUC-Rio
R. M. de S. Vicente, 225, Rio de Janeiro, RJ 22453-900, Brazil
laufer@globo.com, dschwabe@inf.puc-rio.br
Abstract. Modern political life is heavily dependent and influenced by claims
about facts made about participants in a Political System (seen here as sets of
agents and organizations that govern a society). Such claims are commonly found
in news stories, tweets, and social media postings, and, depending on each user,
they may be considered “true” or “false”. The decision to act based on a fact
contained in a claim is dependent on the agent’s trust on the claim, i.e., that the
fact being stated is indeed “true”. In this paper, we propose a framework to de-
scribe the trust process, which can serve as a a support for various possible mod-
els of content trust. Within this framework, we present POLARE, an ontology to
describe a Political System, which includes provenance information, and illus-
trate how it may provide information to feed the trust process in online news
stories.
As a case study, we illustrate the approach using a Knowledge Graph created
with such claims, which can be queried in order to better understand the existing
relations between agents in the Political System in Brazil. In order to use claims
in this Knowledge Graph, users will typically require provenance information to
make trust judgements about them.
Keywords: trust, provenance, linked data, political systems, news.
1 Introduction
Modern society has become heavily influenced by information (and misinformation) that
flows in news sites and social networks in the Internet [10]. There are many studies carried out
in several disciplines attempting to characterize and understand the spread of information in the
cybersphere, and how this affects society1 [3,27,26]. An arguably more recent phenomenon has
been the spread of “fake news”, actually a term used to refer to several different misuses of in-
formation, as postulated by Wardle in [29]. This has also been the focus of much research and
many initiatives [9,1,18,6], including manual fact-checking by sites like Snopes2, Politifact3,
1
See for example, https://www.pheme.eu/ and https://revealproject.eu.
2
http://www.snopes.com
3
http://www.politifact.com/
2
FullFact4, FactCheck5, and Crosscheck6, and automated approaches such as Factmata7,
Hoaxy[25] and others [7,28,22].
This phenomenon highlights an often-implicit assumption in most information systems, in-
cluding websites – the fact that the data it uses is assumed to be “true”, in the sense that the
application can rely on this data to perform whatever computation it needs to do in order to fulfill
its purposes. The advent of user-contributed data raised the issue of assessing its “data quality”
[19], and the fact that data, in reality, expresses a belief or opinion of some agent. This becomes
mostly evident in case of online reviews online and social media, often with direct effect on
commercial success (e.g. [14]).
The original vision for the Semantic Web already envisioned a “Trust” layer, although its
emphasis was more on authentication and validation, and static trust measures for data. There
have been many efforts in representing trust, including computational models; a general survey
can be found in [22], [5] presents an excellent earlier survey for the Semantic Web, and [24]
surveys trust in social networks. The advent of Linked Data highlighted the vision that facts in
Semantic Web should be regarded as claims rather than hard facts [7].
In most if not all research surveyed in [22], there is an implicit model of what is the trust
process that is being adopted or assumed, which is never spelled out precisely. In this paper, we
propose a framework to describe the trust process, which can incorporate various alternative
models of content trust (as proposed in [11]). Within this framework, we present POLARE, an
ontology to describe a Political System, which includes provenance information, and illustrate
how it may provide information to feed the trust process in online news stories.
The remainder of this organized as follows. Section 2 presents a model of the trust process;
Section 3 presents POLARE; Section 4 shows an example of its use, and Section 5 discusses
related work, and Section 5 draws some conclusions and points to future work.
2 A Framework for the Trust Process
In this section, we characterize the trust process that underlies the use or consumption of data/in-
formation on the web. Figure 2 shows a diagram of our proposed framework for the trust process
model.
Data/Information
Metadata
Trust
Trusted
Process Action
Data
Context
Policies
Agent
Figure 1 – A Framework of the Trust Process
We focus here on the cases where an Agent needs to act, i.e., do some computation, make a
decision, or take some Action. The agent must act based on some Data/Information items which,
clearly, it must trust – the Trusted Data. Contrary to how it used to be in pre-Internet times, the
4
https://fullfact.org/
5
https://crosscheck.firstdraftnews.com/france-en/
6
http://factcheck.org/
7
http://factmata.com/
3
Data/Information items to be used by the agent may come from several sources, and it is not
always clear (to the Agent) what is the quality of this Data, or the trustworthiness of the Infor-
mation it containes. Therefore, the Agent must apply a Trust Process to filter the incoming
Data/Information items and extract the Trusted Data items to be used by the Action. From this
point of view, an item is considered as the smallest indivisible element that can be used in the
Trust Process, and may have an internal structure when used by the Action.
This Trust Process can be based on a multitude of different signals, some of which we have
singled out in the diagram in Figure 1, namely, the Metadata which describes various properties
of the Data/Information items, and the Context in which the Action will take place. The criteria
used in the Trust Process are expressed by Policies determined by the Agent. Notice that in this
framework, the Context contains any arbitrary information items used by the Policies, in addition
to Metadata and the Data/Information items themselves.
Many, if not most, of the trust models discussed in the surveys mentioned in Section 1
[5,19,24] can be regarded as providing models or representations for one or more of the elements
of this trust framework, such as different representations for the metadata, or specification lan-
guage for the policies, or particular types of context information that can be added to the data.
Regardless of these models, it should be clear that, as far as the Action is concerned, the whole
process is binary: An incoming Data/Information item is either accepted and inserted into the
Trusted Data, or it is not – there are no “half filters”. In other words, when time comes to Act,
either the Agent uses that Data/Information item, or it does not, it can’t “use it partially” – an
element is considered indivisible from the point of view of the Trust Process [4].
Thus, the actual trusting process should not be confused with models which may attribute
non-discrete or continuous values to the trustworthiness assigned to data/information items that
are used to determine if the incoming data/information items should be filtered or not.
Another point to notice is that, in this framework, only the Agent determines the policies it
wishes to apply to incoming Data/Information. The metadata associated with the incoming
Data/Information may contain Data/Information about the publisher or provider of a Data/Infor-
mation item, and the Policies may take this into account in the filtering process.
In contrast, privacy issues would require adding a similar set of metadata and policies to the
published Data/Information that will be consumed by the Agent, which would act as an additional
filter, applied before the Data/Information is made available to the Agent. The privacy Policies
of a Publisher may use information about the (requesting) Agent, the Action and the Context.
In this work, we will focus on specific types of Data/Information – those pertaining to Po-
litical Systems, and one specific type of metadata, Provenance, in the context of news media. In
the next section, we discuss the trust process in this context, and propose a model for information
and metadata to support it.
3 Modeling Political Agents, Relationships and Provenance
The nature of information in news and social media must, per force, be seen as containing
claims made by someone about some “fact” (or “statement”), i.e., someone (an agent, i.e. a person
or organization) claims a certain fact to be true. It is up to the reader to accept the truth of these
claimed facts.
In order to accept the truth of a claim, the reader either already knows (i.e. believes in) the
truth of the claimed fact, or s/he must somehow establish trust in the agent making the claim.
Whereas there are many definitions of trust [16,17], we follow the view that trust is “the
expectation that some agent will act in a predictable, expected way, in a given situation”. There-
fore, to accept a certain claim about a hitherto unknown fact entails either establishing the truth
through direct observation (or a chain of interdependent observations), or trusting the source of
the claim, possibly based on some evidence. The trust on the source of the claim then implies
4
that one expects the claim to be true because that source has been consistently (predictably and
expectedly) correct in the past.
If, however, the source is not trusted, one may require some evidence to serve as the basis
to establish the truth of the claim. Such evidence is, in turn, another claim made by some third
party, which the claimant believes is more trusted by the first user than her/himself. In this fash-
ion, a chain of interdependent claims is established, such that the truth of the original claim relies
on the trust on the agent at the end of the chain. In modern societies, certain public agents (e.g.,
Vital Record Office, Notary Public) have, by convention and mutual agreement among its mem-
bers, the so-called “public faith”, meaning that whatever they claim (about certain specific facts)
is deemed “true”. For instance, John’s mother claims he was born in Brazil, but for many pur-
poses, this claim is only accepted as true if she can produce a birth certificate issued by the proper
authority stating that he was indeed born in Brazil.
From the discussion above, it is safe to say that provenance data about information published
in the cybersphere is crucial to enable agents to consume this information – i.e., accept it as true
and eventually act based on this. This has been clearly recognized by many researchers and schol-
ars studying the “fake news” phenomenon [13,15].
In [5] Wardle proposes 7 types of mis- and disinformation:
1. Satire or parody – when there is no intention to cause harm, but can be misinterpreted and
cause harm;
2. Misleading content – Use of information to frame an issue or individual in a misleading
way;
3. Imposter content – When a genuine source is impersonated;
4. Fabricated content – New content that is entirely false, designed to deceive and do harm;
5. False connection – Use of headlines, visuals or captions that don’t support the actual con-
tent;
6. False context – Sharing of genuine content with false contextual information;
7. Manipulated content – When genuine information or imagery is manipulated to deceive.
A large part of fact checking, especially when automated, focuses on claims that are in clear
contradiction with verifiable facts – type 4 above.
We have been involved with an initiative to publish a database about Political Agents in
Brazil in the form of Linked Data, named “Se Liga na Politica”8 (SLNP). The data in this database
is obtained from several sources, in both automated and non-automated ways. Most of the auto-
mated extraction is made from official sources, such as the open data published by the House of
Deputies and by the Senate. In addition to such sources, data may also be contributed by individ-
uals, in wiki fashion. One of the main usages for this database is to provide context information
for news stories, to allow readers to establish trust in the claimed facts based on their own criteria.
We present here the rationale and design decisions for the underlying ontologies used in this
database, starting first with our motivations.
We regard a Political System as sets of agents and organizations that govern a society. Our
goal in the SNLP project is to help making the existing relationships between political agents in
a Political System explicit and thus subject to analysis. We do so by providing a Linked Data
database where such relationships appear as data items. Given the multiplicity of sources, and
the nature of the subject matter, this database is designed so that facts are seen as claims made
by some agent, and therefore provenance information becomes a “first class citizen” of the do-
main.
We first describe the “domain” ontology dealing with agents and their relationships, and
then describe how provenance is integrated.
8
The expression “Se liga” in Portuguese has a colloquial meaning of “be aware”, “pay attention
to”, as well as “connect yourself”. In Portuguese, it reads both as “pay attention to Politics”
and “Connect yourself to Politics”. A third (indirect) meaning is the reference to Linked Data.
5
3.1 POLARE – A Political Agents Relationship Ontology
The central concepts in POLARE are People, Organizations and the Posts that people occupy in
Organizations. The main terms of the POLARE vocabulary come from W3C’s ORG Ontology9
and are shown in Figure 2. It is important to notice the reification of the relation “occupies”
between Person and Post via the Membership class, which is fundamental to allow us to represent
properties of this relation, as will be discussed later.
We have added the “polare:hasPost” relation linking “org:Membership” with “org:Post” be-
cause we need to represent that a Post might be occupied by different persons in different periods
of time.10
Both Post and Membership may have start and end dates associated to them. The dates as-
sociated to a Post refer to a time period when the Post exists, for example, for a post in the House
of Representatives, it corresponds to a Legislature, which defines the mandate of the elected per-
son. The Membership dates refer to the period in which the Person actually occupies the Post, as
it is possible for a Person to temporarily leave the Post for a short period of time within the
mandate.
For example, every four years there is a presidential election in Brazil to select a President
and a Vice-President to lead the country during the succeeding four years. Therefore, there is a
President Post (P1) and a Vice-President Post (P2) in the Federal Government (an Organization)
that have established start and end dates. We have to create a Membership (M1) for the person
that was elected for President with the same start date as P1, and a Membership (M2) for the
person that was elected for Vice-President with the same start date as P2.
In some situations, for example, when the President has to travel outside the country, the
Vice-President assumes the Post of President for the period of time that the elected President is
abroad. In that case, we have to add the end date to M1 with the initial date of the travel. We have
to create a new Membership (M3), related to P1, with a start date equal to the initial date of the
travel, and assign it to the person occupying the Vice-President post P2. When the President
returns, we have to add the end date of M3 with the final date of the travel. In addition, we have
to create a new Membership (M4), related to P1, with a start date equal to the final date of the
travel, and allocate it to the President again. Thus, after the travel period, the database will have
three Memberships (M1, M3, M4) related to the same Post (P1).
9
https://www.w3.org/TR/vocab-org/
10
Notice that “Post” here refers to a “Position”, not to a blog post.
6
Figure 2- The main concepts in the POLARE ontology.
Inferred relations are shown as dotted lines.
Several indirect relationships of interest between Persons can be captured simply by the fact
that these Persons occupy Posts in the same Organization. For example, the fact that two con-
gressmen belong to the same party (which is an Organization), or that they were once colleagues
in a company in the private sector or in a department in the executive branch.
Another important kind of relationships between persons are direct kinships, which are mod-
eled in POLARE as Direct Relationships, shown in Figure 3. Once more, since it is necessary to
represent some properties of these relationships, such as the dates involving a marriage, we use
the traditional class approach for reifying the relationship property. The directRelProp property
allows specifying what is the kinship relation, typically using the Rel vocabulary11.
Figure 3 – Direct relationships between Persons in POLARE
In Political Systems, and also in organizations, it is important to know if some Post was
filled through a Referral, i.e., that some Agent indicated (nominated) a Person to occupy a Post.
POLARE can represent which Agent has referred some Person to occupy a Post in an Organiza-
tion, shown in Figure 4. We also reified the “refers” property using a Referral class, to allow
including data related to the Referral.
11
http://vocab.org/relationship/
7
Figure 4 – Modeling Referrals in POLARE
3.2 Heart - a Claim Provenance ontology pattern
As already discussed, a critical information to establish trust is data related to the provenance
of that information, since acceptance of the truth of a claim relies on the trust one has on the
Agent making the claim. For the Political Systems domain, the focus is on the relationships be-
tween agents, which are modeled in POLARE using the Membership, Direct Relation and Refer-
ral classes.
The PROV-O Ontology12 provides a vocabulary to represent provenance information, which
can be used in a variety of ways to represent diverse aspects of provenance. For our purposes,
especially because we allow individual users to enter information (including provenance) in the
database, it is important to ensure that the provenance information is recorded in a uniform way,
providing enough information to allow the trust process. To achieve this, we resort to the notion
of a pattern, similar in spirit to the PROV template proposed in [20]. The advantage of using
PROV Patterns is to extend the ontology with a well-formed structure using the PROV vocabu-
lary, allowing the construction of queries that use PROV data mixed with the data of the main
ontology.
Figure 5 – The PROVHeart Provenance Pattern. A provenance chain can be established using
the prov:hasPrimarySource property. The “heart” symbol represents an instance of this pattern.
We define a provenance pattern called PROVHeart, shown in Figure 5. PROVHeart is a
simple pattern that models the provenance of an Entity that was generated by an Activity at-
tributed to an Agent with a Role in that Activity, using another Entity as a source, and acting on
12
https://www.w3.org/TR/prov-o/
8
behalf of another Agent. Provenance chains can be built using the prov:hadPrimarySource rela-
tion between Entities that serve as the “justification” or “basis” for each claim, forming the basis
for the trust chain discussed earlier. Notice also that “prov:actedOnBehalfOf” is represented as a
property chain property, because this relation has to be linked to the Activity that produced the
particular Entity the provenance refers to. In other words, “prov:actedOnBehalfOf” is seen as
applying to specific activities, and not as a “blanket statement” that always holds for all Activities
carried out by an Agent, relative to another Agent.
Figure 6 – The complete POLARE ontology including provenance information.
Figure 6 shows the complete POLARE ontology, where the heart symbol represents the
PROVHeart pattern. One can see that claims involving any of the main relations (Membership,
Referral and DirectRel) have provenance information attached to them.
As an example of PROVHeart use, consider the case where someone wants to know all
Persons, and respective Posts, that were referred by a Person called “Jader Barbalho”. Further-
more, these Referrals should have provenances stating that the sources used (evidence) were
published by a Person called “Andréia Sadi” or are from a Magazine called “Época”, and, also,
should be published to the database by a Person called “Daniel” or “Laufer”.
We implemented claims based on the nanopublication model [12]13, where a claim, repre-
sented by a set of statements, is recorded as a group of three named graphs:
• assertion graph - containing the set of statements that compose the claim, using
POLARE ontology;
• publication info - containing statements that give provenance information about the
claim publication activity, using the provHeart pattern;
• provenance - containing statements that give provenance information about the claim
itself, using the provHeart pattern.
In our implementation, we used a named graph, called claims, to store the nanopublications
related to the set of all claims published in the database.
The example above can be queried in the database using the following query:
13
http://www.nanopub.org
9
SELECT DISTINCT ?referrerName ?referredName ?roleName ?startdate ?citation
WHERE {
GRAPH graph:claims {
?pub np:hasAssertion ?claim ;np:hasProvenance ?prov ;np:hasPublicationInfo ?pubinfo . }
GRAPH ?claim {
?referral a polare:Referral ;polare:hasReferred ?referred ;schema:startDate ?startdate .
?referrer polare:isReferrer ?referral ;foaf:name ?referrerName .
?referred polare:occupies ?post ; foaf:name ?refferedName . ?post org:role ?role .
?role skos:prefLabel ?roleName .
FILTER (?referrer = ex:JaderBarbalho) . }
GRAPH ?prov {
?claim prov:hadPrimarySource ?source ; prov:wasAttributedTo ?agent ;
prov:wasGeneratedBy ?activity .
?source dcterms:bibliographicCitation ?citation .
?delegation prov:hadActivity ?activity ;prov:agent ?org .
FILTER (?agent = ex:AndreiaSadi || ?org = ex:EpocaMagazine) . }
GRAPH ?pubinfo {
?pub prov:wasAttributedTo ?agpub .
FILTER (?agpub = ex:Daniel || ?agpub = ex:Laufer) . }
In terms of the Trust Framework, the claims discussed here allow describing Data/Infor-
mation items and Provenance information about them. Further discussion about trust and trust
policies is outside the scope of this paper.
4 POLARE in action
In this section, we present an example of how the database can be used to better understand
a news story, discussing the options made available to the reader to establish her/his trust on the
information contained in the story.
Consider the blog post14 made by reporter Andréia Sadi, who works for the GloboNews
news channel, about an investigation carried out by the Federal Police in the office of Congress-
woman Simone Morgado, wife of Senator Jader Barbalho. This investigation is related to em-
bezzlement accusations against an aide working in the Congressswoman’s office. Figure 7 con-
tains a simplified diagram with the main facts regarding the Persons, Posts, and Organizations
cited in the blog post. In this diagram, we use a simplified view of provenance, showing only a
few provenance facts using the “source” link.
The information in the SNLP database contains the following information about the persons
involved in the blog post.
Jader Barbalho is a Brazilian lawyer, politician and businessman. In his long political career,
he was a Federal Deputy of Pará, Governor of Pará, Minister of Social Security of Brazil and
Minister for Agrarian Development of Brasil. He occupies the post of Senator of Pará in the
current legislature. He was married to Simone Morgado, until the end of 2016, according to his
son, Helder Barbalho.
Helder Barbalho is a Brazilian politician and administrator. He was, during 8 years, the
Mayor of Ananindeua, a city located in the state of Pará. He was the former Minister of Fisheries
and Aquaculture of Brazil and is currently the Minister of National Integration.
Simone Morgado is a Brazilian economist and politician. She was Mayor of Bragança, a
city located in the state of Pará and is currently a Congresswoman representing the state of Pará.
Soane Castro currently occupies the post of aide in the office of Simone Morgado, and held
the post of Superintendent in the Ministry of Fisheries and Aquaculture of Brazil, during the
period when Helder Barbalho was its Minister.
14
http://g1.globo.com/politica/blog/andreia-sadi/post/pf-realiza-operacao-na-camara-dos-depu-
tados-mas-alvo-nao-e-localizado.html. (In Portuguese)
10
The shaded area indicates facts (actually, claims) that are not directly cited in the story, but
are part of the database. In particular, it explicates that there are several connections linking Sen-
ator Jader Barbalho to the aide who is the target of the investigation – she was appointed for the
position related to the embezzlement accusations by the Senator’s son, Helder Barbalho, who
was the Minister in charge of that agency at that time. This post (position) is in the home state of
all agents involved, Pará.
Let us focus now on the kinship information about Simone Morgado and Jader Barbalho.
The blog post itself states that they are married, but also adds that Helder Barbalho claims they
have not been married since November 2016. The fact that the reporter did not directly refer to
Ms. Morgado as “Senator Jader Barbalho’s ex-wife” seems to indicate she chose not to fully trust
the information that they are separated, and instead refers the reader to a secondary source citing
the Senator’s son claim about this fact. However, she did not give any provenance to Helder
Barbalho’s claim – i.e., how she became aware of the claim. Figure 8 shows the POLARE in-
stance for the kinship relation claimed in the blog post, including reference to PROVHeart in-
stances, Prov_2 and Prov_3.
Assuming that agent Laufer was responsible for entering the facts mentioned in the blog
post into the database in wiki style, the corresponding provenance information Prov_2 using
PROVHeart is shown in Figure 9.
relation
Person post Organization Document location Date
key
3/5/2016
Superintendent start date
source dou-secao-2-03-
Soane Castro occupies 05-2016-pg-7
has post
occupies
aide
Fishing and
House of Aquaculture
has post
Representatives Coordination
part of
Office has post
Simone location part of
has post
Morgado
occupies
Congressman location Ministry of
Pará Fisheries and
Aquaculture…
location 01/01/2005
part of
spouse
Jader
Barbalho Senator Ananindeua
01/11/2013
occupies end date 02/10/2015
source end date has post
has post location
start date start date
01/11/2016 Senate Mayor 01/01/2015
father
https://goo.gl/CKVUfu source end date
occupies
Minister
Helder occupies
Barbalho
author 12/05/206
occupies
start date
Minister
Andrea has post
Ministry of
Sadi Integration
occupies
…
Reporter GloboNews
has post
Figure 7 – An instance of POLARE with facts underlying a blog post
11
Figure 8 – Kinship relation modeled using POLARE
In this provenance information, Laufer has chosen to interpret the blog post as stating that
the spouse relationship has an end date, in spite of the slight “conflict” in the text. It would be
possible to create a separate claim for the end date should this be considered relevant and im-
portant.
Figure 9 - First provenance information about blog post (Prov2).
Suppose now that another agent, Daniel, decided to check whether this kinship information
is accurate. Short of trying to find a court document stating that they are officially divorced,
Daniel searches for additional sources for this claim, and finds a news story stating the same fact,
and enters it into the database. This becomes a second provenance information for the claim in
the database, which is represented in Figure 8 as the Prov_3 PROVHeart instance.
As mentioned, the actual trust process is outside the scope of this paper, but we nevertheless
outline how the provenance information can be used in a trust process. In the trust process, the
consumer of this data must apply her/his own policies in order to accept the truth of the statement.
12
In the example, one policy could be to simply accept it (meaning, to use it in her/his com-
putations or decisions) based on her/his trust on the recording agent, e.g., Laufer. A second pol-
icy, a bit more cautious, would be to accept it based on the agent responsible for the primary
source, Andréia Sadi. At this point, it is not possible to continue the chain of provenance, because
there is no additional provenance information given in the blog post. A different kind of policy
can be used, however. Analogously to journalistic principles of obtaining multiple independent
sources about the truth of some claim, the consumer could decide to trust this claim based on the
fact that there are two different provenances given, although, strictly speaking, s/he should also
check whether Agents Andréia Sadi and Murilo Ramos are independent.
Figure 10 - Second provenance information about blog post (Prov3).
5 Related Work
Our work is related to several others along different aspects. With respect to the project itself,
and its goals of making connections explicit, we can cite the LittleSis.org15 website, and the Po-
deropedia Project16. Both have built databases about Political Agents, the former specifically for
the US, the latter allowing instantiations for different countries. Compared to SNLP (and
POLARE) LittleSis has a rich model for organizations, and similar types of relations between
People and Organizations. Poderopedia, in addition, has a finer grained model for some of the
relations. None of them include the Referral relation.
Whereas both allow representing provenance, they do so by linking documents as sources,
and therefore it is not possible to use chained provenance data in the way enabled by POLARE.
whose central characteristic is the inclusion of patterns of provenance information, as an exten-
sion of data itself. Since both projects allow inclusion by users, they have a much stricter and
structured editorial process to ensure the quality of the information and uniform use of criteria, a
task that is more easily automated by the use of the PROVHeart pattern. As discussed, this finer
grained provenance information also provides better support for the trust process.
15
https://littlesis.org/
16
http://www.poderopedia.org/poderopedia/pages/index/2
13
The Popolo Project17 is an initiative to define data interchange formats and data models for
governments, in the context of Open Government. They define a set of classes that cover, basi-
cally, the specification of Persons and Organizations, using Posts and Memberships to relate Per-
sons to Organizations. Popolo also defines a set of classes related to voting processes. The main
principle of Popolo is reuse. The classes are defined as a set of properties of well-known vocab-
ularies, including ORG, FOAF, geonames, schema.org, etc. POLARE defines the linking be-
tween Persons to Organizations in a similar way. POLARE introduces a set of relations that are
not contemplated by Popolo, as kinship relations and referrals. Popolo does not include prove-
nance information.
6 Conclusions and Future Work
We have presented a framework for describing the Trust Process, and used it to contextualize the
use of Semantic Data in the SLNP project, which aims at building an open Linked Data database
of Political Agents, using the POLARE ontology. Special attention is given to provenance data
in order to support a trust process.
There are several directions in which we are continuing this research. We have already made
two additional ontologies to represent the Voting Process in the Legislative branch and the Elec-
toral Campaign Process. The former allows recording the various bills proposed in Congress, and
how Congressmen voted on them throughout the legislative process. The latter allows recording
every candidate’s income declaration, the contributions to their campaign as well as payments
made by campaign offices to suppliers and to other campaigns. It is our goal to also include
information about organizations in a manner similar to LittleSis as specializations to the Org
ontology, and by adding more direct relations between Organizations.
Another important type of relation between Agents are transactions; we plan to enrich the
POLARE ontology to include them. In our view, transactions should include business transac-
tions, but also legal actions, educational actions, etc.…
Whereas PROVHeart is able to capture the more common provenance pattern found in the
Political Agents domain, it is possible that more detailed or finer grained patterns are necessary,
something we are also investigating, and plan to evaluate it with journalists involved in the pro-
ject.
Even with provenance information, there is still an issue of authentication of documents –
they could be false after all. This falls under the realm of digital signatures, and this should be
integrated into the framework. In addition, the provenance information itself could be falsified.
There are emerging technologies such as blockchains that may be used to address this, providing
a verification that the provenance chain has not been tampered with, so long as each provenance
assertion is digitally signed as well.
With respect to implementing provenance information, the approach described here uses the
traditional reification via class approach. We have also been investigating the idea of reifying the
properties using the singleton property approach [21], which allows modeling properties (actually
property instances) as individuals and as Properties. Figure 12 shows a snippet of the POLARE
schema using this approach, and the corresponding OWL fragment of an instance. We want to
investigate if the differences in these approaches could reveal a better way of constructing queries
to inspect data in the database.
Another implementation aspect we will investigate is the addition of access control to por-
tions of the database. We envisage that certain communities (e.g., reporters) may want to enter
information to the database but restrict, at least for a period of time, general access to it.
17
http://www.popoloproject.com/
14
Figure 11 - Representing properties using the Singleton approach.
:JaderBarbalho rdf:type owl:NamedIndividual ,foaf:Person ;
foaf:name "Jader Barbalho"^^xsd:string ;
:occupies_1 :Post_1 ;
:occupies_1 rdf:type owl:NamedIndividual, owl:ObjectProperty,
org:Membership ;
schema:startDate "2015-01-01"^^xsd:date ;
:singletonPropertyOf :occupies.
:occupies rdf:type owl:NamedIndividual, owl:ObjectProperty.
Figure 12 – Representing relations as singletons
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