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Building Knowledge Graphs About Political

Agents in the Age of Misinformation

Daniel Schwabe, Carlos Laufer, Antonio Busson

Dept. of Informatics, PUC-Rio, R. M. de S. Vicente, 225, Rio de Janeiro, RJ 22453-900, Brazil

Abstract. This paper presents the construction of a Knowledge Graph about relations between agents in a political system. It

discusses the main modeling challenges, with emphasis on the issue of trust and provenance. Implementation decisions are also

presented

Keywords: Political Systems, trust, linked data, provenance, influence networks.

1. Introduction

Although the term “Knowledge Graph” (KG) was

introduced by Google in 2012

, graph-based databases

were available before this (e.g., Wordnet [28], DBPe-

dia [22], Yago [38], CYC [23], NELL [7], and addi-

tional ones (e.g. ConceptNet [37]) continue to be cre-

ated on a regular basis.

The majority of the largest published KGs are open-

ended, in the sense that they include facts in practically

any domain of knowledge. Consequently, for those

having an underlying semantic model (e.g. DBPedia,

Yago), their supporting ontologies are wide-ranging

and are constantly being updated to accommodate new

domains.

Another set of KGs are domain specific, and use

specialized ontologies to describe their data ( e.g. [36]

discusses several in the Life Sciences, [32] presents

ontologies for the music domain).

KGs differ also on the way they are built (popu-

lated). A few are curated (e.g., CYC), others rely on

crowdsourced information (e.g. Wikidata [39]), and

most extract information from structured, semi-struc-

tured or textual information harvested from the Web.

The multiplicity of sources and various extraction

approaches naturally raises the issue of data quality

and confronts the user of the data in the KG with the

https://googleblog.blogspot.com/2012/05/introducing-

knowledge graph-things-not.html

The expression “Se liga” in Portuguese has a colloquial mean-

ing of “be aware”, “pay attention to”, as well as “connect yourself”.

issue of trusting, or not, the information obtained from

the KG. For some types of information, for example in

case of online reviews online and social media, this

trust can have a direct effect on commercial success

(e.g. [4]). This highlights the fact that data, ultimately,

expresses a belief, opinion or point of view of some

agent.

This paper presents an approach to building a KG

in the domain of Political Agents, with a special em-

phasis on their different types of relations. This ap-

proach has been used in an initiative to build an open

KG about Political Agents in Brazil in the form of

Linked Data, named “Se Liga na Politica”

(SLNP).

The data in this KG is obtained from several sources,

in both automated and non-automated ways. Most of

the automated extraction is made from official sources,

such as the open data published by the House of Dep-

uties and by the Senate. In addition to such sources,

data may also be contributed by individuals, in

crowdsourced fashion.

1.1. Political Systems

Since very early times, men have organized them-

selves to form societies [34], naturally leading to the

formation of Political Systems, defined as “formal and

informal processes by which decisions are made

In Portuguese, it reads both as “pay attention to Politics” and “Con-

nect yourself to Politics”. A third (indirect) meaning is the reference

to Linked Data.

concerning the use, production and distribution of re-

sources in any given society”. Societies include

Agents – Persons and Organizations – that participate

in the processes of its Political System. In doing so,

they are driven, and constrained, by the various types

of relations that exist among them.

With the advent of the Information Society and the

Network Society [9] [13], accelerated by the wide-

spread adoption of the Internet and the Web, infor-

mation has become a vital resource, inextricably inter-

twined with the functioning of Political Systems.

Transparency, the quality that allows participants of

the society to know what are the particular processes

and agents that are being used in its functioning, is

generally regarded as a means to enable checks and

balances within Political Systems to prevent misuse by

any of the parties involved [19] [26]. One of the forms

to increase transparency within a Political System is to

provide information about its participants and their re-

lations, as a way to provide additional context when

analyzing their actions, and, ultimately, making deci-

sions.

In an ideal Linked Data World, a specific database

about political agents would not really be necessary,

as institutions responsible for each type of information

would publish them in Linked Data form, creating a

large KG. In practice, however, we are far from this –

the majority of the published information about Polit-

ical Systems is fragmented and incomplete. The focus

of the SLNP project is to establish the links between

the various “domains of knowledge” involved in de-

scribing Political Systems, taking care not to replicate

all of the information published by each source

(thereby, somehow, replacing it), focusing on charac-

terizing the relations between agents and often omit-

ting other properties that may be of interest of specific

communities.

1.2. Trust

Given the multiplicity of sources, and the nature of

the subject matter, this KG is designed so that facts are

seen as claims made by some agent, and therefore

provenance information becomes a “first class citizen”

of the domain. One of the main usages for this data-

base is to provide context information for news stories,

to allow readers to establish trust in the claimed facts

based on their own criteria.

The issue of trust has been prevalent in the Internet

since its popularization in the early 90s (see [16] for a

survey), with a focus on the lower layers of the

http://xmlns.com/foaf/spec/

Internet Architecture, emphasizing authentication.

More recently, with the advent of the Web and social

networks, the cybersphere, and society as a whole, has

become heavily influenced by information (and mis-

information) that flows in news sites and social net-

works in the Internet. There are many studies carried

out in several disciplines attempting to characterize

and understand the spread of information in the cy-

bersphere, and how this affects society (see [24] for an

overview). A more prominent aspect has been the

spread of “fake news”, actually a term used to refer to

several different misuses of information, as postulated

by Wardle in [39]. This has also been the focus of

much research and many initiatives (e.g. [10], [11],

[3]).

The original vision for the Semantic Web included

a “Trust” layer, although its emphasis was more on au-

thentication and validation, and static trust measures

for data. There have been many efforts in representing

trust, including computational models - a general sur-

vey can be found in [31]; [5] presents an excellent ear-

lier survey for the Semantic Web; and [35] surveys

trust in social networks. In the Linked Data world, it

is clear that facts in Semantic Web should be regarded

as claims rather than hard facts (e.g., [6]), which natu-

rally raises the issue of trust on those claims.

The remainder of this paper is organized as follows.

Section 2 presents the Domain Model for relations be-

teen agents in Political Systems; Section 3 presents a

model for the Trust Process supported by the SLNP

KG, and details how provenance information is repre-

sented and used; Section 4 briefly discusses imple-

mentation aspects, and Section 5 draws some conclu-

sions and points to ongoing and future work.

2. Domain Model

This section presents the POLARE ontologies that

characterize the relations between agents in Political

Systems. Before delving into details, some of the re-

quirements for the ontology and the rationale for the

design approach are discussed.

2.1. Methodological Approach

2.1.1. Ontologies vs vocabularies

As a general rule, preference was given to using

well-known ontologies, such as FOAF

, ORG

https://www.w3.org/TR/vocab-org/

SKOS

, Schema.org

, etc… as controlled vocabularies

to describe concepts in their respective domains. Pre-

cisely because these ontologies are very general, they

allow many possible uses within other ontologies.

POLARE, in many situations, defines specific ways

in which these vocabularies can be used for its pur-

poses; whenever the intended use was incompatible

with these ontologies, SLNP’s own vocabulary was

used. In addition, SLNP’s vocabulary also includes

terms to describe concepts not found in any of the bet-

ter-known controlled vocabularies.

POLARE is meant to be used to characterize data in

a Linked Data database. It is envisioned that this data

may be used in my different ways, for various pur-

poses. To allow such latitude, it was deliberately de-

signed in a “lightweight” fashion, with few specific in-

ference rules. It is understood that it is possible to ex-

tend it with a more “heavyweight” ontology by includ-

ing inference rules to further constrain the possible in-

terpretations, for use in specific situations.

One should also keep in mind that POLARE de-

scribes statements which are understood as claims be-

ing made by some agent (this is elaborated in section

3). Therefore, additional care must be taken when in-

cluding inference rules, as they may be expressing re-

strictions according do some particular point of view,

not necessarily accepted or agreed upon by all users.

The POLARE ontology includes several Datatype

properties, but since they are not so relevant for char-

acterizing relations, they will not be discussed in here.

2.1.2. OWL vs SKOS

OWL is a knowledge representation language, de-

signed to formulate, exchange and reason with

knowledge about a domain of interest. OWL can be

reasoned with by computer programs either to verify

the consistency of that knowledge or to make implicit

knowledge explicit [18].

An alternative approach to represent knowledge is

proposed by the SKOS ontology. A SKOS concept can

be viewed as an idea or notion; a unit of thought. How-

ever, what constitutes a unit of thought is subjective,

and this definition is meant to be suggestive, rather

than restrictive [27].

The approach used for SLNP is based on a hybrid

set of OWL vocabularies and SKOS Concept Schemes

to describe concepts in its domain. OWL is used to de-

fine more formal structures where the inference rules

can be used to make implicit knowledge. SKOS is

http://www.w3.org/TR/skos-reference

http://schema.org/docs/full.html

used to define concepts that are mainly used for re-

trieval and navigation tasks, and for which there are

many possible alternative schemes.

Several SKOS Concept Schemes have been identi-

fied that should complement the OWL classes defined

in the POLARE OWL vocabularies. They are used as

classifications for specific classes. The rationale for

choosing to use SKOS as opposed to OWL was based

on the generality vs specificity of the concept involved

– whenever the concept could be represented in many

different ways depending on the particular Political

System, SKOS was preferred. For example, the “clas-

sification” of an Organization can be made in many

different taxonomies, often non-mutually exclusive –

for instance, according to fiscal status, legal status,

type of ownership/control, etc… Such uses will be

highlighted throughout the description of POLARE.

Whenever possible, preference was given to utilizing

standard vocabularies.

2.2. Domain Models

2.2.1. People and Organizations

The central concepts in POLARE are Persons and

Organizations, as they are the Political Agents within

a Political System. Given the goal to characterize the

various kinds of relations between them, direct rela-

tions between Persons was the first focus, and then re-

lations between Persons and Organizations were ex-

amined, as they establish indirect relations between

Persons. The FOAF vocabulary was chosen to de-

scribe Persons, and the ORG vocabulary to describe

Organizations, adding relations in the POLARE ontol-

ogy as needed

The first kind of relations between persons are di-

rect family relations., which are modeled in POLARE

as Direct Relationships, shown in Error! Reference

source not found.. Rather than simply using an

owl:ObjectProperty, they are modeled via reification,

due to the need to qualify this relation with temporal

information. The directRelProp property allows spec-

ifying what is the family relation; its value, rather than

being an rdf:Property, is a skos:Concept, whose value

will be taken from a suitable Skos:ConceptScheme.

This allows inclusion of certain relations that may not

be “formally” accepted as a family relation but may be

of interest for some types of analyses, e.g., “co-habi-

tates”.

For readability purposes, we do not add a prefix to terms of PO-

LARE itself (e.g., pol:hasPost). Similarly, when it is clear which

ontology a term is from (e.g. foaf:Person), we omit the prefix.

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