=Paper=
{{Paper
|id=None
|storemode=property
|title=Towards a Data-Centric Notion of Trust in the Semantic Web
|pdfUrl=https://ceur-ws.org/Vol-576/paper5.pdf
|volume=Vol-576
}}
==Towards a Data-Centric Notion of Trust in the Semantic Web==
https://ceur-ws.org/Vol-576/paper5.pdf
Towards a Data-Centric Notion of Trust in the
Semantic Web
(A Position Statement)
Olaf Hartig
Department of Computer Science
Humboldt-Universität zu Berlin
hartig@informatik.hu-berlin.de
Abstract. Existing research on trust in the Semantic Web extensively
studies trustworthiness and trust in the context of active entities such as
persons and agents. However, few work exist that focus on the content
in the Semantic Web and that study trustworthiness as an information
quality criterion. Hence, computer systems that use the trustworthiness
of Semantic Web data for filtering or decision making usually apply a
very simple assessment approach: each data object is related to some
kind of a source for which a trust score can be determined using one of
the methods that exist for active entities; this score is then adopted for
the trustworthiness of the data object. In this position paper we argue
that such a simple notion of trustworthiness for data is insufficient and
we propose to adjust the focus of trust research for the Semantic Web
from an actor-centric view to a data-centric perspective.
1 Introduction
Today, a large amount of RDF data is published on the Web; large datasets are
interlinked; new applications emerge that utilize this data in novel and innovative
ways. However, the openness of the Web and the ease to combine Linked Data
from different sources creates new challenges. Unreliable data could dominate the
result of queries, taint inferred data, affect local knowledge bases, or may have
negative or misleading impact on software agents. Hence, questions of reliability
and trustworthiness must be addressed.
A great many approaches exist that allow for a calculation of trust values for
active entities such as persons, software agents, or peers in a P2P scenario [1].
While several of these approaches can be applied to consider trustworthiness of
data providers in the Semantic Web (e.g. [2,3,4]), little has been done consid-
ering the data itself. Existing work applies a very simple assessment approach:
each data object is related to some kind of a source for which a trust score
can be determined using one of the methods that exist for active entities; this
score is then adopted as the trustworthiness of the data object. However, simply
adopting the trustworthiness of a source for its data does not consider cases
where statements have multiple sources, where providers (re)publish data aggre-
gated from the original sources, or where inference engines discover implicit facts
from statements of other sources. Hence, source-level approaches are too coarse-
grained and, thus, insufficient for the Web of data. Furthermore, our knowledge
of the provenance of a data object is not the only criterion that can be applied
to assess the trustworthiness of the object. Other factors such as the correctness
of the data or the opinion of another data consumer may affect our decision.
In this paper we argue for making data the central subject of research on trust
in the Semantic Web. Therefore, we propose to reconsider the actor-centric trust
research for the Semantic Web and conceive trust in the Semantic Web more
as an effort that fits in the wider area of information quality (IQ) research. IQ
reflects the fitness for use of information [5]. Since this fitness for use may depend
on various factors, IQ is a multi-dimensional concept which includes different IQ
criteria such as accuracy, completeness, and timeliness [6]. Consequently, we
consider the trustworthiness of Linked Data as another such IQ criterion.
Our fundamental understanding of the trustworthiness of data is the subjec-
tive belief or disbelief in the truth of the information represented by this data [7].
The decision to believe or to disbelieve is affected by a broad variety of influences.
Notice, this complexity renders the actor-centric idea of simply representing the
trustworthiness of data by adopting the trust value of an actor as insufficient. We
propose to classify the influences in three categories: i) information quality, ii)
provenance, and iii) others’ opinions. In the remainder of this paper we discuss
these categories in more detail (cf. Sections 3 to 5). As a basis for this discussion
we review existing approaches that focus on the trustworthiness of data or on
content in general (cf. Section 2).
2 Existing Research
In this paper we propose to focus trust research in the Semantic Web on the
trustworthiness of data. Similarily, Gil and Artz [8] identify that the majority of
existing work on trust “focuses on entity-centered issues such as authentication
and reputation and does not take into account the content.” Therefore, the
authors propose to study content trust which “is a trust judgment on a particular
piece of information in a given context.” As the units of content that are being
judged Gil and Artz identify Web resources in general.
To the best of our knowledge, there is still only very few work on content
trust in the Semantic Web community. With IWTrust and FilmTrust, two sys-
tems have been proposed that consider the trustworthiness of statements during
processing tasks and for decisions. IWTrust [9], the trust component of the Infer-
ence Web answering engine, understands trust in answers as the trust in sources
and in users. Similarily, FilmTrust [10] represents the trustworthiness of movie
reviews by a user’s trust in the reviewer and in other users’ competence to rec-
ommend movies. A similar understanding of the trustworthiness of statements
published on the Semantic Web has been presented by Rowe and Butters [11].
Their approach adopts a contextual trust value determined for the person who
asserted a statement as the trustworthiness of the statement itself. Hence, even
if these approaches take the trustworthiness of statements into account they still
apply an actor-centric view.
Systems that explicitly focus on trust assessments for statements are TREL-
LIS and QUATRO Plus. The TRELLIS [12] system assesses the truth of state-
ments by considering their provenance and related statements. Users can rate
information sources and follow the assessments that are presented with the cor-
responding analysis and the influencing facts. The QUATRO Plus [13] system
enables trust assessments for descriptions of Web resources; trust assessment is
based on user ratings of these descriptions. Both approaches, however, do not
provide a trust model that explicity represents the trustworthiness of content.
Mazzieri [14] and Richardson et al. [15] propose such trust models; they rep-
resent content trust for RDF data on the level of RDF statements. Mazzieri
introduces fuzzy RDF; a membership value associated with each statement rep-
resents the likelyhood that the statement belongs to the RDF graph. By equating
those membership values with trustworthiness of statements Mazzieri inappro-
priately mixes two different concepts; trustworthiness is not the same as a fuzzy
notion of truth nor is trustworthiness of RDF statements tied to a specific RDF
graph. Richardson et al. [15] represent a user’s personal belief in a statement by
a value in the interval [0,1]. Besides the vague explanation that a “high value
means [...] the statement is accurate, credible, and/or relevant” the approach
lacks a more formal definition of those values. Thus, what is missing in all cases
is a well-founded definition of the meaning of trustworthiness of RDF data.
Another related system is the WIQA framework [5] that permits quality
based filtering of data aggregated from the Web. Filtering is based on policies;
these policies are constraints that are enforced during query evaluation and that
restrict the resultset of queries. Furthermore, the system explaines why data
should be trusted, more precisely, why results passed the filters. The WIQA
approach does not use explicit scores for IQ criteria. However, missing scores
prevent comparisons of the trustworthiness of different pieces of data; moreover,
without explicit ratings it is impossible to compare the opinions of multiple data
consumers regarding the trustworthiness of the same data. Instead of a filtering
approach other work focuses on the ranking of Linked Data [16,17].
Other relevant research is provided by the IQ community where trustworthi-
ness of data is often considered synonymous to believability [6]. Lee et al. [18]
decompose believability into three sub-dimensions: trustworthiness of source,
reasonableness of data, and temporality of data. Following this differentiation,
Prat and Madnick [19] propose a provenance based approach to measure believ-
ability by aggregating quality scores for the sub-dimensions. Another provenance
based approach has been proposed by Dai et al. [20]. Their main idea is to deter-
mine the trustworthiness of a data item by considering source data from which
the item has been derived. Furthermore, the approach compares data items to
other, similar, but also to conflicting data items. In [21] we present a generic
approach for methods that assess IQ of Web data and we apply this approach
for the IQ criterion timeliness. Similarily, this generic approach can also be used
as the base for a method to assess the trustworthiness of Web data.
3 Influence Category: Provenance
The decision to believe that a data object represents the truth includes consid-
ering questions such as the following:
– How was the creation of the data conducted?
– Who or what participated in the creation of the data and how much do I
trust this participant?
– To what extend does the input from which the data was produced represents
the truth?
– What happened to the data since its creation; how likely is a manipulation?
These questions refer to the provenance of the data object. We understand the
provenance of a data object as everything that is related to how the object in
its current state came to be. Hence, provenance information about a data object
is information about the whole history of this object. This history may start
long before the object has been created itself because the provenance of source
artifacts used for the creation is also a relevant part of this history. Hence, this
history includes multiple actors that participated in various, different roles. All
of these actors had a certain influence on the data object and the current state
of the object in which it is available to us.
Traditionally, there are two main areas in which researchers study provenance
of data: workflows and databases [22]. Research in these areas usually focuses
on the creation of data, be it a data product generated by a workflow [23] or the
query results created by a database query engine [24]. This focus is reasonable
given that workflows and databases are self-contained systems. The Web, in
contrast, is a much more open environment. A data object on the Web may have
passed through many (virtual) hands before it is finally available in the current
application. Hence, the history of a data object includes more aspects than the
creation. This additional information is of interest when it comes to assessing the
trustworthiness of data objects from the Web as the last of the aforementioned,
provenance-related questions illustrates. For this reason we propose a new model
for Web data provenance in [25]; this model considers the Web based access to
data and the creation of this data equally important. Based on this model we
present concepts and tools to integrate provenance information into the Web
of data in [26]. This information can then be used to apply provenance based
assessment approaches as we introduce in [21].
4 Influence Category: Information Quality
Even if we consider trustworthiness as a criterion of information quality other
IQ criteria are likely to affect our trustworthiness assessment. Knowing about
a lack of correctness, accuracy, or consistency in the data reduces our belief in
the truth of the information represented by that data. Apart from these obvious
influences it often depends on the context if a specific criterion is relevant for the
trustworthiness assessment. An example is completeness: knowing that a dataset
is incomplete may give rise to doubt because missing data may change the infor-
mation in the dataset. On the other hand, the part of the data that is available
might still be trustworthy and, thus, be usable in some application. Similarily,
the relevancy of time-related criteria depends on context and application: old
data might not be believed to be true anymore in some context; in another ap-
plication the low currency might not cause a reduced trustworthiness score for
the same data, for instance, when the development of certain data values over
time should be analyzed.
As can be seen from these examples, trustworthiness can be understood as
a more abstract kind of IQ criteria. Compared to other, independent criteria,
trustworthiness comprises multiple other criteria. This characteristic means that
scores for other, relevant IQ criteria have to be determined as a prerequisite
to assess trustworthiness. These scores, then, have to be weighted during the
actual trustworthiness assessment so that the context-dependent relevancy of
the corresponding IQ criteria is reflected.
5 Influence Category: Other Opinions
An additional factor that can be used to assess a user’s belief in the truth of a
data object is the opinion of other consumers of this data. This approach is simi-
lar to the idea of determining the trustworthiness of actors using trust assertions
in a Web of trust. In addition to the trust assertions about the actor in question,
these Web of trust approaches also take the trustworthiness of the actors into
account that provided the assertions. This principle must be adopted for opinion
based assessment of the trustworthiness of data: the assessment is either based
on the opinion of trusted consumers only or opinions must be weigthed by the
trust in the corresponding consumer to provide reliable trustworthiness scores
for data. Furthermore, we note that the development of trustworthiness assess-
ment approaches that take other consumers’ opinion into account can benefit
from existing work on recommendation systems.
6 Conclusions
In this paper we argue to apply a data-centric view for further research on trust
for the Semantic Web. We understand trustworthiness of Semantic Web data as a
criterion of information quality and identify main categories of factors that affect
the assessment of this criterion. Even if we discuss these categories separately
we suggest that an actual assessment approach should take factors from all
categories into account. As an additional requirement for such trustworthiness
assessment approaches we note that the assessment system should be able to
explain a determined trustworthiness score to end users.
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