This paper introduces an ontology for representing trust that extends existing ones by integrating them with recent trust theories. Then, we propose an extension of such an ontology, tailored for representing trust assessments of data, and we outline its specificity and its relevance. 1 Stephen Marsh, “Trust: Really, Really Trust”, IFIP Trust Management Conference 2014 Tutorial
In this paper we tackle the problem of modeling and representing trust assertions, in particular about (Web) data. This is an important issue for a variety of reasons. First, trust is an important aspect both of everyday life and of many computational approaches, for similar reasons. In fact, trust is a “leap of faith” 1 that is necessary to be taken whenever we need to rely on third party agents or information. We decide whether or not to take this leap of faith based on the evaluation of the trustworthiness of the agent or information. In general, when trusting, a risk is involved, i.e., the risk of relying on uncertain and possibly unpredictable actions or information. We can soften such a risk, and one way to achieve this result is to share trust and trustworthiness values, along with their provenance, to allow their reuse and increase the probability to correctly place trust thanks to the availability of this information. Therefore, an ontology for trust assessments, in particular of Web data, can indicate the basic elements that are necessary to define a trust value.
This paper aims at introducing an ontology for trust representation, starting from existing ones and extending them to cover aspects indicated by recent trust theories. In Section 2 we present related work, in Section 3 we provide a summary of the trust theory of O’Hara that we use in the rest of the paper, in Section 4 we propose an extended ontology for representing trust and in Section 5 we expose our vision about the issues of trusting (Web) data. Lastly, we conclude in Section 6.
Trust is a widely explored topic within a variety of computer science areas.
Here, we focus on those works directly touching upon the intersection of trust,
reputation and the Web. We refer the reader to the work of Sabater and Sierra
[
We extend the ontology proposed by Alnemr et al. [
Goldbeck at al. [
Trust has been modeled also in previous works of ours [
We recall here the main elements of “A Definition of Trust” by O’Hara [
X places trust in Y (Trust Action) X performs some action which introduces a vulnerability for X, and which is inexplicable without the truth of Trust attitude.
We are interested in enabling the sharing of the trust values regarding both
trust attitude and actions, along with their provenance. The ontology proposed
by Alnemr et al. [
We also distinguish between the attitude and the act of trusting. Trust Attitude Object: it represents the graded belief held by the trustor in the trustworthiness of the trustee and it is treated as a quality attribute when deciding if to place trust in the trustee or not. It replaces the reputation object defined by Alnemr et al. because it has a similar function to it (quantifying the trust in something), but implements the trust attitude relation defined by O’Hara that is more precise and complete (e.g. warranties are not explicitly modeled by the reputation object); Trust Action Object: the result of the action of placing trust. Placing trust is an instantaneous action based on an “outright” belief. Therefore the trust value is likely to be a Boolean value.
Role, Context and Warranty: in the original ontology, the criterion is a generic class that contextualizes the trust value. We specialize it, to be able to model the role and the context indicated in the theory of O’Hara, as well as the evidence on which the trust value is based, by means of the warranty.
The trustworthiness relation is not explicitly modeled, since it falls outside our current focus. We discuss this further in the following section. The remaining elements of the model shown in Figure 1 are part of the original trust ontology. These include a criterion for the trust value, and an algorithm that allows combining observations (warranties) into a trust value (an algorithm is used also to determine the value of the trust action). The trust attitude value corresponds to the Deg element of the theory of O’Hara. We model the action both when it is performed and when it is not. Both trust values are modeled uniformly. 5
In the previous section we provided an extended ontology that aims at capturing the basic elements that are involved in the process of taking trust decisions. Here we focus on the specificity of trusting (Web) data. 2 The ontology is available at http://trustingwebdata.org/ontology Role
* collectedBy 1
Warranty
* hasWarranty hasRole1
* hasCriteria 1...*
Context
* hasContext rdfs:subClassof
1 1 Criterion 1
Description
Name * 1...* CalculatedBy
rdfs:subClassof hasValue part of 1...*
1 * Trust Attitude Value
1 Computation
Algorithm 1 1 hasCriteria Collection Algorithm 1
Assessment rdf:type rdf:type
Rating
Data are used as information carriers, so actually trusting data does not mean
to place trust in a sequence of symbols. It rather means to place trust in the
interpretation of such a sequence of symbols and on the basis of its trustworthiness.
For instance, consider a painting reproducing the city “Paris” and its annotation
“Paris”. To trust the annotation, we must have evidence that the painting
actually corresponds to the city Paris. But, to do so, we must: (1) give the right
interpretation to the word “Paris” (e.g., there are 26 US cities and towns named
“Paris”), and (2) check if one of the possible interpretations is correct. Both in
the case the picture represents another city or in the case the picture represents
a town named Paris which existence we ignored, we would not place trust in the
data, but for completely different reasons. One possible RDF representation of
the above example is: exMuseum:ParisPainting ex:depicts dbpedia:Paris,
where we take for granted the correctness of the subject and of the property
and, if we accept the triple, we do so because we believe in the correctness of the
object in that context (represented by the subject), and role (represented by the
property). We make use of the semantics of RDF 1.1 [
Items 1, 2, 4 and 5 map the URIs, literals and the RDF triples to real-world objects. We are particularly interested in Item 3, that maps the property of an RDF triple to the corresponding real-world relation between subject and object. Trusting a piece of data means to place trust in the information it carries, in a given context. The trust context can be represented by means of the subject and object of an RDF triple, so their semantic interpretation is assumed to be known by the trustor. If the trustor trusts the triple, he believes that the interpretation of the object o makes the interpretation of the triple s p o true: TrustAttitudetrustor(ojs; p) = Belief trustor(9I(o) : <I(s),I(o)> 2 IEXT (I(p))
Belief is an operator that maps logical propositions to values that quantify
their believed truth, e.g., by means of subjective opinions [
By virtue of items c and d, we do not model explicitly the trustworthiness relation defined by O’Hara: we consider an object o to be trustworthy by virtue of the fact that it is part of an RDF triple that is asserted.
Criterion
Trustor (Source) rdf:type ex:me
Trustworthiness Object exMuseum:ParisPainting ex:depicts rdf:type hasContext hasRole rdf:object
CO Trustee (Target) rdf:type dbpedia:Paris
hasSource rdf:type hasContext TO1 hasTarget hasTrustAttitudeValues 0.8 calculatedBy
Computation
Algorithm rdf:type TrustAlgorithm In this paper we introduce an ontology for trust representation that extends an existing model with recent trust theories. We specialize it in order to model data-related trust aspects, and we motivate our design choices based on standard RDF 1.1 semantics. This model is still at a very early stage, but it emerges from previous research and from standard trust theories. In the future, it will be extended, and evaluated in depth, also by means of concrete applications.