Linked open data offers a set of design patterns and conventions for sharing data across the semantic web. In this position paper we enumerate some key uncertainty representation issues which apply to linked data and suggest approaches to tackling them. We suggest the need for vocabularies to enable representation of link certainty, to handle ambiguous or imprecise values and to express sets of assumptions based on named graph combinators.
Dave.e.Reynolds@gmail.com
The need for reasoning over uncertain information within the semantic web occurs in
many different situations. It can arise from intrinsic uncertainty in the world being
modeled or from limitations of the sensing or reasoning agent itself (epistemic). The
term uncertainty is often used to refer to many different notions including ambiguity,
randomness, vagueness, inconsistency, incompleteness [
In recent years an approach to the semantic web, called linked data, has been developed and offers a promising route to practical and widespread semantic web uptake. It provides a set of design guidelines or patterns for how the semantic web technologies, and broader web architecture, can be used for sharing information. The existing guidelines and practices have no provision for representation of uncertainty; yet linked data is indeed fraught with many of these different types of uncertainty.
In this brief position paper we examine the ways in which uncertainty can occur in a linked data setting and sketch possible approaches to addressing the issues raised. multiple data sources that have been independently developed and modeled. Uncertainty can arise from the instance linking process, from the mapping between different sources models and due to differing hidden assumptions in the underlying datasets. Yet the essence of linked data, and a large part of the reason for its uptake, is simplicity. The data is intended to be self-descriptive and accessible through simple link following and graph union or through SPARQL endpoints. Our challenge is to develop a common, easy to deploy, approach to uncertainty representation which can be applied to linked data sets without losing this simplicity. 3
In this section we enumerate some key sources of uncertainty for linked data. We focus on the sources which directly result from the intrinsic nature of linked data – the cross-linking of independently developed RDF datasets. 3.1
In linked data, entities (Individuals) which co-occur with different URIs in different
datasets are unified. This is achieved by publishing owl:sameAs relations between
identified entities, either within the dataset or as a separate link set. The process of
identifying such co-references is imperfect. Firstly, the co-references are typically
found by a mixture of string matching, attribute matching, and type constraints,
generally based on a statistical or machine learning algorithm [
Many datasets in the linked data web publish property values for the entities they describe; for example, the population of the City of London. Yet those values are sometimes imprecise or dependent upon measurement assumptions that are not made explicit. For example, the population of a city depends on the time of the measurement, the measurement methodology and the precise definition of the boundary of the city; it is also subject to statistical uncertainty. As a result, at the time of writing, a linked data query on London returns a graph with four assertions on its population ranging from 7,700,000 to 8,500,000. One of these sources of variation, the time of measurement, is sometimes made explicit in data and indeed one of the four assertions is (indirectly) time qualified. However, such contextual qualification is not consistently available and, in any case, only accounts for one source of variation. Thus when datasets are linked the resulting union will often have multiple conflicting values for supposedly functional properties.
When linking datasets we also want to map the associated ontologies. This process is just as error prone as entity co-reference since the axiomatization of concepts in the ontologies is rarely so complete as to allow a unambiguous mapping. Errors in the ontology mapping can lead to global effects such as unexpected identification of related concepts. Determining and publishing such alignment errors is the subject of considerable research and is outside the scope of this paper.
Separate from the uncertainty arising from combination and linking of datasets then the datasets themselves can be uncertain or contain errors (either accidental or malicious). While this is true in general in the semantic web, the linked data approach implies broad cross linking with no provision for narrow scoping of link references. This exacerbates the problems of the veracity or trustworthiness of included datasets. 4
We now discuss approaches to mitigate the effects of these uncertainty sources on the consumers of linked data. In keeping with linked data methodology we seek simple, broadly applicable, design patterns. In particular, we suggest the need for design patterns for making the uncertainty inherent in the linked datasets more explicit, and mechanisms to enable selective combination of datasets (so that problematic values or links can be omitted). In this a short position paper we only sketch the suggested approaches as a basis for discussion in the workshop.
The link vocabulary would provide a common representation for co-reference links,
enabling publication of the link certainty information on which per-link inclusion
decisions can be made. This can be achieved by extending the voiD ontology [
The imprecise value vocabulary would provide a common representation for imprecise values that arise from data set merger, as discussed in 3.2. This would allow republication of merged datasets which explicitly show the variation in source data values. Returning to our example of the population of London the merged set might look like: :London :population [a :ImpreciseValue; :samplevalue [:value 7700000; :source :s1; :context :y2009] :samplevalue [:value 7900000; :source :s2; :context :y2008] :estimatedValue 785123]
Finally we suggest the need for override graphs so that one agent can publish retractions and overrides to the link assertions or data assertions made by another.
The current approach to this, in linked data applications, is to partition data and
link sets into named graphs [
Of the issues in section 3 we have suggested an agenda for how to address some of them. The link and imprecise value vocabularies enable publication of link uncertainty (3.1) and value ambiguity (3.2) information in linked data sets. The vocabularies themselves would not remove the uncertainties, nor the problems of estimating them. However, simply having a means to publish this information is already a step forward. The suggested graph combinators would enable an agent to make and publish more selective data combinations, based on its interpretation of link strengths and data values. This does not solve the problems of deciding which parts of which sources to trust, but it does enable more effective sharing of such decisions.