The emergence in recent years of initiatives like the Linked Open Data (LOD) has led to a significant increase in the amount of structured semantic data on the Web. In this paper we argue that the shareability and wider reuse of such data can very often be hampered by the existence of vagueness within it, as this makes the data's meaning less explicit. Moreover, as a way to reduce this problem, we propose a vagueness metaontology that may represent in an explicit way the nature and characteristics of vague elements within semantic data.
Ontologies are formal shareable conceptualizations of domains, describing the
meaning of domain aspects in a common, machine-processable form by means of concepts
and their interrelations [
Our position is threefold. i) That vagueness exists not only within isolated, applicationspecific, semantic data but also in public datasets that should be shareable and reusable. ii) That vagueness hampers the comprehensibility and shareability of these datasets and cause problems. iii) That the negative effects of vagueness can be partially tackled by making the data vagueness-aware, namely by annotating their elements with metainformation about the nature and characteristics of their vagueness. In the next section we explain and support the first two parts of our position with real world examples. In section 3 we describe how semantic data can become vagueness-aware via a vagueness metaontology. Sections 4 and 5 present related work and summarize our own.
The possibility of vagueness in ontologies and semantic data has long been recognized
in the research literature, especially in the area of Fuzzy Ontologies [
The presence of vague terms in semantic data often causes disagreements among the people who develop, maintain or use it. Such a situation arose in a real life scenario where we faced significant difficulties in defining concepts like “Critical System Process” or “Strategic Market Participant” while trying to develop an electricity market ontology. When, for example, we asked our domain experts to provide exemplary instances of critical processes, there was dispute among them about whether certain processes qualified. Not only did different domain experts have different criteria of process criticality, but neither could anyone really decide which of those criteria were sufficient for the classification. In other words, the problem was the vagueness of the predicate “critical”. While disagreements may be overcome by consensus, they are inevitable as more users alter, extend, or use semantic data. A worse situation is when a user misinterprets the intended meaning of a vague term and uses it wrongly. Imagine an enterprise ontology where the concept “Strategic Client” was initially created and populated by the company’s Financial Manager whose implicit criterion was the amount of revenue the clients generated for the company. Imagine also the new R&D Director querying the instances of this concept when crafting an R&D strategy. If their own applicability criteria for the term “Strategic” do not coincide with the Financial Manager’s, using the returned list of clients might lead to poor decisions. The above examples show how the inherent context-dependence and subjectivity that characterizes vagueness may affect shareability in a negative way, due to potential disagreements or misunderstandings. More generally, typical use-case scenarios where this may happen include: 1. Structuring Data with a Vague Ontology: When domain experts are asked to define instances of vague concepts and relations, then disagreements may occur on whether particular entities constitute instances of them. 2. Utilizing Vague Facts in Ontology-Based Systems: When knowledge-based systems reason with vague facts, their output might not be optimal for those users who disagree with these facts. 3. Integrating Vague Semantic Information: When semantic data from several sources need to be merged then the merging of particular vague elements can lead to data that will not be valid for all its users.
decide whether a particular dataset is suitable for their needs, the existence of vague elements can make this decision harder. It can be quite difficult for them to assess a priori whether the data related to these elements are valid for their application context.
To reduce the negative effects of vagueness, we put forward the notion of vaguenessaware semantic data, informally defined as “semantic data whose vague ontological elements are accompanied by comprehensive metainformation that describes the nature and characteristics of their vagueness”. For example, a useful piece of metainformation is the set of applicability criteria that the element creator had in mind when defining the element (e.g. the amount of generated revenue as a criterion for a client to be strategic in the previous section’s example). Another is the element creator itself (e.g. the author of a vague fact). In any case, our position is that having such metainformation, explicitly represented and published along with the vague semantic data, can improve the latter’s comprehensibility and shareability, especially in regard to the four scenarios of the previous section. For example, the knowledge of the same vague concept’s intended applicability criteria in two different datasets can i) prevent their merging in case these criteria are different and ii) help a data practitioner decide which of these two concepts’s associated instances are more suitable for his/her application. 3 3.1
In the literature two kinds of vagueness are identified: quantitative- or degree-vagueness;
and qualitative- or combinatory vagueness [
Furthermore, vagueness is subjective and context dependent. The first has to do with the same vague term being interpreted differently by different users. Two company executives might have different criteria for the term “strategic client”. Even if they share an understanding of the type and dimensions of this term’s vagueness, a certain amount of R&D budget (e.g. 1 million euros) makes a client strategic for one but not the other. Similarly, context dependence has to do with the same vague term being interpreted or applied differently in different contexts even by the same user; celebrating an anniversary is different to celebrating a birthday when it comes to judging how expensive a restaurant is. Therefore we additionally suggest that one should explicitly represent the term’s creator as well as the applicability context for which it is defined or in which it is used. 3.2
Ontology elements that can be vague are typically concepts, relations, attributes and
datatypes [
The metamodel is to be used by producers and consumers of semantic data, the former utilizing it to annotate the vague part of their ontologies with relevant metainformation and the latter querying this metainformation to better use them. Vagueness annotation is a manual task, meaning that knowledge engineers and domain experts should detect the vague elements, determine the relevant characteristics (type, dimensions, etc.) and populate the metamodel. How this task may be best facilitated is a subject for further research, but a good starting point would be the integration of the process within traditional semantic data production processes. Regarding the consumption of a vagueness-aware ontology, the first benefit it has for its potential users is that it makes them aware of the existence of vagueness in the domain. This is important because vagueness is not always obvious, meaning it can easily be overlooked and cause problems. The second benefit is that the ontology’s users may query each of the vague elements’ metainformation and use it in order to reduce these problems.
For example, when structuring data with a vague ontology, disagreements may
occur on whether particular objects are instances of vague concepts. If, however,
information like the applicability conditions and contexts of these elements are known to the
people who perform this task, then their possible interpretation spaces will be reduced.
Also, when vague elements are used within some end-user application, the availability
of vagueness metainformation can help the system’s developers in two ways. i) It will
make them aware of the fact that the ontology contains vague information and thus some
of the system’s output might not be considered accurate by the end-users. ii) They may
use the vagueness metainformation to try to deal with that. For example, the
applicability context of a vague axiom can be used in a recommendation system to explain why a
particular item was recommended. Finally, in dataset integration and evaluation
scenarios, the vagueness metamodel can be used to compare ontologies’ vagueness
compatibility. For example, if the same two vague classes have different vagueness dimensions,
then the one class’s set of instance membership axioms might not be appropriate for the
second’s as it may have been defined with a different vagueness interpretation in mind.
A simple query to the two ontologies’ vagueness metamodel could reveal this issue.
Representing semantic data metainformation is common in the community, like the
VoID vocabulary for describing Linked datasets [
In this paper we considered vagueness in semantic data and we demonstrated the need and potential benefits of making the latter vagueness-aware by annotating their elements with a metaontology that explicitly describes the vagueness’s nature and characteristics. The idea is that even though the availability of the metainformation will not eliminate vagueness, it will manage to reduce the high level of disagreement and low level of comprehensibility it may cause. This increased semantic data comprehensibility and shareability we intend to establish in our future work through user-based experiments.
The research has been funded from the People Programme (Marie Curie Actions) of the European Union’s 7th Framework Programme P7/2007-2013 under REA grant agreement no 286348.