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.

1. Towards Vagueness-Aware Semantic Data
Panos Alexopoulos, Boris Villazon-Terrazas, Jeff Z. Pan
9th International Workshop in Uncertainty Reasoning for the Semantic Web
Sydney, Australia, October 21st, 2013

2. Vagueness & Semantic Data
Vagueness
●Semantic data and ontologies
● Semantic data: data annotated with ontological vocabulary
● Ontological vocabulary is defined in ontologies
● Ontologies meant to capture shared understanding
●What happens when vocabularies have blurred boundaries:
● What’s the threshold number of years separating old and not old
films?
● What are the exact criteria that distinguish modern restaurants
from non-modern?
2

3. Vagueness & Semantic Data
Examples of Vague Semantic Data
●Vague categorization relations where entities are assigned to
categories with no clear applicability criteria.
● “hasFilmGenre” (LinkedMDB and Freebase).
● “dbpedia-owl:ideology“ and “dbpedia-owl:movement“ (DBPedia)
●Specializations of concepts according to some vague property of
them.
● “Famous Person" and “Big Building" (Cyc Ontology)
● “Managerial Role” and “Competitor“ (Business Role Ontology)
3

4. Vagueness & Semantic Data
Vagueness
●Two main types:
● Quantitative: Lack of crisp applicability boundaries for the
predicate in one or more dimension (e.g., Tall, Rich, Recent etc.)
● Qualitative: Inability to define sufficient applicability criteria for
the predicate (e.g., Modern, Expert, Religion etc.)
●Additional characteristics:
● Subjectiveness: The same vague term can be differently
interpreted and/or applied by different users.
● Context dependence: The same vague term can be differently
interpreted or applied in different contexts (even if the user is the
same).
4

5. Vagueness & Semantic Data
Our Threefold Position
1. Vagueness may be found not only within isolated applicationspecific semantic data but also in public datasets that are meant
to be shareable and reusable (e.g. Linked Open Data).
2. This vagueness may hamper the comprehensibility and
shareability of these datasets and cause problems in a number of
different use case scenarios.
3. The negative effects vagueness may cause, can be partially tackled
by annotating the vague data elements with metainformation
that explicitly describes the vagueness's nature and characteristics.
5

6. Vagueness & Semantic Data
Vagueness Consequences
●The problem with vague terms in semantic data is that they may
cause disagreements among the people who develop it, maintain it or
use it.
●E.g., when we asked domain experts to provide instances of the
concept Critical Business Process, there were certain processes for
which there was a dispute among them about whether they should be
regarded as critical or not
●The problem was that different experts had different criteria of process
criticality and could not decide which of those criteria were sufficient
to classify a process as critical.
●In other words, qualitative vagueness!.
6

7. Vagueness & Semantic Data
Vagueness in Data Creation and Exploitation Scenarios
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 are actually
instances of them or not.
2. Utilizing Vague Facts in Ontology-Based Systems: When
knowledge-based systems utilize vague facts as part of their
reasoning, then their output might not be optimal for its users when
the latter disagree with these facts.
7

8. Vagueness & Semantic Data
Vagueness Consequences in Use Case Scenarios
3. Integrating Vague Semantic Information: When semantic data
from different sources need to be merged in a single dataset then
the merging of particular vague elements can lead to data that will
not be valid for all its users.
4. Evaluating Vague Semantic Datasets for Reuse: When data
practitioners need to decide whether a particular dataset is suitable
for their needs, then the existence of vague elements in it can make
it difficult to assess a priori whether the data related to these
elements are valid for their application context.
8

9. Vagueness & Semantic Data
Rationale for Vagueness-Aware Data
●Vagueness-aware semantic data is “data whose vague ontological
elements are accompanied by comprehensive metainformation that
describes the nature and characteristics of their vagueness”.
●E.g., a useful piece of metainformation is the set of applicability
criteria that the element creator had in mind when defining the
element .
●When two datasets have the same vague concept, the knowledge of
these criteria can:
● Prevent their merging in case these criteria are different.
● Help a data practitioner decide which of these two concepts'
associated instances are more suitable for his/her application.
9

10. A Metaontology for Vagueness
Proposed Metainformation
Vagueness Type
E.g. the concept “Low Budget” has
quantitative vagueness while the
concept “Expert Consultant” qualitative.
Vagueness dimensions
and applicability criteria
“E.g. the term “Strategic Client" is
vague in the dimension of the
revenue it generates”
Applicability Context
E.g. “Company X is a strategic
client” only for the purposes of R&D
collaboration.
Element Creator
E.g. “Company X is a strategic
client” is asserted by the Financial
Manager.
10

11. A Metaontology for Vagueness
Metamodel Classes
11

12. A Metaontology for Vagueness
Metamodel Properties
12

13. Vagueness-Aware Semantic Data
Production & Consumption
●Our metamodel is meant to be used by both producers and
consumers of ontologies and semantic data.
● The former to annotate the vague part of their produced
ontologies with relevant vagueness metainformation
● The latter to query this metainformation and use it to make a
better use of the data.
●The annotation task is primarily manual and should take place in the
course of the ontology's construction and evolution process.
●Future research should focus on making production of vaguenessaware data easier and more efficient.
13

14. Vagueness-Aware Semantic Data
Examples of Annotated Vague Semantic Data
Vague Class with Type and Dimensions
Vague Relation with Type, Dimension and Context
Vague Axioms with Context and Creator
14

15. Vagueness-Aware Semantic Data
Supported Competency Questions
●What elements have been defined as vague?
●What elements have qualitative vagueness?
●What elements have quantitative vagueness, in what contexts and in
what dimensions?
●How many different applicability contexts does the relation
“isExpertAtResearch” have?
●Who asserts that “Accenture is a Competitor” and in what context?
15

16. Vagueness-Aware Semantic Data
Usage and Benefits
Metamodel Usage
• Communicate the meaning of the vague
elements to the domain experts.
Structuring Data with a Vague
Ontology
• Use the metamodel to characterize the created
data's vagueness.
Expected Benefits
• Make the job of the experts easier
and faster and reduce
disagreements among them.
• Check which data is vague.
Utilizing Vague Semantic Data
in an Ontology-Based System
• Use the properties of the vague elements to
provide vagueness-related explanations to the
users.
• Know a priori which data may
affect the system's effectiveness.
Integrating Vague Semantic
Datasets
• Compare same vague elements across
datasets according to their vagueness type
and dimensions
• Know a priori which data may
affect the system's effectiveness.
Evaluating Vague Semantic
Datasets for Reuse
• Query the metamodel to check the vagueness
compatibility of the dataset with the intended
domain and application scenario.
• Avoid re-using (parts of) datasets
that are not compatible to own
interpretation of vagueness.
16

17. Vagueness-Aware Semantic Data
Concluding Remarks
●The overall idea behind our proposed approach is that the
availability of vagueness metadata in ontologies and semantic data
will manage to reduce the high level of disagreement and increase
the low level of comprehensibility it may cause.
●In that sense, it can be a major step towards achieving better
common understanding, and thus shareability, of vague semantic
information in the Semantic Web.
●Moreover, our approach is complementary to any fuzzy ontology
related work, in the sense that it may be used to provide better
explanations on the intended meaning of fuzzy degrees.
17

18. Vagueness-Aware Semantic Data
Future Work
●We are currently working towards three directions:
● Express the metamodel in a more formal way.
● Evaluate its usefulness by means of qualtitative experiments that
will measure the level of increased semantic data
comprehensibility and shareability it may achieve.
● Facilitate and encourage the practical use of the metamodel
within the Semantic Data Lifecycle by means of relevant
guidelines, methods and tools.
18

19. Thank you!
Questions?
Dr. Panos Alexopoulos
Semantic Applications Research
Manager
Quieres
innovar?
palexopoulos@isoco.com
(t) +34 913 349 797
iSOCO Barcelona
iSOCO Madrid
iSOCO Pamplona
iSOCO Valencia
iSOCO Colombia
Av. Torre Blanca, 57
Edificio ESADE CREAPOLIS
Oficina 3C 15
08172 Sant Cugat del Vallès
Barcelona, España
(t) +34 935 677 200
Av. del Partenón, 16-18, 1º7ª
Campo de las Naciones
28042 Madrid
España
(t) +34 913 349 797
Parque Tomás
Caballero, 2, 6º4ª
31006 Pamplona
España
(t) +34 948 102 408
C/ Prof. Beltrán Báguena, 4
Oficina 107
46009 Valencia
España
(t) +34 963 467 143
Complejo Ruta N
Calle 67, 52-20
Piso 3, Torre A
Medellín
Colombia
(t) +57 516 7770 ext. 1132
Key Vendor
Virtual Assistant 2013
19