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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.
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K-DRIVE
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
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K-DRIVE VaguenessVagueness & Semantic Data
●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?
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K-DRIVE Examples of Vague Semantic DataVagueness & 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)
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K-DRIVE VaguenessVagueness & Semantic Data
●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).
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K-DRIVE Our Threefold PositionVagueness & Semantic Data
1. Vagueness may be found not only within isolated application-specific 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.
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K-DRIVE Vagueness ConsequencesVagueness & Semantic Data
●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!.
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K-DRIVE Vagueness in Data Creation and Exploitation ScenariosVagueness & Semantic Data
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.
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K-DRIVE Vagueness Consequences in Use Case ScenariosVagueness & Semantic Data
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.
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K-DRIVE Rationale for Vagueness-Aware DataVagueness & Semantic 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.
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K-DRIVE Proposed MetainformationA Metaontology for Vagueness
E.g. the concept “Low Budget” has quantitative vagueness while the
concept “Expert Consultant” qualitative.
“E.g. the term “Strategic Client" is vague in the dimension of the
revenue it generates”
E.g. “Company X is a strategic client” only for the purposes of R&D
collaboration.
E.g. “Company X is a strategic client” is asserted by the Financial
Manager.
Vagueness Type
Vagueness dimensions and applicability criteria
Applicability Context
Element Creator
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K-DRIVE Metamodel ClassesA Metaontology for Vagueness
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K-DRIVE Metamodel PropertiesA Metaontology for Vagueness
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K-DRIVE Production & ConsumptionVagueness-Aware Semantic Data
●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 vagueness-aware data easier and more efficient.
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K-DRIVE Examples of Annotated Vague Semantic DataVagueness-Aware Semantic Data
Vague Class with Type and Dimensions Vague Relation with Type, Dimension and Context
Vague Axioms with Context and Creator
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K-DRIVE Supported Competency QuestionsVagueness-Aware Semantic Data
●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?
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K-DRIVE Usage and BenefitsVagueness-Aware Semantic Data
Metamodel Usage Expected Benefits
Structuring Data with a Vague Ontology
• Communicate the meaning of the vague elements to the domain experts.
• Use the metamodel to characterize the created data's vagueness.
• Make the job of the experts easier and faster and reduce disagreements among them.
Utilizing Vague Semantic Data in an Ontology-Based System
• Check which data is vague.
• 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.
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K-DRIVE Concluding RemarksVagueness-Aware Semantic Data
●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.
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K-DRIVE Future WorkVagueness-Aware Semantic Data
●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.
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K-DRIVE Questions?Thank you!
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