Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Towards Vagueness-Aware Semantic Data


Published on

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.

Published in: Technology, Education, Spiritual
  • Be the first to comment

  • Be the first to like this

Towards Vagueness-Aware Semantic Data

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 11. A Metaontology for Vagueness Metamodel Classes 11
  12. 12. A Metaontology for Vagueness Metamodel Properties 12
  13. 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. 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. 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. 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. 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. 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. 19. Thank you! Questions? Dr. Panos Alexopoulos Semantic Applications Research Manager Quieres innovar? (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