Vagueness in Semantic Information Management

1,206 views
1,121 views

Published on

The phenomenon of vagueness, manifested by terms and concepts like Tall, Red, Modern, etc., is quite common in human knowledge and it is related to our inability to precisely determine the extensions of such terms due to their blurred applicability boundaries. In the context of Ontologies and Semantic Web, vagueness is primarily treated by means of Fuzzy Ontologies, namely extensions of classical ontologies that apply truth degrees to vague ontological elements in an effort to quantify their vagueness and reason with it. Nevertheless, while a number of fuzzy conceptual formalisms and fuzzy ontology language extensions for representing vagueness in ontologies have been proposed by the community, the methodological issues entailed within the development process of such ontologies have been rather neglected. In this talk we position vagueness within the overall lifecycle of semantic information management and we present IKARUS-Onto, a methodology for engineering fuzzy ontologies that covers all typical ontology development stages, from specification to validation.

Published in: Technology, Education, Spiritual
1 Comment
1 Like
Statistics
Notes
No Downloads
Views
Total views
1,206
On SlideShare
0
From Embeds
0
Number of Embeds
649
Actions
Shares
0
Downloads
11
Comments
1
Likes
1
Embeds 0
No embeds

No notes for slide
  • I’ll talk about Semantic Web and how it affects information management research I’ll talk about the problem of vagueness in information management and how IKARUS, a framework I developed in PhD, can help towards dealing with it And I’ll talk, finally, about of my short and long term research plans
  • I’ll talk about Semantic Web and how it affects information management research I’ll talk about the problem of vagueness in information management and how IKARUS, a framework I developed in PhD, can help towards dealing with it And I’ll talk, finally, about of my short and long term research plans
  • Now, the answer to the user’s query is really in the CIA world fact book. So, what we really need is to be able to access the web like a single global database, rather than a repository of documents. But how can we integrate all this data under a common schema?
  • Ontologies
  • I’ll talk about Semantic Web and how it affects information management research I’ll talk about the problem of vagueness in information management and how IKARUS, a framework I developed in PhD, can help towards dealing with it And I’ll talk, finally, about of my short and long term research plans
  • As an example consider the following dialogue…
  • I’ll talk about Semantic Web and how it affects information management research I’ll talk about the problem of vagueness in information management and how IKARUS, a framework I developed in PhD, can help towards dealing with it And I’ll talk, finally, about of my short and long term research plans
  • So my research focused on satisfying these requirements
  • And the result was IKARUS…
  • IKARUS CBR: Novel information management paradigm based on the Case Based Reasoning paradigm and extending it with Fuzzy Ontology representation and reasoning capabilities IKARUS Platform: Semantic in the sense that its design is based on Semantic Web standards and technologies. Commercialized by my former employer IMC technologies and applied in various projects
  • Around 40% improvement compared to not using any methodology at all
  • I’ll talk about Semantic Web and how it affects information management research I’ll talk about the problem of vagueness in information management and how IKARUS, a framework I developed in PhD, can help towards dealing with it And I’ll talk, finally, about of my short and long term research plans
  • And the framework I use to plan and realize this statement looks like this
  • Vagueness in Semantic Information Management

    1. 1. 1 l July 15, 20131 l Dealing with Vagueness in Ontologies and Semantic Data – A Methodological Perspective Dr. Panos Alexopoulos Senior Researcher in Semantic Technologies iSOCO S.A. University of Aberdeen 9/4/2013
    2. 2. What will I talk about Semantic Web and Information Management The IKARUS-Onto Methodology Roadmap & Ongoing Research Vagueness in Semantic Information
    3. 3. What will I talk about Semantic Web and Information Management The IKARUS-Onto Methodology Roadmap & Ongoing Research Vagueness in Semantic Information
    4. 4. The Semantic Web „The Semantic Web is an extension of the current web in which information is given well- defined meaning, better enabling computers and people to work in co-operation.“ [Berners-Lee et al., 2001] „The Semantic Web is a collaboration of the World Wide Web Consortium (W3C) and others to provide a standard for defining structured data on the Web.“
    5. 5. The Semantic Web Vision Use the Web like a single global database Move from a Web of documents to a Web of Data MovieDBMovieDB CIA World FactBook CIA World FactBook But how can we integrate all this information? Slide by Boris Villazon-Terrazas
    6. 6. 1. By structuring and interlinking web information Global Identifier: URI (Uniform Resource Identifier), which is a string of characters used to identify a name or a resource on the Internet. http://cia.../Bolivia http://imdb.../TLLuvia Data Model: RDF (Resource Description Framework), which is a standard model for data interchange on the Web http://.../population http://.../name 8000000 “Even the Rain” http://.../filming_location MovieDBMovieDB CIA World FactBook CIA World FactBook Slide by Boris Villazon-Terrazas
    7. 7. 2. By adding meaning with ontologies „Ontologies are explicit descriptions of domains ... ... that establish a joint terminology between members of a community of interest (human or machines)... ... by standardizing and formalizing the meaning of terms … ... through the definition of concepts, relations and axioms“
    8. 8. Linked Open Data (2011) Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
    9. 9. Semantic Web Information Management & Access Paradigm Streaming resources Slide by Boris Villazon-Terrazas
    10. 10. What will I talk about Semantic Web and Information Management The IKARUS-Onto Methodology Roadmap & Ongoing Research Vagueness in Semantic Information
    11. 11. Vagueness „Vagueness is a semantic phenomenon where predicates admit borderline cases, namely cases where it is not determinately true that the predicate applies or not“ [Shapiro, 2006] This happens when predicates 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?
    12. 12. Vagueness in human communication I am telling you this is a strategic client for the firm with large-budget projects! Come on, $300,000 in two years is hardly a large budget!
    13. 13. Vagueness in human computer interaction I would like an inexpensive modern restaurant near the city centreThere is a restaurant 3km away, is that near or not?
    14. 14. Why care about vagueness in the Semantic Web? ● To improve semantic information access: Human users will probably never stop using vague terminology, so systems need to learn to deal with it. ● To improve semantic interoperability: Semantic information is based on human knowledge and the latter’s vagueness can always cause disagreements and meaning misalignments. ● To increase data coverage: Trying to avoid vagueness in semantic data can exclude really useful knowledge.
    15. 15. What will I talk about Semantic Web and Information Management The IKARUS-Onto Methodology Roadmap & Ongoing Research Vagueness in Semantic Information
    16. 16. Requirements for handling and managing vagueness ●Detect, identify and analyze vagueness in information and knowledge sources. Vagueness Recognition Vagueness Modeling Vagueness Exploitation ●Conceptualize and semantically represent vague information in an explicit, shareable and machine-processable way. ●Take advantage of the modelled vagueness to provide more accurate and complete knowledge-intensive services to users.
    17. 17. The IKARUS Framework „Imprecise Knowledge Acquisition, Representation and Use“ ●Methodology for modeling vague domain knowledge in the form of fuzzy ontologies.IKARUS-Onto IKARUS-CBR IKARUS- Platform ●Fuzzy ontology-based framework for managing and retrieving information objects in vague domains. ●Software platform for implementing applications that manage and exploit vague semantic information.
    18. 18. IKARUS application ● Intelligent Information Access ● Electronic Libraries ● Decision Support ● Contact Centers ● eParticipation Application Domains ● Energy Market ● History and Paleography ● Pre-sales Management ● IT Support ● Culture Knowledge Domains
    19. 19. IKARUS-Onto Fuzzy ontologies extend traditional ontologies by using Fuzzy Set Theory to quantify vagueness through degrees of truth: •E.g. being 36 years old is considered young to a degree of 0.4. IKARUS-Onto is a methodology that defines a structured process for modeling vagueness with fuzzy ontologies easier, faster and more effectively (according to our experiments)
    20. 20. IKARUS-Onto 1. People often confuse vagueness with uncertainty (in the sense of probability), inexactness, ambiguity etc. ● Most frequent question in ESWC 2010 poster session while presenting a fuzzy ontology: “Are these degrees probabilities?” 2. Domain experts and/or ontology users cannot really understand what fuzzy degrees are supposed to represent and thus decide/judge their values. ● Most frequent question/claim by historians when asking them to populate the fuzzy relation “hasPlayedImportantRoleInEvent”: “What are the criteria of importance?” 3. Guidelines and practices for documenting design decisions in traditional ontology engineering have not evolved so as to cover fuzzy ontologies as well. Experiences from developing and working with fuzzy ontologies
    21. 21. IKARUS-Onto 1. Ontology engineers and domain experts should be able to identify easily and correctly the domain knowledge that is vague. 2. Domain experts and/or ontology users should intuitively decide or judge which should approximately be the values of the ontology’s fuzzy degrees. 3. The ontology’s fuzzy degrees should reflect the interpretation of the domain’s vagueness as accurately as possible. 4. The fuzzy ontology should be comprehensible and shareable among human users through the explicit documentation of the intended meaning of the vagueness’s elements and their degrees. Goals
    22. 22. IKARUS-Onto Steps Acquire Crisp Ontology Establish Need for Fuzziness ● Establish a basis for the development of the fuzzy ontology. ● Develop or acquire the crisp ontology. ● Justify and estimate the necessary work for the fuzzy ontology development. ● Ensure existence of vagueness in the domain. ● Ensure vagueness is a requirement. ● Conceptualization of vagueness in an explicit way and shareable way. ● Define fuzzy ontology elements ● Define or generate fuzzy degrees and membership functions. ● Make fuzzy ontology machine- processable. ● Select fuzzy ontology language and use it to represent the defined elements. ● Ensure adequate and correct capturing of the domain’s vagueness ● Check correctness, accuracy, completeness and consistency. Step Goals Actions Define Fuzzy Ontology Elements Formalize Fuzzy Elements Validate Fuzzy Ontology
    23. 23. Clarifying Vagueness ● Vagueness involves predicates that admit borderline cases namely cases where it is unclear whether or not the predicate applies. ● E.g. Tall, Near, Expert, Modern etc. Definition ● Uncertainty: “Today it might rain” ● Inexactness: “Paul is between 25 and 30 years old” Confused Notions ● Degree vagueness: The existence of borderline cases stems from the lack (or at least the apparent lack) of precise predicate applicability boundaries along some dimension. ● E.g. Bald, Tall etc. ● Combinatory vagueness: The predicate has many applicability conditions, yet it is not possible to determine which of these are sufficient for its application. ● E.g. Religion, Expert, Strategic etc. Vagueness Types
    24. 24. Detecting Vagueness in Ontologies ● A concept is vague if it admits borderline cases, i.e. if there are (or could be) individuals for which it is indeterminate whether they instantiate the concept. ● Usual suspects: ● Concepts that denote some phase or state (e.g Adult, Child) ● Attributions, namely concepts that reflect qualitative states of entities (e.g., Red, Big, Broken etc.) Vague Concepts ● Such terms are identified by considering the ontology’s attributes and assessing whether their potential values can be expressed through vague terms. ● E.g. gradable attributes such as size or height give rise to terms such as large, tall, short, etc. Vague Datatype Terms ● A relation is vague if there are (or could be) pairs of individuals for which it is indeterminate whether they stand in the relation. Vague Relations
    25. 25. Fuzzy Ontology Elements ● A fuzzy ontology concept may have instances that belong to it at certain degrees. ● E.g. “John is a TallPerson to a degree of 0.5”. Fuzzy Concepts ● A fuzzy ontology relation links pairs of concept instances to certain degrees. ● E.g. “John is expert at Machine Learning to a degree of 0.9”. ● Similarly, a fuzzy attribute assigns literal values to concept instances at certain degrees. Fuzzy Relations and Attributes ● A fuzzy datatype consists of a set of vague terms which may be used within the ontology as attribute values. ● E.g. Low, Average, High for the attribute Project Budget. ● Each term is mapped to a fuzzy set that defines the term’s meaning. Fuzzy Datatypes
    26. 26. Defining Fuzzy Ontology Concepts/Relations Identify Element Determine Vagueness Type ● Competitor ● belongsToFilmGenre ● Degree Vagueness ● Combinatory Vagueness ● Degree vagueness in the dimension of the number of common technologies. ● Lack of minimum concrete criteria for classifying films to a given genre ● The degree to which the number of common technologies make the given company a competitor ● The degree to which the film’s characteristics classify it to the given genre. ● “Company X is a competitor to a degree of 0.7” ● “The Shining is a horror film to a degree of 0.8” Step Example 1 Example 2 Describe Vagueness Meaning/Source Describe fuzzy degree interpretation Generate Fuzzy Degrees
    27. 27. Defining Fuzzy Ontology Datatypes Identify Datatype Identify fuzzy datatype terms ● Project Budget ● Consulting Experience ● Low, Average, High ● Junior, Senior, Veteran Step Example 1 Example 2 Generate Term Membership Functions
    28. 28. Fuzzy Ontology Formalization and Validation ● Typically extensions of description logics: ● Fuzzy OWL 2 (Bobillo & Straccia) ● Fuzzy OWL 2 QL (Pan et al) ● … ● Important choice parameters: ● The range and expressivity of supported fuzzy ontological elements ● The range of supported fuzzy reasoning capabilities ● Supporting tools like editors, reasoners etc. Fuzzy Ontology Languages ● Correctness: All the fuzzy ontology elements convey a meaning which is indeed vague ● Accuracy: The fuzzy degree are perceived as natural and relatively accurate by those who use the ontology. ● Completeness: All the vague elements have been identified and represented within the ontology. Vague Relations
    29. 29. IKARUS-Onto Evaluation Process Formation of 3 teams IKARUS-Onto Training ● Team 1 to develop a fuzzy ontology without IKARUS-Onto ● Team 2 to do the same with IKARUS-Onto ● Team 3 to validate and compare the two resulting ontologies ● Teams 2 and 3 are trained in using IKARUS-Onto ● Paralled development of the same fuzzy ontology by teams 1 and 2 ● Team 3 validates and compares the 2 developed ontologies ● Evaluation of the whole process by the 3 teams Step Description Fuzzy Ontology Development Fuzzy Ontology Validation Feedback & Evaluation
    30. 30. IKARUS-Onto Evaluation Process ● Knowledge engineers and domain experts of the teams that were trained in the methodology: 1. How easy did you find the task of becoming familiar with the whole process and applying it in practice? ● Domain experts of the two developing teams: 1. How easy was it for you to identify vague knowledge within the given ontology? 2. How easy was it for you to assign fuzzy degrees to the defined fuzzy elements? ● Knowledge engineers of the two developing teams: 1. How easy it was for you to guide the domain experts in their tasks (identification of vague knowledge and assignment of fuzzy degrees)? Evaluation Questions
    31. 31. IKARUS-Onto Evaluation Process ● Knowledge engineer of the evaluation team: 1. How easy was for you to determine the criteria of the validation and evaluation process when you weren’t aware of IKARUS-Onto? ● Domain experts of the evaluation team: 1. Given the validation criteria of IKARUS-Onto, but not the rest of the methodology, how easy was it for you to perform the validation? 2. How easy was it after knowing the whole IKARUS-Onto? ● Which ontology was easier to validate and how did each ontology perform in terms of completeness, correctness, and accuracy? Evaluation Questions
    32. 32. IKARUS-Onto Evaluation Results
    33. 33. IKARUS-Onto Evaluation Results
    34. 34. IKARUS-Onto Evaluation Results
    35. 35. What will I talk about Semantic Web and Information Management The IKARUS-Onto Methodology Roadmap & Ongoing Research Vagueness in Semantic Information
    36. 36. Vagueness in the Semantic Information Lifecycle Information & Knowledge Management Business Intelligence Interactive Systems Semantic Publishing and Linked Data Semantic Information Reuse Semantic Information Retrieval Conceptual and Ontological Modeling Representation Languages and Standards Modeling Methodologies and Processes Model & Represent Vague Semantic Information Use & Exploit Share & Access Acquire & Generate Semantic Information Extraction Knowledge Elicitation & Acquisition Ontology Learning
    37. 37. Current ongoing research ● Problem: Manual definition of the degrees and membership functions of the fuzzy elements is difficult: ● Too many! ● High level of subjectivity. ● Context dependence. ● Changing interpretations ● Idea: Utilize application-specific user input: ● Develop, initialize and deploy the fuzzy ontology. ● Define and use an application-dependent mechanism for generating and gathering vague assertions. ● E.g. in the project PARLANCE we use dialogues between the system and the users to elicit vagueness-related feedback. ● Use the vague assertions to generate fuzzy degrees and membership functions for the respective elements. 1. Automating the fuzzy degree acquisition process
    38. 38. Current ongoing research ● Problem: Comprehensibility and shareability of (both crisp and fuzzy) ontologies is hindered by the lack of adequate description/documentation of their vagueness’s characteristics: ● Users often disagree with the existing definitions of vague elements ● User often misinterpret the intended meaning of a vague term and use it wrongly. ● Idea: Define and use a vagueness meta-ontology to describe and share the characteristics of vague elements: ● Vagueness Type ● Vagueness Dimensions ● Applicability Context ● … 2. Making ontologies vagueness-aware
    39. 39. References ● P. Alexopoulos, M. Wallace, K. Kafentzis, D. Askounis (2011), “IKARUS-Onto: A Methodology to Develop Fuzzy Ontologies from Crisp Ones”, Knowledge and Information Systems, Volume 32, Issue 3, Page 667-695 ● P. Alexopoulos (2013), “Engineering Fuzzy Ontologies for Semantic Processing of Vague Knowledge”, Semantic Multimedia Analysis and Processing, CRC Press, 2013
    40. 40. Thank you for your attention! Dr. Panos Alexopoulos Senior Researcher in Semantic Web Technologies Email:  palexopoulos@isoco.com Web: www.panosalexopoulos.com LinkedIn: www.linkedin.com/in/panosalexopoulos Twitter: @PAlexop

    ×