the 20th international conference on software engineering and knowledge engineering (seke2008)...

The 20th International Conference on Software Engineering and Knowledge Engineering (SEKE2008)The 20th International Conference on Software Engineering and Knowledge Engineering (SEKE2008)

Department of Electrical and Computer EngineeringDepartment of Electrical and Computer Engineering

http://www.ualberta.ca/~golmoham/http://www.ualberta.ca/~golmoham/

Koosha GolmohammadiKoosha Golmohammadi

Fuzziness in the Semantic Web: Survey and Future DirectionsFuzziness in the Semantic Web: Survey and Future Directions22 of 13 of 13

SEKE2008SEKE2008

• Extracting information from the web is not trivial due to:– exponential growth of the web contents– rapidly growing number of situations on the web

that involve uncertainties or inconsistencies

Standard representation of uncertaintyuncertainty and imprecisionimprecision in the web environment is

highly desirable

Introduction


SEKE2008SEKE2008

• Discuss web utilization situations that would benefit from the application of uncertainty and approximate reasoning

• Review methodologies that can be applied to these situations focusing on fuzzy approaches

• Highlight potentials for future research works that enable agents to provide high quality services in existence of imprecise information

Objectives


SEKE2008SEKE2008

The Semantic Web (the web evolution)


SEKE2008SEKE2008

The Semantic Web (the web evolution) cont.


SEKE2008SEKE2008

The Semantic Web is a “living organism” combining autonomously evolving data

sources/knowledge repositories

The Semantic Web - Web of Data

SW promises:• Define and link the unstructured data on the web in a way that enables

machines for automation, integration and reuse of data across various applications

• Offer developers a framework to make intelligent decisions using logic rules

• Offer an environment in which agents are able to perform tasks on behalf of the user

Integrate data on the web and create a web of data ultimately


SEKE2008SEKE2008

• Information correctness and availability

• Information imprecision

• Concept mapping between ontologies

• Identification and composition of the web services

Example scenarios


SEKE2008SEKE2008

Real world is informal and involves knowledge that is imprecise, uncertain, partially true and approximate

– Answers from different sources come with different degrees of confidence (e.g. query systems)

– Impossible to make boundaries for a lot of concepts (e.g. cheap room, close to downtown etc.)

Fuzzy principles in the SW


SEKE2008SEKE2008

SW knowledge representation using fuzzy methods

Current Status• Extensions to Ontology Web Language (OWL)

– Fuzzy OWL: a class is defined by membership functions and the membership of each object is a fuzzy value

– Fuzzy extension of SHOIN: subsumption relation between classes and the entailment relation is no more crisp

• Extensions to Semantic Web Rule Language (SWRL) – Fuzzy-SWRL: rules atoms can have weights in [0,1]

• Combination of fuzzy logic and Formal Concept Analysis – Fuzzy Ontology Generation frAmework (FOGA): is a framework to represent the

uncertainty information by a fuzzy value


SEKE2008SEKE2008

Future Directions• Automatic construction of fuzzy ontologies (where relationships

among concepts/properties are fuzzy membership degrees) and interaction with crisp ontologies

• Development of fuzzy-based methods and algorithms for matching and comparison of ontologies

• Integration of fuzzy methods and rough sets for representing ontologies to handle different facets of imperfect knowledge

• Development of reasoning systems for fuzzy DL and Fuzzy OWL-DL

SW knowledge representation using fuzzy methods


SEKE2008SEKE2008

Current Status• Collaborative filtering multi-agent model: …

• Soft Semantic Web Services agents: provides high quality semantic web services using fuzzy neural networks and genetic algorithms

• Concept-matching information retrieval system: uses fuzzy synonymy and fuzzy generality to retrieve web pages that are conceptually related to the implicit concepts of the query

• Ambient Intelligent (AmI) systems: provide fuzzy web services - transform rough information on sensors, actuators and services towards “smart data” - using Fuzzy Markup Language

• Semantic Web search agent based on Fuzzy Conceptual Model: to handle the ambiguity and imprecision of the concept on the Internet

• The architecture that treats the trust as a degree that a source can be trusted: introduces a model that takes into account partial trust, distrust and ignorance simultaneously

Semantic Web Services using fuzzy methods


SEKE2008SEKE2008

Future DirectionsDeveloping technologies to support agents to use

imprecise information and reason about it: – selection of most suitable services in the presence of

partial information– integration of atomic services when they are not fully

compatible– supporting user in human-centric multi-criteria decision

making when multiple alternatives and service providers are available

– Open-source tools for automatically identifying levels of information uncertainty and reason about that

Semantic Web Services using fuzzy methods


SEKE2008SEKE2008

Thanks and questions

the 20th international conference on software engineering and knowledge engineering (seke2008)...

Documents