An extension of the crisp ontology for uncertain information modeling : fuzzy ontology map

Abstract

In the current World Wide Web (WWW), users find it difficult to locate relevant information using search engines. This may be due to the fact that the current World Wide Web lacks semantic markup. One of the possible solutions for this problem is Semantic Web. In the latest Semantic Web technology, descriptive markup languages, such as Resource Description Framework (RDF) and Web Ontology Language (OWL), were proposed to model the web content in a machine-readable way which assists information gathering and automatic searching by software agents. Since these ontology markup languages deal with 'hard' semantics with the description and manipulation of crisp data, they are not capable to represent uncertain information. This thesis proposes an extension of the current ontology representation which supports uncertain information modeling. The extension is called Fuzzy Ontology Map (FOM) which is based on the integration of fuzzy theory and graph theory. By considering an ontology as a graph, an ontology graph can be constructed by using a vertex to represent a subject or literal value and an arrow to represent a predicate. Each edge in the ontology graph has a fuzzy membership value. A FOM is a connection matrix which collects the membership values between classes in the ontology graph. Thus, a fuzzy ontology could be created by using the FOM and the ontology document (RDF/OWL). This research also defines a set of algorithms for inferring fuzzy relationships in an FOM. It is possible to use an FOM to develop real-world applications and systems that can deal with imprecise or vague information. To demonstrate how FOM works, two prototype applications were developed, SemTour:HK and iJADE FreeWalker (iJFW). SemTour:HK is a tourist information portal integrating an ontology with the FOM to allow users to perform fuzzy searches. The average processing time for a fuzzy search is around 1.9s which is 0.4s longer than the exact match searching. The prototype was tested on 20 novice users. 80% of the users felt that the system can help tourists find tourist information in Hong Kong. 75% of the users agreed that the fuzzy search function is useful. iJFW is an intelligent agent-based tourist guiding system which is used in mobile devices. It was developed based on the integration of intelligent agent technology (IAT) and Global Positioning System (GPS) with FOM. There are two FOMs in the system: i) a preference FOM in the client side and that logs the users' cuisine interests; ii) an accommodation FOM in the server side for fuzzy searching of accommodation choices. iJFW was tested on a 36.6kbit/s wireless connection. The average response time for a fuzzy search is around 25s. Proportionally, the average processing time for gathering the result for 3G wireless connections would be 3.4s. 30 candidates were invited to answer a questionnaire after using the system. 67% of the candidates felt that the fuzzy search function was useful for finding information about a particular cuisine. 17 candidates thought that iJFW could replace tourist guidebooks. 13 candidates though that iJFW would be a subsidiary tool for traveling.