Fuzzy Ontology Construction Based on Incomplete Knowledge

There exists widely incomplete knowledge all over the world, but incomplete knowledge still cannot be dealt with in the process of ontology construction. Hence, a method for fuzzy ontology construction based on incomplete knowledge is proposed. First, the calculation principle of the attribute weight of the ontology concept is presented, and the calculation function of the attribute weight is derived through experiments. Then, the membership degree of the incomplete individual to the concept is computed. Finally, the incomplete individual is classified according to the principle of the variable precision rough set model. The experimental results show that the average precision of the classification of the incomplete individuals is 81.7% when the common attributes are omitted and that it is difficult to classify the incomplete individuals correctly when the private attributes are omitted. This method is significant for handling incomplete knowledge in the process of ontology construction.

Rules-based object-relational databases ontology construction

To solve the problems of shaving and reusing information in the information system, a rules-based ontology constructing approach from object-relational databases is proposed. A 3-tuple ontology constructing model is proposed first. Then, four types of ontology constructing rules including class, property, property characteristics, and property restrictions ave formalized according to the model. Experiment results described in Web ontology language prove that our proposed approach is feasible for applying in the semantic objects project of semantic computing laboratory in UC Irvine. Our approach reduces about twenty percent constructing time compared with the ontology construction from relational databases.

Automatic Ontology Construction Based on Clustering Nucleus

Ontology construction is the core task of ontology-based knowledge representation. This paper explores a semantic description approach based on primitive structure, which benefits ontological relation description in a more precise and concrete way. In view of primitive structure, this paper introduces an approach to extract primitive structures of words based on a multi-label learning model, correlated label propagation. Also, this paper proposes an approach to recognize clustering nucleuses in word clusters heuristically. By this approach, more precise ontological relations are able to be discovered automatically.

Construction of an ontology-based nursing knowledge system

This study proposes the establishment of a knowledge-system ontology in the nursing field. It uses advanced data mining techniques,digital publishing technologies, and new media concepts to comprehensively integrate and deepen nursing knowledge and to aggregate sources of knowledge in specialized technical fields. This study applies all forms of media and transmission channels, such as personal computers and mobile devices, to establish a knowledge-transmission system that provides knowledge services such as knowledge search, update retrieval, evaluation, questions and answers(Q&As), online viewing, information subscription, expert services, push notifications, review forums, and online learning. In doing so, this study creates an authoritative and foundational knowledge service engine for the nursing field, which provides convenient, flexible, and comprehensive knowledge services to members of the nursing industry in a digital format.

Ontology Engineering and Knowledge Services for Agriculture Domain

This paper presents a knowledge service system for the domain of agriculture. Three key issues for providing knowledge services are how to improve the access of unstructured and scattered information for the non-specialist users, how to provide adequate information to knowledge workers and how to provide the information requiring highly focused and related information. Cyber-Brain has been designed as a platform that combines approaches based on knowledge engineering and language engineering to gather knowledge from various sources and to provide the effective knowledge service. Based on specially designed ontology for practical service scenarios, it can aggregate knowledge from Internet, digital archives, expert, and other resources for providing one-stop-shop knowledge services. The domain specific and task oriented ontology also enables advanced search and allows the system ensures that knowledge service could improve the user benefit. Users are presented with the necessary information closely related to their information need and thus of potential high interest. This paper presents several service scenarios for different end-users and reviews ontology engineering and its life cycle for supporting AOS （Agricultural Ontology Services） Vocbench which is the heart of knowledge services in agriculture domain.