Fuzzy Ontology Models Based on Fuzzy Linguistic Variable for Knowledge Management and Information Retrieval

Employment of university graduates is important to the overall development of a country. Lack of an effective communication channel between labor supply and demand is one of the most important reasons that lead to the declining employment rate of university graduates. Aiming at this problem, a mobile service application platform is established in this paper. In order to handle uncertainty of employment information and knowledge, the platform adopts a fuzzy ontology model which is capable of fuzzy semantic retrieval and can select appropriate candidates from graduates for a job. Short messages generated from the mobile service application platform will be sent directly to the candidates who are selected by the fuzzy ontology model and likely to be employed by companies. Application of the platform shows that the fuzzy ontology model facilitates the semantic information retrieval and the platform increase the efficiency and accuracy of employment.

Ontology is adopted as a standard for knowledge representation on the Semantic Web, and Resource Description Framework (RDF) is used to add structure and meaning to Web applications. In order to incorporate fuzzy systems into the Semantic Web, this paper utilizes fuzzy ontology to represent formally the fuzzy linguistic variables, considering the semantic relationships between fuzzy concepts. Then fuzzy rule is described as a RDF resource with properties: “IF” and “THEN”, and rule's antecedent and consequent is represented in RDF statement. Taking the fuzzy control system of industrial washing machine for example, the fuzzy system with ontology and RDF is built, which shows that this research enables distributed fuzzy applications on the Semantic Web.

Ontology method has become an important method in computer science, and it has been used in many domains, such as semantic web, search engine and knowledge management. Ontology plays an important role in semantic retrieval because of its specification, its good concept hierarchy and its support for logical reasoning. Domain ontology can be used to extend the user’s retrieval words and index domain knowledge. Under which we can achieve semantic retrieval for domain knowledge. The semantic extension of user’s retrieval words is the more important step in semantic retrieval system. First, this paper designed the model of semantic query expansion based on domain ontology and gave five kinds of semantic expansion methods. Then the model of semantic query expansion is applied in semantic retrieval system. The designed system consists of four main components: ontologies building, retrieval words semantic extension, semantic precomputation for document and semantic retrieval. Finally, this paper makes an experiment on high-speed railway domain. The experimental results show that the developed semantic retrieval system can improve the recall and precision.

Construction of fuzzy ontologies from fuzzy XML models

Recent increase in the use of video-based applications has revealed the need for extracting the content in videos. Raw data and low-level features alone are not sufficient to fulfill the user 's needs; that is, a deeper understanding of the content at the semantic level is required. Currently, manual techniques, which are inefficient, subjective and costly in time and limit the querying capabilities, are being used to bridge the gap between low-level representative features and high-level semantic content. Here, we propose a semantic content extraction system that allows the user to query and retrieve objects, events, and concepts that are extracted automatically. We introduce an ontology-based fuzzy video semantic content model that uses spatial/temporal relations in event and concept definitions. This metaontology definition provides a wide-domain applicable rule construction standard that allows the user to construct an ontology for a given domain. In addition to domain ontologies, we use additional rule definitions (without using ontology) to lower spatial relation computation cost and to be able to define some complex situations more effectively. The proposed framework has been fully implemented and tested on three different domains. We have obtained satisfactory precision and recall rates for object, event and concept extraction.

How to quickly and cheaply construct Web ontologies has become a key technology to enable the Semantic Web. Classical ontologies are not sufficient for handling imprecise and uncertain information that is commonly found in many application domains. In this paper, we propose an approach for constructing fuzzy ontologies from fuzzy UML models, in which the fuzzy ontology consists of fuzzy ontology structure and instances. Firstly, the fuzzy UML model is investigated in detail, and a kind of formal definition of fuzzy UML models is proposed. Then, a kind of fuzzy ontology called fuzzy OWL DL ontology is introduced. Furthermore, we consider the fuzzy UML model and the corresponding fuzzy UML instantiations (i.e., object diagrams) simultaneously, and translate them into the fuzzy ontology structure and the fuzzy ontology instances, respectively. In addition, since a fuzzy OWL DL ontology is equivalent to a fuzzy Description Logic f-SHOIN(D) knowledge base, how the reasoning problems of fuzzy UML models (e.g., consistency, subsumption, equivalence, and redundancy) may be reasoned through reasoning mechanism of f-SHOIN(D) is investigated, which can help to construct fuzzy ontologies more exactly.

Information retrieval (IR) has become an active area of research in recent years to handle with the dynamic environment by establishing intelligent systems which can offer effective web content in real time to various wired or wireless devices. Evolutionary and adaptive systems (EASs) have been widely used. Web information gathering has been widely investigated and a number of approaches have been proposed to carry out web information gathering in an effluent way. In such systems ontology has been expected to have a bright future. In order to represent user profiles in personalized web information gathering several ontology models are widely used. These models are used for representing and reasoning over user profiles. To further improve the overall performance of the ontology based information retrieval system, fuzzy ontology model has been presented. This fuzzy based ontology model learns user profiles from both world knowledge base and user local instance warehouse. This ontology model is evaluated by comparing its performance against benchmark models in web information gathering.

Ontologies play an important role of the applications in recent years, especially on the Semantic Web, Information Retrieval, Information Extraction, and Question Answering. Ontologies are used in order to specify the knowledge that is exchanged and shared between the different systems. Ontologies define the formal semantics of the terms used for describing data, and the relations between these terms. Hence, one of the important steps for building the domain specific ontology is to construct the semantic relations among the terms of the ontology. This paper represents our proposed method for automatic semantic relation extraction from text documents of the ACM Digital Library. A random sample among 170 categories of ACM categories is used to evaluate our proposed method. Results generated show that our proposed method achieves high precision.

Effective ranking and search techniques for Web resources considering semantic relationships

Crisp ontologies become less suitable in all domains in which the concepts to be represented have imprecise definitions. Fuzzy ontologies are developed to overcome this problem. Determining similarity relations among fuzzy ontology components is essential for many reuse subprocesses, for query rewriting as well as for fuzzy ontology modeling. Denning similarity relations within fuzzy context may be realized basing on the linguistic similarity among ontology components or may be deduced from their intensional definitions. The later approach needs to be dealt with differently in crisp and fuzzy ontologies. In the first case, two ontology components are related by a given similarity relation or not. In the second one, two ontology components have a degree to be related by a given similarity relation. This is the scope of this paper.

The existing ontology reasoners are all unilaterally description logic-based or rule-based, and their abilities to deal with fuzzy ontology are limited. It is the diversity of fuzzy ontology representation methods among them that leads to a low processing efficiency of fuzzy ontology. To solve this bottle-neck problem of fuzzy ontology-based knowledge reasoning, fuzzy ontology, fuzzy ontology representation method and fuzzy description logic are investigated, and the existing fuzzy ontology reasoners are comparatively analyzed. By virtue of the advantages of the typical reasoners such as Pellet, JenaAPI and fuzzyDL (fuzzy Description Logic), a method combining rule and description logic reasoning is designed, and a framework about fuzzy ontology based knowledge reasoning is proposed. By reducing fuzzy ontology, checking consistency and repairing inconsistency, the efficiency and accuracy of fuzzy ontology based knowledge reasoning can be improved. Finally, an experiment is given to verify the validity of proposed method.

The reconfigurable manufacturing system is a cost-effective system that can accommodate a variety of equipments required by customers. However, because of the surprisingly increasing volume and semantically fuzzy nature of the equipment information, one of the main difficulties to realize efficient equipment reconfiguration is the lack of effective equipment representation technologies and semantic description methods. In this paper, a fuzzy ontology model is proposed for storing semantic concepts in the reconfigurable manufacturing system. To discuss fuzzy inference mechanism has indicated that the fuzzy ontology-based method can promote productivity of the reconfigurable manufacturing system.

The goal of higher education evaluation and accreditation is to ensure that education provided by institutions meets acceptable levels of quality. In 2011, the higher education evaluation and accreditation council of Taiwan (HEEACT) starts to evaluate the eighty-one higher education institutions through five predefined items, including (1) university goal, (2) university administration and management, (3) teaching and learning resource, (4) performance and society responsibility, and (5) improvement and quality assurance mechanism. As a result, in this paper, a novel fuzzy markup language (FML)-based university assessment system is developed to infer the passing possibility of the university assessment. Additionally, a fuzzy ontology model is utilized to represent the university evaluation and accreditation domain knowledge predefined by domain experts. Then, the FML is adopted to describe the knowledge base and rule base of the university assessment. Simulation results indicate that the proposed approach can effectively infer the passing level of the university assessment.

Extracting information from the medical imaging reports with domain ontology has attracted much attention in medical natural language processing field. Based on the unstructured characteristics of medical image report text, the existing image report domain ontology is constructed by ontology learning method, including ontology learning method based on language and ontology learning method based on machine learning. However, these existing methods ignore the anatomical knowledge embedded in medical imaging reports, which is very useful for extracting entity relationships from reports. In order to solve the above problems, this paper proposes a domain ontology construction method for medical image reporting based on anatomy knowledge, which combines the prior knowledge of pathology and anatomy to obtain the basic framework of the domain ontology as the knowledge driver. In particular, our proposed approach consists of two tasks. The first task is to convert each text report into a semantic tree through an anatomic knowledge based semantic subtree generation algorithm. Semantic subtree generation algorithm mainly consists of three parts: framework positioning, relation extraction and adding relation. Firstly, the text is located based on anatomical knowledge, and the relational extraction mainly adopts the method of dependency syntactic analysis to extract the three semantic relations contained in the text. Finally, add the relationship to the corresponding branch. The second task is to obtain the domain ontology by merging semantic subtree. This step is mainly to obtain the domain ontology by merging the nodes of the XML structure semantic tree. The experimental results show that this method can be used for relational extraction and domain ontology construction, laying a good foundation for the follow-up research.

Events can be characterized by a set of descriptive, collocated keywords extracted documents. Intuitively, documents describing the same event will contain similar sets of keywords, and the graph for a document collection will contain clusters individual events. Helping users to understand the event is an acute problem nowadays as the users are struggling to keep up with tremendous amount of information published every day in the Internet. The challenging task is to detect the events from online web resources, it is getting more attentions . The important data source for event detection is a Web search log because the information it contains reflects users’ activities and interestingness to various real world events. There are three major issues playing role for event detection from web search logs: effectiveness, efficiency of detected events. We focus on modeling the content of events by their semantic relations with other events and generating structured summarization. Event mining is a useful way to understand computer system behaviors. The focus of recent works on event mining has been shifted to event summarization from discovering frequent patterns. Event summarization provides a comprehensible explanation of the event sequence based on certain aspects.