Mining Fuzzy Domain Ontology Based on Concept Vector from Wikipedia Category Network

Due to the diverse nature of business products and operations in different geographic locations, petroleum industries demand more accurate and precise information and data. Ontology is playing an important role in knowledge management and sharing, both users and system can communicate with each other using a common understanding knowledge of a domain. This study proposes a context-based ontology construction method for extracting petroleum exploration domain ontology from unstructured Chinese text documents. The proposed approach includes the steps of (i) domain documents preprocessing, (ii) concept clustering based on the fuzzy c-means, (iii) context extraction, and (iv) domain ontology construction. Experimental results show the effectiveness and feasibility of the proposed approach.

Opinion mining based on fuzzy domain ontology and Support Vector Machine: A proposal to automate online review classification

Helpful online reviews could be utilized to create sustainable marketing strategies in the restaurant industry, which contributes to national sustainable economic development. This study, the main aspects (including food/taste, experience, location, and value) from 294,034 reviews on Yelp.com were extracted empirically using the Latent Dirichlet Allocation (LDA) and positive and negative sentiment were assigned to each extracted aspect. Positive sentiments were associated with food/taste, while negative sentiments were associated with value. This study further proves a robust classification algorithm based on Support Vector Machine (SVM) with a Fuzzy Domain Ontology (FDO) algorithm outperforms other traditional classification algorithms such as Naïve Bayes (MB) and SVM ontology in predicting the helpfulness of online reviews. This study enriches the literature on managerial aspects of sustainability by analyzing a large amount of plain text data that customers generated. The results of this study could be used as sustainable marketing strategy for review website developers to design sophisticated, intelligence review systems by enabling customers to sort and filter helpful reviews based on their preferences. The extracted aspects and their assigned sentiment could also help restaurateurs better understand how to meet diverse customers’ needs and maintain sustainable competitive advantages.

We define a general framework for ontology-based information retrieval (IR). In our approach, document and query expansion rely on a base taxonomy that is extracted from a lexical database or a Linked Data set (e.g. WordNet, Wiktionary etc.). Each term from a document or query is modelled as a vector of base concepts from the base taxonomy. We define a set of mapping functions which map multiple ontological layers (dimensions) onto the base taxonomy. This way, each concept from the included ontologies can also be represented as a vector of base concepts from the base taxonomy. We propose a general weighting schema which is used for the vector space model. Our framework can therefore take into account various lexical and semantic relations between terms and concepts (e.g. synonymy, hierarchy, meronymy, antonymy, geo-proximity, etc.). This allows us to avoid certain vocabulary problems (e.g. synonymy, polysemy) as well as to reduce the vector size in the IR tasks.

Domain ontology, encompassing both concepts and entities, along with their relations and properties, is a new medium for the storage and propagation of domain specific knowledge. For all of its associated advantages, a great deal of effort must still be expended during ontology construction, including collecting the domain-related vocabularies, developing the domain concept hierarchy, defining the properties of each concept and relationships between concepts. In recent years, several engineering handbooks have described detailed domain knowledge via organization into categories, sections, and chapters with indices in the appendix. In this paper, an approach is proposed with a view to extracting concepts, entities, properties and relationships from a handbook of a specific domain. The extracted information can then be reorganized and converted into OWL (Web Ontology Language) format to represent the domain ontology. The generation of an earthquake engineering ontology from an Earthquake Engineering Handbook is used to illustrate the proposed

Using the techniques and theories of natural language processing (NLP), this paper puts forward threads and ways to build automatically domain ontologies based on text content. Through restructuring and utilizing the generally-acknowledged domain knowledge in medical domain, this paper also constructs a concept system of the multidimensional model of modern medicine towards clinic medicine, realizing the automatic construction and acquisition of modern medical knowledge specification system. This further provides theoretical foundation and technical support for automatic construction of professional domain ontologies.

Domain ontology, which is widely used in knowledge engineering, artificial intelligence and semantic Web domain, describes concepts of the entities and mutual relationships between them. However, there is no existing research on construction of database domain ontology. Domain ontology of database will help to optimize associative knowledge learning in teaching and develop the intelligent tutoring system. It can also maximize domain knowledge sharing and reusing. In this paper, database domain ontology is constructed, and then we achieve the visualization. Finally we also implement the persistent storage of ontology and query.

This paper describes an approach and an implementation for the semantic integration of social and domain ontologies in a collaborative platform. The platform, built based on the Drupal CMS framework, aims to offer SMEs possibilities to access specific knowledge by means of a collaborative community. A model to semantically express the socio-collaborative activities of the platform connected with the domain knowledge related to the project is presented. The model is based on existing W3C ontologies such as FOAF, SIOC and SIOC-types for the socio-collaborative semantics and SKOS to describe the domain knowledge. An architecture to support the model and related applications of the semantic metadata generated in the platform are also described.

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.

Information and resources available on the Web are growing increasingly and web users need to have a common understanding of them. The Semantic Web whose most important role is to help machine to understand and analyze the existing data on the Web, has not been used commonly, yet. The foundation of the Semantic Web are ontologies. Ontologies play the main role in the exchange of information and development of the Lexical Web to the Semantic Web. Manual construction of ontologies is time-consuming, expensive, and dependent on the knowledge of domain engineers. Also, Ontologies that have been extracted automatically from corpus on the Web might have incomplete information. The main objective of this study is describing a method to improve and expand the information of the ontologies. Therefore, this study first discusses the automatic construction of prototype ontology in animals‟ domain from Wikipedia and then a method is presented to improve the built ontology. The proposed method of improving ontology expands ontology concepts through Bootstrapping methods using a set of concepts and relations in initial ontology and with the help of the Google search engine. A confidence measure was considered to choose the best option from the returned results by Google. Finally, the experiments showed the information that was obtained using the proposed method is twice more accurate than the information that was obtained at the stage of automatic construction of ontology from Wikipedia.

From this chapter, we present the technical part of this book including Chapters 6, 7, 8 and 9. The technical part deals with problems related to specific methods, algorithms, and techniques which are the implementation of the cognition-driven decision process (CDDP) model. This chapter discusses two kinds of domain knowledge used in the CDDP model: ontology and experience. A domain ontology is represented as a class tree and a class graph based on two types of relationships between classes: subsumption and property-share. Experience is elicited and represented as experience maps using the cognitive mapping technique. The domain ontology is used for extracting semantic information of SA. The extracted SA semantics together with experience are then used for query construction. This chapter will focus on domain ontology and experience.

One key issue about Web service application is how to discover the desired services. Discovery Service is a key technology in service-oriented Web software development. Considering that the current Web services registered on the public UDDI lack semantic description, the researches on service discovery add semantic description about Web services. In actual applications, however, a lot of difficulties are found when using these methods. The goal of domain ontology is to capture the related domain knowledge, to provide common understanding about the domain knowledge, to determine the conceptions commonly recognized in this domain, and to well define these conceptions and the relationship between these conceptions. In this way, errors and failures generated due to misunderstanding the conceptions are reduced, function-based services are much easier to be discovered and combined, and in the meantime, the deficiency in keyword-based search technology of UDDI is made up. In view of the above, this document suggests a method to build domain ontology. Based on the domain ontology, this method presents the retrieve process of service components and implements a domain-ontology-based service search engine prototype system. This method has many benefits: refine and expand users' initial query, support fuzzy query, and improve recall and precision. Especially, with the Internet becoming a reusable software resource library, component query is sure to be supported by the search engines.

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.

Information retrieval (IR) from the free text within electronic health records (EHRs) is time consuming and complex. We hypothesize that natural language processing (NLP)-enhanced search functionality for EHRs can make clinical workflows more efficient and reduce cognitive load for clinicians. This study aimed to evaluate the efficacy of 3 levels of search functionality (no search, string search, and NLP-enhanced search) in supporting IR for clinical users from the free text of EHR documents in a simulated clinical environment. A clinical environment was simulated by uploading 3 sets of patient notes into an EHR research software application and presenting these alongside 3 corresponding IR tasks. Tasks contained a mixture of multiple-choice and free-text questions. A prospective crossover study design was used, for which 3 groups of evaluators were recruited, which comprised doctors (n=19) and medical students (n=16). Evaluators performed the 3 tasks using each of the search functionalities in an order in accordance with their randomly assigned group. The speed and accuracy of task completion were measured and analyzed, and user perceptions of NLP-enhanced search were reviewed in a feedback survey. NLP-enhanced search facilitated more accurate task completion than both string search (5.14%; P=.02) and no search (5.13%; P=.08). NLP-enhanced search and string search facilitated similar task speeds, both showing an increase in speed compared to the no search function, by 11.5% (P=.008) and 16.0% (P=.007) respectively. Overall, 93% of evaluators agreed that NLP-enhanced search would make clinical workflows more efficient than string search, with qualitative feedback reporting that NLP-enhanced search reduced cognitive load. To the best of our knowledge, this study is the largest evaluation to date of different search functionalities for supporting target clinical users in realistic clinical workflows, with a 3-way prospective crossover study design. NLP-enhanced search improved both accuracy and speed of clinical EHR IR tasks compared to browsing clinical notes without search. NLP-enhanced search improved accuracy and reduced the number of searches required for clinical EHR IR tasks compared to direct search term matching.

Up to now, there are few detailed proposals for the cooperative (and distributed) construction of ontologies (cf. [2]). The problem of how to establish a consensus and a shared conceptualization, especially when dealing with contradictory knowledge and conflicting interests has hardly been dealt with. We propose and evaluate a three-phased knowledge mediation procedure which is especially conceived to integrate different perspectives and information needs into one consensual ontology.