Trong Hai Duong

A Method for Integration of WordNet-Based Ontologies Using Distance Measures

While there is a large body of previous work focused on WordNet-based for finding the semantic similarity of concepts and words, the application of these word oriented methods to ontology integration tasks has not been yet explored. In this paper, we propose a methodology of WordNet-based distance measures, and we apply the meaning of concepts of upper ontologies to an ontology integration process by providing semantic network called OnConceptSNet. It is a semantic network of concepts of ontologies in which relations between concepts derived from upper ontology WordNet. We also describe a methodology for conflict in ontology integration process.

Constructing and mining a semantic-based academic social network

A number of studies have focused on how to constructan academic social network. The relational ties among researchers are either co-authorship or shared keywords that are captured from scientific journals. The problem with such a network is that researchers are limited within their professional social network. In this paper, we propose a novel method for building a social network explicitly based on researchers’ knowledge interests. The researcher’s profile is automatically generated from metadata of scientific publications and homepage. By measuring the similarity between topics of interest, we are able to construct a researcher social network with relational linkages among authors that are produced by matching the their corresponding profiles. A direct loop graph-based social network is proposed. The graph naturally represents such a social network. Interestingly, our results showed that a social network based on profile matching is more representative than network based on publication co-authorship or shared keywords. Researcher mining in the academic social network has been explored via two problems Researcher Ranking and Expert Finding.

A Hybrid Method for Integrating Multiple Ontologies

While there has been a variety of research focusing on ontology integration based on simple techniques (e.g., element- or structure-level techniques), the hybrid approaches combining the simple techniques have not been explored. In this paper we describe a hybrid method to integrate multiple ontologies in several levels, such as the element level, internal structure, and relational structure. A semantic supporting environment (SSE) combining special domains (e.g., WordNet) and text corpus are defined in the proposed approach. An enriched ontology model (EOM) has been proposed to reduce the initial complexity of the process of ontology integration. Subsequently, the semantic network called OnConceptSNet is provided. The relations between the concepts in the OnConceptSNet are derived from the SSE. An enhanced algorithm (EA) has been proposed to enhance OnConceptSNet.

A Collaborative Ontology-Based User Profiles System

The main goal of this research is to investigate the techniques that implicitly build ontology-based user profiles . In particular, automatically building profiles based on users information (blogs, publications, home pages,...) generated from the internet. We proposed a framework to search the user details to build profile automatically. An initial profile is constructed with user interest in hierarchical manner and this profile is learned by assigning user details collected by our search method. Main focused on how quickly we can collect users information and achieve a profile stability, and how effectively improve profiles. Along with the framework we also consider a new method to extract feature from document. A Wordnet and Lexico-syntactic pattern for hyponyms approach is applied to export the important feature of document to represent the profile ontology. The user profile further improved by learning interesting knowledge from similar profiles.

A Method for Integration across Text Corpus and WordNet-Based Ontologies

Most information in the world exists in the format of text, such as news articles and web pages. Different lines of research have been conducted to discover, understand and access knowledge about real-world entities and relations from text. However, the application of these word oriented methods to ontology integration tasks has not been yet explored. In this paper, we apply these word oriented methods to ontology integration tasks in which we analyze a noun phrase (NP) to identify its head noun, which is useful to avoid wrong relations between entities. We also propose a collaborative acquisition algorithm that combines WordNet-based and Text corpus.

A HYBRID METHOD FOR FUZZY ONTOLOGY INTEGRATION

The main contribution of this work consists of combining a heuristic method for propagation of matchable concepts and using consensus techniques for conflict resolution for fuzzy ontology integration. Two central observations behind this approach are as follows: (1) if two concepts across different source ontologies equivalently match each other, then their neighboring concepts will be often matched as well; and (2) conflicts regarding integration of multiple ontologies can be resolved by creating a consensus among the conflict ontological entities. The key idea of the first observation is to start from an aligned pair of concepts (called medoids) to determine so-called potentially common parts to provide additional suggestions for possible matching concepts. This approach is used to obtain pairs of matchable concepts and to avoid pairs of mismatching concepts. On the other hand, the second observation is used to discover a new merged concept from matched concepts by making a consensus among conflict ontological entities. This idea is to determine the best representative as the merged version of the component ones. A combination of both observations for fuzzy ontology integration is a significant contribution of this work. The results of the experiments imply that the proposed approach is effective with regard to both completeness and accuracy.

Solving conflict on collaborative knowledge via social networking using consensus choice

While knowledge exchange among users is rapidly increased in social networking environment, collaborative knowledge in social networking is being become more and more essential for knowledge management systems. In this work, a method for solving conflict on collaborative knowledge via social networking using consensus choice is presented. A knowledge base can be considered as a pair KB = (O, I) where O is an ontology and I is a set of instances of concepts belonging to the ontology O. The main issue presented here is how to organize a collaboration process and to resolve conflicts in collaborative knowledge creation via social networking. In this work, ontology is considered as sharing mechanism for social collaboration. The structure of a collaborative group is distinguished by three types including centralized-, decentralized-, and distributed group. For each group type, we propose a corresponding algorithm for conflict resolution using consensus choice.

Fuzzy Ontology Integration Using Consensus to Solve Conflicts on Concept Level

Nowadays, ontology has been backbone of Semantic web. However, the current ontologies are based on traditional logic such as first-order logic and description logic. The conceptual formalism of the ontologies cannot be fully representative for imprecise and vague information (e.g. ”rainfall is very heavy”) in many application domains. In this paper, a domain fuzzy ontology is defined clearly, and its components such as fuzzy relation, concrete fuzzy concept, and fuzzy domain concept as well as similarity measures between the components are addressed. Fuzzy ontology integration on concept level using consensus method to solve conflicts among the ontologies is proposed. In particular, the postulates for integration are specified and algorithms for reconciling conflicts among fuzzy concepts in ontology integration are proposed.

Personalized Semantic Search Using ODP: A Study Case in Academic Domain

Personalized search utilizes the user context in a form of profile to increase the information retrieval accuracy with user’s interests. Recently, semantic search has greatly attracted researchers’ attention over the traditional keyword-based search because of having capabilities to figure out the meaning of search query, understanding users’ information needs accurately using semantic web technology. In this paper, a ODP (open directory project)-based approach for personalized semantic information search is proposed. A reference ontology is generated by utilizing ODP to model user’s interests and semantic search space. User profile is initially derived by matching between user’s details and the reference ontology to model his/her interests and preference. Semantic search space is constructed by fuzzily classifying documents into the reference ontology. We also present an evaluation of our proposed method which indicates a considerable improvement of search accuracy with the field of application human judgment.

Personalized Facets for Semantic Search Using Linked Open Data with Social Networks

The main aim of this research is to deal with semantic search based on personalized facets in linked open data. User profile is learned from his/her activities and preferences in social networks using tf-idf feature vector model. A faceted graph visualization for result collaborative filtering is proposed. The facets are vertices representing ontological concepts. Other vertices represent instances belonging to the concepts, which are known as facets values. The vertices are highlighted by matching between facets and user profile in order to individually produce search interfaces. The ties between vertices are ontological relations or properties considering as variables/attributes of facets. An algorithm to construct the faceted graph visualization and collaboratively filter search result is also provided. The faceted search method presented here is implemented to demonstrate these ideas.