Hanmin Jung

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Hanmin Jung is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Acronym-expansion recognition based on knowledge map system Acronym-Expansion recognition
DBpedia
Instance mapping
Knowledge map
Linked Open Data
URI resolution
Wikipedia
Information (Japan) English 2013 In this paper, we present a method for instance mapping and URI resolving to merge two heterogeneous resources and construct a new semantic network from the viewpoint of acronym-expansion. Acronym-expansion information extracted from two unstructured large datasets can be remapped by using linkage information between instances and measuring string similarity. Finally we evaluate the acronym discrimination performance based on the proposed knowledge map system. The result showed that noun phrase based feature selection method gained 89.6% micro averaged precision, which outperformed single noun based one by 20.1%. We found a possibility of interoperability between heterogeneous databases through the experiment of acronym-expansion recognition. 0 0
Extracting protein terminologies in literatures Keyword refinement
Protein terminologies
Wikipedia terminologis
Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013 English 2013 Recently, key terminologies in literatures play an important role in analyzing and predicting research trends. Extracting those terminologies therefore used in the papers of researchers' has become the most major issue in a variety of fields. To extract those terminologies, dictionary-based approach that contains terminologies has been applied. Wikipedia also can be considered as a dictionary since Wikipedia has abundant terminologies and power of the collective intelligence. It means that the terminologies are continuously modified and extended every day. Thus it could be an answer set to compare with the terminologies in literatures. However, it hardly extracts terminologies that are newly defined and coined by researchers. In order to solve this issue, we propose a method to derive a set of terminology candidates by comparing terminologies in literatures and Wikipedia. The candidate set extracted from the method showed an accuracy of about 64.33%, which is a good result as an initial study. 0 0
Horizontal search method for Wikipedia category grouping Category grouping
Horizontal search
Similarity measure
Wikipedia category
Proceedings - 2012 IEEE Int. Conf. on Green Computing and Communications, GreenCom 2012, Conf. on Internet of Things, iThings 2012 and Conf. on Cyber, Physical and Social Computing, CPSCom 2012 English 2012 Category hierarchies, which show the basic relationship between concepts, are utilized as fundamental clues for semantic information processing in diverse research fields. These research works have employed Wikipedia due to its high coverage of real-world concepts and data reliability. Wikipedia also constructs a category hierarchy, and defines various categories according to the common characteristics of a concept. However, some limitations have been uncovered in the use of a vertical search (especially top-down) to form a set of domain categories. In order to overcome these limitations, this paper proposes a horizontal search method, and uses Wikipedia components to measure the similarity between categories. In an experimental evaluation, we confirm that our method shows a wide coverage and high precision for similar (domain) category grouping. 0 0
Accessing information sources using ontologies Education
Ontology
Wikipedia
International Journal of Computers, Communications and Control English 2011 In this paper, we present a system that helps users access various types of information sources using ontologies. An ontology consists of a set of concepts and their relationships in a domain of interests. The system analyzes an ontology provided by a user so that the user can search and browse Wikipedia [1], DBpedia [4], PubMed [5], and the Web by utilizing the information in the ontology. In particular, terms defined in the ontology are mapped to Wikipedia pages and the navigation history of a user is saved so that it can serve as a personalized ontology. In addition, users can create and edit ontologies using the proposed system. We show that the proposed system can be used in an educational environment. 0 0
Measuring Similarities between Technical Terms Based on Wikipedia Similarity Measure
Technical Terms
Wikipedia InterLink
Wikipedia Category
ITHINGSCPSCOM English 2011 0 0
Measuring similarities between technical terms based on Wikipedia Similarity measure
Technical terms
Wikipedia category
Wikipedia internal link
Proceedings - 2011 IEEE International Conferences on Internet of Things and Cyber, Physical and Social Computing, iThings/CPSCom 2011 English 2011 Measuring similarities between terms is useful for semantic information processing such as query expansion and WSD (Word Sense Disambiguation). This study aims at identifying technologies closely related to emerging technologies. Thus, we propose a hybrid method using both category and internal link information in Wikipedia, which is the largest database that everyone can share and edit its contents. Comparative experimental results with a state-of-theart WLM (Wikipedia Link-based Measure) show that this proposed method works better than each single method. 0 0
Knowledge management using Wikipedia CEUR Workshop Proceedings English 2009 In this paper, we present an ontology-based system that helps users manage knowledge using Wikipedia. The system analyzes ontologies and uses the structural information about the ontologies to re-structure contents of Wikipedia for better browsing. Using the system, users can acquire knowledge easily from Wikipedia. We show how the system can be used for life science applications. 0 0