Senator Jeong

A Social Network Analysis of Research Topics in Korean Nursing Science

PURPOSE This study was done to explore the knowledge structure of Korean Nursing Science. METHODS The main variables were key words from the research papers that were presented in the Journal of Korean Academy of Nursing and journals of the seven branches of the Korean Academy of Nursing. English titles and abstracts of the papers (n=5,936) published from 1995 through 2009 were included. Noun phrases were extracted from the corpora using an in-house program (BiKE Text Analyzer), and their co-occurrence networks were generated via a cosine similarity measure, and then the networks were analyzed and visualized using Pajek, a Social Network Analysis program. RESULTS With the hub and authority measures, the most important research topics in Korean Nursing Science were identified. Newly emerging topics by three-year period units were observed as research trends. CONCLUSION This study provides a systematic overview on the knowledge structure of Korean Nursing Science. The Social Network Analysis for this study will be useful for identifying the knowledge structure in Nursing Science.

SEDE: An ontology for scholarly event description

Scholarly events are important scientific communication channels. Our research goal is to satisfy scientists’ basic information needs by collecting, archiving and providing access to scholarly event information. Furthermore, we aim to satisfy users’ in-depth information needs by excavating scholarly meaningful information through reasoning about knowledge. A prerequisite to accomplishing this end is to define a description base for scholarly events to enable software agents to crawl and extract scholarly event data, and to facilitate unified access to this data. The collected data may then be mined for non-obvious knowledge. We present the design and implementation of an ontology for scholarly event description (SEDE) to achieve the research goal, and the application use case scenarios in scholarly event information space. The scenarios presented highlight the characteristics of the SEDE ontology.

Knowledge Structure of Korean Medical Informatics: A Social Network Analysis of Articles in Journal and Proceedings

Objectives This study aimed at exploring the knowledge structure of Korean medical informatics. Methods We utilized the keywords, as the main variables, of the research papers that were presented in the journal and symposia of the Korean Society of Medical Informatics, and we used, as cases, the English titles and abstracts of the papers (n = 915) published from 1995 through 2008. N-grams (bigram to 5-gram) were extracted from the corpora using the BiKE Text Analyzer, and their cooccurrence networks were generated via a cosine correlation coefficient, and then the networks were analyzed and visualized using Pajek. Results With the hub and authority measures, the most important research topics in Korean medical informatics were identified. Newly emerging topics by three-year period units were observed as research trends. Conclusions This study provides a systematic overview on the knowledge structure of Korean medical informatics.

OntoPipeliner: An ontology-based automatic semantic service pipeline generator

The Web is a distributed environment rich with Web services going through continual metamorphosis; thus, sustaining semantic stability of service composition has become a major challenge. Automatic service composition - enabled both by the use of ontologies that describe service domains and by user-specified constraints bound to the ontologies - provides us candidate service pipelines at composition design time. The ontology-based languages for semantically describing web services, such as OWL-S, have been widely used. Though rich and comprehensive in their expressiveness, the use of these languages still leaves much of composition process manual. In this work, we present an ontology-based semantic web service composition system called OntoPipeliner. It employs a novel way of utilizing characteristics of Web services that reflect the classes and properties of domain ontologies and provides the ontology-guided constraints for automatic composition of services, in order to guide the user toward the best pipeline that meets the user requirements.

Clinical Data Element Ontology for Unified Indexing and Retrieval of Data Elements across Multiple Metadata Registries

Objectives Classification of data elements (DEs), which is used in clinical documents is challenging, even in across ISO/IEC 11179 compliant clinical metadata registries (MDRs) due to no existence of reliable standard for identifying DEs. We suggest the Clinical Data Element Ontology (CDEO) for unified indexing and retrieval of DEs across MDRs. Methods The CDEO was developed through harmonization of existing clinical document models and empirical analysis of MDRs. For specific classification as using data element concept (DEC), The Simple Knowledge Organization System was chosen to represent and organize the DECs. Six basic requirements also were set that the CDEO must meet, including indexing target to be a DEC, organizing DECs using their semantic relationships. For evaluation of the CDEO, three indexers mapped 400 DECs to more than 1 CDEO term in order to determine whether the CDEO produces a consistent index to a given DEC. The level of agreement among the indexers was determined by calculating the intraclass correlation coefficient (ICC). Results We developed CDEO with 578 concepts. Through two application use-case scenarios, usability of the CDEO is evaluated and it fully met all of the considered requirements. The ICC among the three indexers was estimated to be 0.59 (95% confidence interval, 0.52-0.66). Conclusions The CDEO organizes DECs originating from different MDRs into a single unified conceptual structure. It enables highly selective search and retrieval of relevant DEs from multiple MDRs for clinical documentation and clinical research data aggregation.

Standardization of DRM Technologies in MPEG-21

MPEG-21 is an open standard framework for creation, delivery and consumption of digital content in interoperable and rights-managed and protected way. Focusing on DRM technologies, this paper covers with concept and ongoing activities of MPEG-21`s parts - Digital Item Declaration which is the base unit of trade and delivery, Digital Item Identification, Intellectual Property Management & Protection, Rights Data Dictionary, Rights Expression Language, Persistent Association Technology, Event Reporting, and so on.

Structuralizing biomedical abstracts with discriminative linguistic features

OBJECTIVE Nearly 75% of the abstracts in MEDLINE papers present in an unstructured format. This study aims to automate the reformatting of unstructured abstracts into the Introduction, Methods, Results, and Discussion (IMRAD) format. The quality of this reformatting relies on the features used in sentence classification. Therefore, we explored the most effective linguistic features in MEDLINE papers. METHODS We constructed a feature set consisting of bag of words, linguistic features, grammatical features, and structural features. In order to evaluate the effectiveness, which is the capability of the sentence classification with the features, three datasets from PubMed Central Open Access Subset were selected and constructed: (1) structured abstract (SA) for training, (2) unstructured RCT abstract (UA-1) and (3) unstructured general abstract (UA-2). F-score and accuracy were used to measure the effectiveness on IMRAD section level and the overall classification. RESULTS Adding linguistic features improves the classification of the abstract sentence from 1.2% to 35.8% in terms of accuracy in three abstract datasets. The highest accuracies achieved were 91.7% in SA, 86.3% in UA-1, and 77.9% in UA-2. Linguistic features (dimensions=15) had fewer dimensions than bag-of-words (dimensions= 1541). All representative linguistic features (n-gram and verb phrase, and noun phrase) for each section are identified in our system (available at http://abstract.bike.re.kr). CONCLUSION Linguistic features can be used to effectively classify sentence with low computation burden in MEDLINE abstract.

Design of semantically interoperable adverse event reporting framework

Patient safety is one of the most significant issues not only to medical providers but also to the general public Despite the widespread recognition of the adverse event reporting for patients safety, there is no widely accepted or standardized way to request and report adverse event information We designed the semantically interoperable Adverse Event Reporting framework It consists of two components: the Adverse Event Ontology to describe adverse event in semantically interoperable way and the Adverse Event Reporting Schema (AERS) to envelope and deliver the content of adverse event report request and report The Adverse Event Ontology was built upon existing adverse event taxonomies The AERS was designed for common adverse event messaging interface in the form of XML Schema The adverse event reporting framework is expected to allow semantic interoperability in sharing and exchange of patient safety information within and among various healthcare information systems.