Hong-Gee Kim

Exploiting noun phrases and semantic relationships for text document clustering

Text document clustering plays an important role in providing better document retrieval, document browsing, and text mining. Traditionally, clustering techniques do not consider the semantic relationships between words, such as synonymy and hypernymy. To exploit semantic relationships, ontologies such as WordNet have been used to improve clustering results. However, WordNet-based clustering methods mostly rely on single-term analysis of text; they do not perform any phrase-based analysis. In addition, these methods utilize synonymy to identify concepts and only explore hypernymy to calculate concept frequencies, without considering other semantic relationships such as hyponymy. To address these issues, we combine detection of noun phrases with the use of WordNet as background knowledge to explore better ways of representing documents semantically for clustering. First, based on noun phrases as well as single-term analysis, we exploit different document representation methods to analyze the effectiveness of hypernymy, hyponymy, holonymy, and meronymy. Second, we choose the most effective method and compare it with the WordNet-based clustering method proposed by others. The experimental results show the effectiveness of semantic relationships for clustering are (from highest to lowest): hypernymy, hyponymy, meronymy, and holonymy. Moreover, we found that noun phrase analysis improves the WordNet-based clustering method.

Nonlinear Model Predictive Formation Flight

This correspondence paper presents the validation of a formation flight control technique with obstacle avoidance capability based on nonlinear model predictive algorithms. Control architectures for multi-agent systems employed in this correspondence paper can be categorized as centralized, sequential decentralized, and fully decentralized methods. Centralized methods generally have better performance than decentralized methods. However, it is well known that the performance of the centralized methods for formation flight degrades when there exists communication failure among the vehicles, and they require more computation time than the decentralized method. This correspondence paper evaluates the control performance and the computation time reduction of the sequential decentralized and fully decentralized methods in comparison with the centralized method and shows that the fully decentralized method can be made effective against short term communication failure. The control inputs for formation flight are computed by nonlinear model predictive control (NMPC). The control input saturation and state constraints are incorporated as inequality constraints using Karush Kuhn Tucker conditions in the NMPC framework, and the collision avoidance can be considered in real time. The proposed schemes are validated by numerical simulations, which include the process and measurement noise for more realistic situations.

An Ontology-Based Bayesian Network Approach for Representing Uncertainty in Clinical Practice Guidelines

Clinical Practice Guidelines (CPGs) play an important role in improving quality of care and patient outcomes. Although several machine-readable representations of practice guidelines have been implemented with semantic web technologies, there is no implementation to represent uncertainty in activity graphs in clinical practice guidelines. In this paper, we explore a Bayesian Network(BN) approach for representing the uncertainty in CPGs based on ontologies. Using this representation, we can evaluate the effect of an activity on the whole clinical process, which can help doctors judge the risk of uncertainty for other activities when making a decision. A variable elimination algorithm is applied to implement the BN inference and a validation of an aspirin therapy scenario for diabetic patients is proposed.

Social semantic cloud of tag: semantic model for social tagging

Tagging has proven to be a successful and efficient way for creating metadata through a human collective intelligence. It can be considered not only an application of individuals for expressing ones interests, but also as a starting point for leveraging social connections through collaborative user participations. A number of users have contributed to tag resources in web sites such as Del.icio.us, Flickr etc. However, there is no uniform structure to describe tags and users activities. This makes difficult to share and represent tag data among people. The SCOT (Social Semantic Cloud of Tags) ontology is aimed to represent the structure and semantics of a set of tags and promotes their global sharing. The paper introduce the SCOT ontology and methods of its representation.

Application of support vector machine for prediction of medication adherence in heart failure patients.

Objectives Heart failure (HF) is a progressive syndrome that marks the end-stage of heart diseases, and it has a high mortality rate and significant cost burden. In particular, non-adherence of medication in HF patients may result in serious consequences such as hospital readmission and death. This study aims to identify predictors of medication adherence in HF patients. In this work, we applied a Support Vector Machine (SVM), a machine-learning method useful for data classification. Methods Data about medication adherence were collected from patients at a university hospital through self-reported questionnaire. The data included 11 variables of 76 patients with HF. Mathematical simulations were conducted in order to develop a SVM model for the identification of variables that would best predict medication adherence. To evaluate the robustness of the estimates made with the SVM models, leave-one-out cross-validation (LOOCV) was conducted on the data set. Results The two models that best classified medication adherence in the HF patients were: one with five predictors (gender, daily frequency of medication, medication knowledge, New York Heart Association [NYHA] functional class, spouse) and the other with seven predictors (age, education, monthly income, ejection fraction, Mini-Mental Status Examination-Korean [MMSE-K], medication knowledge, NYHA functional class). The highest detection accuracy was 77.63%. Conclusions SVM modeling is a promising classification approach for predicting medication adherence in HF patients. This predictive model helps stratify the patients so that evidence-based decisions can be made and patients managed appropriately. Further, this approach should be further explored in other complex diseases using other common variables.

Simple Algorithms for Representing Tag Frequencies in the SCOT Exporter

In this paper we describe the SCOT Exporter and its algorithms to create instance data based on the SCOT (Social Semantic Cloud of Tags) ontology for sharing and reusing tag data. The algorithms use tag frequencies and co-occurrence relations to represent statistical information via the SCOT ontology. We give an overview of the Exporter and the algorithms, and then discuss some experimental results.

Predictors of Medication Adherence in Elderly Patients with Chronic Diseases Using Support Vector Machine Models

Objectives The aim of this study was to establish a prediction model of medication adherence in elderly patients with chronic diseases and to identify variables showing the highest classification accuracy of medication adherence in elderly patients with chronic diseases using support vector machine (SVM) and conventional statistical methods, such as logistic regression (LR). Methods We included 293 chronic disease patients older than 65 years treated at one tertiary hospital. For the medication adherence, Moriskys self-report was used. Data were collected through face-to-face interviews. The mean age of the patients was 73.8 years. The classification process was performed with LR (SPSS ver. 20.0) and SVM (MATLAB ver. 7.12) method. Results Taking into account 16 variables as predictors, the result of applying LR and SVM classification accuracy was 71.1% and 97.3%, respectively. We listed the top nine variables selected by SVM, and the accuracy using a single variable, self-efficacy, was 72.4%. The results suggest that self-efficacy is a key factor to medication adherence among a Korean elderly population both in LR and SVM. Conclusions Medication non-adherence was strongly associated with self-efficacy. Also, modifiable factors such as depression, health literacy, and medication knowledge associated with medication non-adherence were identified. Since SVM builds an optimal classifier to minimize empirical classification errors in discriminating between patient samples, it could achieve a higher accuracy with the smaller number of variables than the number of variables used in LR. Further applications of our approach in areas of complex diseases, treatment will provide uncharted potentials to researchers in the domains.

Exploiting social bookmarking services to build clustered user interest profile for personalized search

Search engine users tend to write short queries, generally comprising of two or three query words. As these queries are often ambiguous or incomplete, search engines tend to return results whose rankings reflect a community of intent. Moreover, search engines are designed to satisfy the needs of the general populace, not those of a specific searcher. To address these issues, we propose two methods that use Singular Value Decomposition (SVD) to build a Clustered User Interest Profile (CUIP), for each user, from the tags annotated by a community of users to web resources of interest. A CUIP consists of clusters of semantically or syntactically related tags, each cluster identifying a topic of the users interest. The matching cluster, to the given users query, aids in disambiguation of user search needs and assists the search engine to generate a set of personalized search results. A series of experiments was executed against two data sets to judge the clustering tendency of the cluster structure CUIP, and to evaluate the quality of personalized search. The experiment results indicate that the CUIP based personalized search outperforms the baseline search and is better than the other approaches that use social bookmarking services for building a user profile and use it for personalized search.

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.

GOClonto: An ontological clustering approach for conceptualizing PubMed abstracts

Concurrent with progress in biomedical sciences, an overwhelming of textual knowledge is accumulating in the biomedical literature. PubMed is the most comprehensive database collecting and managing biomedical literature. To help researchers easily understand collections of PubMed abstracts, numerous clustering methods have been proposed to group similar abstracts based on their shared features. However, most of these methods do not explore the semantic relationships among groupings of documents, which could help better illuminate the groupings of PubMed abstracts. To address this issue, we proposed an ontological clustering method called GOClonto for conceptualizing PubMed abstracts. GOClonto uses latent semantic analysis (LSA) and gene ontology (GO) to identify key gene-related concepts and their relationships as well as allocate PubMed abstracts based on these key gene-related concepts. Based on two PubMed abstract collections, the experimental results show that GOClonto is able to identify key gene-related concepts and outperforms the STC (suffix tree clustering) algorithm, the Lingo algorithm, the Fuzzy Ants algorithm, and the clustering based TRS (tolerance rough set) algorithm. Moreover, the two ontologies generated by GOClonto show significant informative conceptual structures.