• Journal of Computing Science and Engineering
• /
• 제2권1호
• /
• pp.44-73
• /
• 2008

There is general agreement that the problem of data semantics has to be addressed for XML data to become machine-processable. This problem can be tackled by defining a semantic mapping between an XML schema and an ontology. Unfortunately, creating such mappings is a tedious, time-consuming, and error-prone task. To alleviate this problem, we present a solution that heuristically discovers semantic mappings between XML schemas and ontologies. The solution takes as input an initial set of simple correspondences between element attributes in an XML schema and class attributes in an ontology, and then generates a set of mapping formulas. Once such a mapping is created, it is important and necessary to maintain the consistency of the mapping when the associated XML schema and ontology evolve. In this paper, we first offer a mapping formalism to represent semantic mappings. Second, we present our heuristic mapping discovery algorithm. Third, we show through an empirical study that considerable effort can be saved when discovering complex mappings by using our prototype tool. Finally, we propose a mapping maintenance plan dealing with schema evolution. Our study provides a set of effective solutions for building sustainable semantic integration systems for XML data.

• ETRI Journal
• /
• 제43권4호
• /
• pp.630-639
• /
• 2021

Autonomous driving requires a computerized perception of the environment for safety and machine-learning evaluation. Recognizing semantic information is difficult, as the objective is to instantly recognize and distinguish items in the environment. Training a model with real-time semantic capability and high reliability requires extensive and specialized datasets. However, generalized datasets are unavailable and are typically difficult to construct for specific tasks. Hence, a light detection and ranging semantic dataset suitable for semantic simultaneous localization and mapping and specialized for autonomous driving is proposed. This dataset is provided in a form that can be easily used by users familiar with existing two-dimensional image datasets, and it contains various weather and light conditions collected from a complex and diverse practical setting. An incremental and suggestive annotation routine is proposed to improve annotation efficiency. A model is trained to simultaneously predict segmentation labels and suggest class-representative frames. Experimental results demonstrate that the proposed algorithm yields a more efficient dataset than uniformly sampled datasets.

• Journal of the Korean Chemical Society
• /
• 제66권5호
• /
• pp.376-389
• /
• 2022

The purpose of this study was to investigate the effects of implementing semantic mapping reading strategy in the science class on high school students' science text reading ability. 3^rd grade students of science core high school in a small and medium-sized city participated in this study for a semester. Texts with socio-scientific issues and chemistry subjects were used to implement semantic mapping reading strategy in the science class. To investigate the changes in students' science text reading ability, experimental group students participated in the pre-reading and post-science reading ability tests and the results were analyzed. The results of this study showed that the mean of the science reading ability test score of experimental group was significantly higher than that of the comparison group. We found that drawing a semantic mapping before solving a reading task made it easier for students to find information and infer meaning from text. It can be seen that students also recognize that the semantic mapping is helpful in understanding the text because it is easy to understand the relationship between concepts by visualizing the content of the text, and can connect their background knowledge with the text content.

• Journal of The Korean Association For Science Education
• /
• 제26권3호
• /
• pp.393-405
• /
• 2006

The purpose of this study was to analyze the effects of Semantic Network Program (SNP) instruction on learning achievement and motivation in high school biology classes. For this study, a SNP was designed by applying the recommendations in regard to student attention and satisfaction factors in Keller's ARCS theory. SNP instruction was conducted with an experimental group and a control group, each consisting of 62 high school biology class student. A pretest-posttest control group design was employed. The pre-test was used to analyze the learning achievement test, learning motivation test, and semantic forming test. For 4 weeks the experiment group was instructed using the developed SNP which centered on Keller's attention and satisfaction factors, and the control group was instructed via teacher-centered lectures based on the textbook. It was found that SNP instruction efficiently increased students' biology learning achievement (p<.001). It was also discovered that SNP instruction was effective in increasing Keller's motivation strategies on attention and satisfaction factors (p<.001). In addition, SNP instruction positively affected students' semantic formation (p<.001) and learning content retention (p>.05) in the heredity unit by aiding students in the area of active multimedia learning. An in depth interview with students in the class using SNP instruction showed that material learned via this method in biology had longer retention of problem-solving methods. Consequently, SNP instruction according to motivation strategies may high school biology teachers with meaningful teaching-learning methods strategies for the unit on heredity.

• Journal of Korea Society of Digital Industry and Information Management
• /
• 제17권4호
• /
• pp.103-108
• /
• 2021

Knowledge graph is a technology that improves search results by using semantic information based on various resources. Therefore, due to these advantages, the knowledge graph is being defined as one of the core research technologies to provide AI-based services recently. However, in the case of the knowledge graph, since the form of knowledge collected from various service domains is defined as plain text, it is very important to be able to analyze the text and understand its meaning. Recently, various lexical dictionaries have been proposed together with the knowledge graph, but since most lexical dictionaries are defined in a language other than Korean, there is a problem in that the corresponding language dictionary cannot be used when providing a Korean knowledge service. To solve this problem, this paper proposes an ontology based on the parts of speech of Korean. The proposed ontology uses 9 parts of speech in Korean to enable the interpretation of words and their semantic meaning through a semantic connection between word class and word class. We also studied various scenarios to apply the proposed ontology to vehicle services.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 한국컴퓨터정보학회 2022년도 제66차 하계학술대회논문집 30권2호
• /
• pp.225-228
• /
• 2022

본 논문에서는 의미적 유사도 기반의 장면 분할 방법을 제안한다. 이 방법은 의미적 접근을 통해 기존 연구에서 가졌던 한계를 극복하고 정확한 장면 분할이 가능할 것으로 기대한다. 의미적 유사도 비교를 Class 종류 비교, Class별 객체의 개수 비교, 샷 간의 Histogram비교, 객체의 관심영역(ROI) Histogram비교 총 4가지 규칙으로 정의했고 이때 도출된 4가지 유사도는 전처리를 거쳐 종합 유사도를 계산한다. 또한 의미적 접근을 통해 연속되는 Shot의 유사도를 비교하고 기준값에 따라 Shot을 묶어서 최종적으로 의미적 유사도(Semantic Similarity)에 기반한 장면의 경계(Scene Boundary) 분할 방법을 제시한다.

• The KIPS Transactions:PartB
• /
• 제17B권1호
• /
• pp.85-92
• /
• 2010

A shallow semantic parsing system analyzes the relationship that a syntactic constituent of the sentence has with a predicate. It identifies semantic arguments representing agent, patient, instrument, etc. of the predicate. In this study, we propose a two-phase shallow semantic parsing model which consists of the identification phase and the classification phase. We first find the boundary of semantic arguments from partial syntactic parsing results, and then assign appropriate semantic roles to the identified semantic arguments. By taking the sequential two-phase approach, we can alleviate the unbalanced class distribution problem, and select the features appropriate for each task. Experiments show the relative contribution of each phase on the test data.

• Korean Journal of Remote Sensing
• /
• 제34권4호
• /
• pp.693-702
• /
• 2018

With the development of data analysis methods and data processing capabilities, semantic analysis of floorplans has been actively studied. Therefore, studies for extracting text information from drawings have been conducted for semantic analysis. However, existing research that separates rasterized text from floorplan has the problem of loss of text information, because when graphic and text components overlap, text information cannot be extracted. To solve this problem, this study defines the morphological characteristics of the text in the floorplan, and classifies the class of the corresponding region by applying the class of the SIFT key points through the SVM models. The algorithm developed in this study separated text components with a recall of 94.3% in five sample drawings.

• English Language & Literature Teaching
• /
• 제7권1호
• /
• pp.137-160
• /
• 2001

This paper is to suggest the way to improve EFL reading skill through semantic mapping by leveled group activities. Semantic mapping is a categorical structuring of information in graphic forms or diagrams. It can be used to activate and organize background knowledge on topics in classrooms. For small group activities, the class is divided into higher leveled groups and lower leveled groups of four members based on their grades. The teaching process has three stages: Pre-reading, while-reading, and post-reading. In the pre-reading stage, students discuss what they know about the topic. They map ideas with a brainstorming technique. In the while-reading stage, they read the text about the topic. While they are reading, they could ask some questions they might have and discuss the information in the text and categorize them with semantic mapping. In the post-reading stage, they discuss what they thought of the topic and add some information about the topic with semantic mapping. For the subjects of this study, third grade, middle school students were selected: 41 students for the experimental group and 35 students for the control group. The experimental period covered almost one semester from March to August, 2000. The results were as follows: 1) The students in the experimental group had higher scores in reading comprehension than those in the control group when semantic mapping was used; 2) The use of semantic mapping in reading comprehension was found to be much more effective in the higher leveled group than in the lower leveled group; 3) The results of questionnaires showed that many students became more interested and motivated in English, and semantic mapping helped them to participate positively in reading the English text. Thus, using semantic mapping by leveled group activities can be an effective alternative to traditional teaching methods for teachers who desire to improve reading skill in middle school students' English classes.

• Journal of the Korean Institute of Educational Facilities
• /
• 제20권6호
• /
• pp.21-30
• /
• 2013

This study aimed to identify elementary school teachers' perceptions of various aspects of physical environments of a classroom. For the purpose, a survey questionnaire was administered to 982 classroom teachers(female 82.2%) working at 67 elementary schools(Seoul 55%) located in Seoul and Busan. According to the analyses of the survey data, 50.8% of the respondent teachers suggested 20-25 as an optimum class size. Also, 57% of the teachers were positive about the current size of a classroom($67.5m^2$), but evaluations of a classroom size were divided between the teachers who had more and less than 30 students in class. Furthermore, three factors, labelled as 'uniqueness', 'residential stability' and 'visually pleasing', were extracted from a factor analysis of the Semantic Differential Scale consisting of 24 adjectives evaluating a classroom environment. Teachers from Busan tended to rate higher in the uniqueness domain while female teachers scored higher in the domain of residential stability, respectively, compared to their counterparts. Overall, the teachers perceived their classrooms having no particular characteristics, but rated highly in terms of stability. In addition, it was found out that the teachers' requests for the improvement of a classroom environment were not so much as the increased size of a classroom as a reduced class size as well as educational facilities that are corresponding to a variety of instructional methods. In summary, this study confirmed that elementary school teachers' perceptions of a classroom environment varied according to such factors as sex, teaching career, grade of their concerned class, and class size.