• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.74-82
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• 2022

The purpose of this paper is to provide a multi-modular account of double accusative constructions in Korean in the framework of Autolexical Grammar. The grammar views syntactic, semantic, and morphological structures of sentences as modules which are generated simultaneously and independently. Unlike syntactocentric theories, this paper analyzes semantic characteristics of double accusatives through function-argument (F/A) structure along with roles structure (RS) and information structure (IS). In F/A structure of double accusatives, the first accusative becomes an argument of a predicate, unlike the possessive, which is an argument of a relational noun. Furthermore, the first accusative of double accusatives takes the role of patient in RS, which allows it to become the subject of a passive sentence. On the other hand, the second accusative, which is originally the possessee, becomes a focal area in IS. Therefore, the purpose of double accusatives is twofold: one is to turn the possessor into an independent argument of a predicate which takes patient role, and the other is to turn the possessee into a focus. Such semantic characteristics of double accusatives can be expressed by means of multi-dimensional structures of F/A structure, RS, and IS of Autolexical Grammar, which allows an integrated account of the phenomenon.

• Korean Journal of Cognitive Science
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• v.11 no.1
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• pp.25-32
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• 2000

Korean passive verbs are derived from their corresponding active verbs by suffixation or by adding endings and auxiliaries to their stems. Therefore. we assume p passive verbs share some lexical informations with their active counterparts. This paper extending the Generative Lexicon theory of Pustejovsky (995). aims to characterize the argument realization patterns of Korean passive verbs focusing on the case alternation a and to propose their lexical semantic structures which account for the syntactic behavior.

• Asia pacific journal of information systems
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• v.18 no.1
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• pp.79-96
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• 2008

One of the roles of the Semantic Web services is to execute dynamic intra-organizational services including the integration and interoperation of business processes. Since different organizations design their processes differently, the retrieval of similar semantic business processes is necessary in order to support inter-organizational collaborations. Most approaches for finding services that have certain features and support certain business processes have relied on some type of logical reasoning and exact matching. This paper presents our approach of using imprecise matching for expanding results from an exact matching engine to query the OWL(Web Ontology Language) MIT Process Handbook. MIT Process Handbook is an electronic repository of best-practice business processes. The Handbook is intended to help people: (1) redesigning organizational processes, (2) inventing new processes, and (3) sharing ideas about organizational practices. In order to use the MIT Process Handbook for process retrieval experiments, we had to export it into an OWL-based format. We model the Process Handbook meta-model in OWL and export the processes in the Handbook as instances of the meta-model. Next, we need to find a sizable number of queries and their corresponding correct answers in the Process Handbook. Many previous studies devised artificial dataset composed of randomly generated numbers without real meaning and used subjective ratings for correct answers and similarity values between processes. To generate a semantic-preserving test data set, we create 20 variants for each target process that are syntactically different but semantically equivalent using mutation operators. These variants represent the correct answers of the target process. We devise diverse similarity algorithms based on values of process attributes and structures of business processes. We use simple similarity algorithms for text retrieval such as TF-IDF and Levenshtein edit distance to devise our approaches, and utilize tree edit distance measure because semantic processes are appeared to have a graph structure. Also, we design similarity algorithms considering similarity of process structure such as part process, goal, and exception. Since we can identify relationships between semantic process and its subcomponents, this information can be utilized for calculating similarities between processes. Dice's coefficient and Jaccard similarity measures are utilized to calculate portion of overlaps between processes in diverse ways. We perform retrieval experiments to compare the performance of the devised similarity algorithms. We measure the retrieval performance in terms of precision, recall and F measure? the harmonic mean of precision and recall. The tree edit distance shows the poorest performance in terms of all measures. TF-IDF and the method incorporating TF-IDF measure and Levenshtein edit distance show better performances than other devised methods. These two measures are focused on similarity between name and descriptions of process. In addition, we calculate rank correlation coefficient, Kendall's tau b, between the number of process mutations and ranking of similarity values among the mutation sets. In this experiment, similarity measures based on process structure, such as Dice's, Jaccard, and derivatives of these measures, show greater coefficient than measures based on values of process attributes. However, the Lev-TFIDF-JaccardAll measure considering process structure and attributes' values together shows reasonably better performances in these two experiments. For retrieving semantic process, we can think that it's better to consider diverse aspects of process similarity such as process structure and values of process attributes. We generate semantic process data and its dataset for retrieval experiment from MIT Process Handbook repository. We suggest imprecise query algorithms that expand retrieval results from exact matching engine such as SPARQL, and compare the retrieval performances of the similarity algorithms. For the limitations and future work, we need to perform experiments with other dataset from other domain. And, since there are many similarity values from diverse measures, we may find better ways to identify relevant processes by applying these values simultaneously.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.953-957
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• 2009

This paper proposes a keyword-based query translation system for the semantic web. With the relationship between keywords and ontology structure information, the system converts keyword based queries into queries written by formal query language which is appropriate for the semantic web. As a result, casual web users could not only express queries easily but also obtain the better result.

• Journal of the Korean Society of Clothing and Textiles
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• v.28 no.11
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• pp.1458-1469
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• 2004

In this study, color names focusing on the purple series, appearing in the clothing of the Joseon Dynasty, were classified systematically, and the conceptual meaning of each name were investigated through various methods. The results are as follows; First, the color names of purple-series were classified systematically. According to the integration scheme of morphemes, color names could be divided into two categories; single names and composite names. Color names could also be classified into universal and limited names according to the areal distribution of literatures. Secondly, the conceptual meaning of the color names of purple-series were considered. The conceptual meaning could be divided into two categories; one 'etymological and dyeing methodological meaning', the other 'color systematic meaning' By studying the dictionary definition of color names, comparing and analyzing the material composition of colors, the etymological and dyeing methodological meaning could be grasped. Furthermore, the color systematic meaning of each name could be grasped through the cluster analysis of L*, a*, b* values measured from the relics of clothing. Thirdly, the conceptual semantic structure were established on the basis of conceptual meanings of purple-series color names. The conceptual semantic structure of purple-series color names is forming discrete structure with the dyeing method and material of dyes as their semantic components.

• Journal of Korean Elementary Science Education
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• v.37 no.1
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• pp.39-53
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• 2018

The purpose of this study was to investigate the conceptual structure of subjects related to matter having pre-service elementary school teachers by applying semantic network analysis (SNA). The analyzed concepts in the subjects of matter were 6 words such as 'atom', 'molecule', 'ion', 'electron', 'matter' and 'particle'. The results of SNA of the concepts are as follows : 1. In the semantic network of 'atom', words having a high betweenness centrality were linked with the words based on both the scientific context and the everyday context. 2. The network of 'molecule' was analyzed to be more organized than the network of the 'atom'. 3. In the network of 'ion', the group of words of the scientific context was distinguished from the group of words of the everyday context. 4. The network of 'electron' was analyzed to be more oriented on electricity and magnetism in the field of physics. 5. In the network of 'matter', the words related to compounds were linked with knowledge of history of science. 6. The network of 'particle' was not structured with words based on particulate nature of matter.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.271-277
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• 2013

Typical semantic search query for Semantic search promises to provide more accurate result than present-day keyword matching-based search by using the knowledge base represented logically. Existing keyword-based retrieval system is Preference for the semantic interpretation of a user's query is not the meaning of the user keywords of interconnect, you can not search. In this paper, we propose a method that can provide accurate results to meet the user's search intent to user preference based evaluation by ranking search. The proposed scheme is Integrated ontology-based knowledge base built on the formal structure of the semantic interpretation process based on ontology knowledge base system.

• Journal of information and communication convergence engineering
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• v.11 no.3
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• pp.179-184
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• 2013

Traditional clustering methods are usually based on the bag-of-words (BOW) model. A disadvantage of the BOW model is that it ignores the semantic relationship among terms in the data set. To resolve this problem, ontology or matrix factorization approaches are usually used. However, a major problem of the ontology approach is that it is usually difficult to find a comprehensive ontology that can cover all the concepts mentioned in a collection. This paper proposes a new document clustering method using semantic features and fuzzy relations for solving the problems of ontology and matrix factorization approaches. The proposed method can improve the quality of document clustering because the clustered documents use fuzzy relation values between semantic features and terms to distinguish clearly among dissimilar documents in clusters. The selected cluster label terms can represent the inherent structure of a document set better by using semantic features based on non-negative matrix factorization, which is used in document clustering. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• ETRI Journal
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• v.45 no.5
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• pp.822-835
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• 2023

Recent semantic segmentation frameworks usually combine low-level and high-level context information to achieve improved performance. In addition, postlevel context information is also considered. In this study, we present a Context ReFinement Network (CRFNet) and its training method to improve the semantic predictions of segmentation models of the encoder-decoder structure. Our study is based on postprocessing, which directly considers the relationship between spatially neighboring pixels of a label map, such as Markov and conditional random fields. CRFNet comprises two modules: a refiner and a combiner that, respectively, refine the context information from the output features of the conventional semantic segmentation network model and combine the refined features with the intermediate features from the decoding process of the segmentation model to produce the final output. To train CRFNet to refine the semantic predictions more accurately, we proposed a sequential training scheme. Using various backbone networks (ENet, ERFNet, and HyperSeg), we extensively evaluated our model on three large-scale, real-world datasets to demonstrate the effectiveness of our approach.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.533-540
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• 2006

The semantic web environment promise semantic search of heterogeneous data from distributed web page. Semantic search would resuit in an overwhelming number of results for users is increased, therefore elevating the need for appropriate personalized ranking schemes. Culture Finder helps semantic web agents obtain personalized culture information. It extracts meta data for each web page(culture news, culture performance, culture exhibition), perform semantic search and compute result ranking point to base user profile. In order to work efficient, Culture Finder uses five major technique: Machine learning technique for generating user profile from user search behavior and meta data repository, an efficient semantic search system for semantic web agent, query analysis for representing query and query result, personalized ranking method to provide suitable search result to user, upper ontology for generating meta data. In this paper, we also present the structure used in the Culture Finder to support personalized search service.