Legacy information search systems have limitation that it does not consider semantic information but just lexical information such as keywords. A semantic web is expected to solve such limitation of present systems. In constructing semantic web, an ontology is believed to be a must. However, the ontology construction is very difficult. It requires great human efforts, since the creation of individuals is a time consuming task. Thus, there is a potential need for automatic or semiautomatic ontology population system, which greatly alleviates the human efforts. This paper proposes a method for an ontology population, in which the population is processed by computing the overlap between instances and concepts. This method is very simple but shows high performance.

Machine translation systems have various problems although they have been developed continuously. Especially, in Korean-English translation system, zero pronoun problem is an important problem, since omitted subject or object Korean are must be restored in English. In order to solve this problem, various methods have been proposed. In this paper, we focus on the gender determination problem in Korean names as a first-step for solving a zero pronoun problem in Korean. Since this problem can be viewed as a binary classification problem, we adopt support vector machines which are well-known for solving binary classification. The bag-of-words model is used to represent a name with context as a vector and information entropy of words is adopted for selecting features. An evaluation of the proposed method shows about 86% of accuracy. This method achieves higher accuracy than baseline which determines the gender of a name by its majority and additionally resolves the limitation of memory based and statistical method which use only names.

Document understanding and information extraction include different tasks to understand a document and extract valuable information automatically. Recently, there has been a rising demand for developing document understanding among different domains, including business, law, and medicine, to boost the efficiency of work that is associated with a large number of documents. This workshop aims to bring together researchers and industry developers in the field of document intelligence and understanding diverse document types to boost automatic document processing and understanding techniques. We also release a data challenge on the recently introduced document-level VQA dataset, PDFVQA. The PDFVQA challenge examines the model's structural and contextual understandings on the natural full document level of multiple consecutive document pages by including questions with a sequence of answers extracted from multi-pages of the full document. This task helps to boost the document understanding step from the single-page level to the full document level understanding.