We describe ongoing work in semi-automatic annotating corpus, with the goal to answer why-question in question answering system and give a construction of the coherent tree for text summarization. In this paper we present annotation schemas for identifying the discourse relations that hold between the parts of text as well as the particular textual of span that are related via the discourse relation. Furthermore, we address several tasks in building the annotated corpus in discourse level, namely creating annotated guidelines, ensuring annotation accuracy and evaluating.

An ontological knowledge management system requires dynamic and encapsulating operation in order to share knowledge among communities. The key to success of knowledge sharing in the field of agriculture is using and sharing agreed terminologies such as ontological knowledge especially in multiple languages. This paper proposes a workbench with three authoring tools for collaborative multilingual ontological knowledge construction and maintenance, in order to add value and support communities in the field of food and agriculture. The framework consists of the multilingual ontological knowledge construction and maintenance workbench platform, which composes of ontological knowledge management and user management, and three ontological knowledge authoring tools. The authoring tools used are two ontology extraction tools, ATOM and KULEX, and one ontology integration tool.

Knowledge has been crucial for the country’s development and business intelligence, where valuable knowledge is distributed over several websites with heterogeneous formats. Moreover, finding the needed information is a complex task since there has been lack of semantic relation and organization. Even if it has been found, an overload may occur because there is no content digestion. This paper focuses on ontology-driven knowledge extraction with natural language processing techniques and a framework of usercentric design for accessing the required information based on their demands. These demands can be expressed in the form of Knowwhat, Know-why, Know-where, Know-when, Know-how, and Know-who for a question answering system.