20th International CODATA Conference
Session: Integrating Heterogeneous and Very Large Datasets for Global Water Cycle Studies: a Case Study for GEOSS

Developing Ontological Information to Integrate Global Observation Data

Ryosuke Shibasaki (shiba@csis.u-tokyo.ac.jp), Masahiko Nagai (nagaim@iis.u-tokyo.ac.jp), Shigenobu Tachizuka (tachis@csis.u-tokyo.ac.jp), Takashi Oguchi (oguchi@csis.u-tokyo.ac.jp), Hiromichi Fukui (hfukui@sfc.keio.ac.jp)

The University of Tokyo, Center for Spatial Information Science, Japan

A Global Earth Observation System of Systems, GEOSS, which was reached at the Third Observation Summit held in Brussels, proposes an international cooperative attempt to gather accessible hardware and software for all of the nations. GEOSS has begun to make efforts to supply data and information at no cost by collecting data, enhancing data distribution, and providing models for the global environment.

The global environment is lying on trans-disciplinary fields, including hydrology, geography, agriculture, ecology, biology, and so on. Under the trans-disciplinary condition, not only standardization for data structure and geographic information but also communizing particular terminology and classification schema are serious hindrances for data sharing and integration of distributive data. Especially in earth observation data, distributive system to utilize flexibly and easily for various application needs is expected.

We propose to develop the ontology registry system to collect, manage, and compare ontological information such as data dictionaries, classification schemas, terminologies, and thesauruses for the global observation data. Data sharing and data service such as support of metadata deign, structuring of data contents, support of text mining is applied for better use of data to the ontology registry system. We propose semantic network dictionary as a trans-disciplinary dictionary including civil engendering, agriculture, classification schema, satellite observation data, and so on. We endeavor to add dictionaries and data model to the system by digitalizing text based dictionaries, developing “knowledge writing tool” for experts, and extracting semantic relations form authoritative documents with natural language processing technique.

Keywords: ontology, semantic network dictionary