Earth Science Knowledge Graph - An Automatic Approach to Building Earth Science Knowledge Graph to Improve Data Discovery.
Big Earth observation data have been produced, archived and made available online, but discovering the right data in a manner that precisely and efficiently satisfies user need presents a significant challenge to the Earth Science (ES) community. An emerging trend in information retrieval community is to utilize knowledge graph to assist user fast finding desired information. This is particularly prevalent within the fields of social media and complex multimodal information processing to name but a few.
However, building a domain-specific knowledge graph is labour-intensive and hard to keep up-to-date. We propose an automatic approach to building a dynamic knowledge graph for ES to improve data discovery by leveraging implicit, latent existing knowledge present within the Web Pages of NASA DAACs websites. This project will strengthen ties between observations and user communities by:
- developing a knowledge graph derived from Web Pages via natural language processing and knowledge extraction techniques, and
- allowing users to traverse, explore, query, reason and navigate ES data via knowledge graph interaction.
Please see the wiki entry covering an end-to-end example of knowledge graph construction using ESKG.
ESKG was initially conceived and funded through the ESIP Testbed initiative. ESIP funding acknowledged.
ESKG is licensed permissively under the Apache License v2.0 a copy of which ships with this source code.