Wikidata is the largest collaborative general knowledge graph supported by a worldwide community. It includes many helpful topics for knowledge exploration and data science applications. However, due to the enormous size of Wikidata, it is challenging to retrieve a large amount of data with millions of results, make complex queries requiring large aggregation operations, or access too many statement references. This paper introduces our preliminary works on Wikidata-lite, a toolkit to build a database offline for knowledge extraction and exploration, e.g., retrieving item information, statements, provenances, or searching entities by their keywords, attributes. Wikidata-lite has high performance and memory efficiency, much faster than the official Wikidata SPARQL endpoint for big queries. The Wikidata-lite repository is available at https://github.com/phucty/wikidb.

Thanks to the open government initiative movement, many base registries have been made available and beneficial for all citizens to ensure accountability and transparency. All data are easy to access and process by people for effective, efficient public oversight. However, most base registries publish their data in a different data model or structure. As a result, building an AI system to utilize these data is challenging since they are machine-unreadable, poorly managed, and scattered. This paper proposes a design method for base registry data structures that uses unification and federation to improve the accessibility and availability of data. We also use Wikibase as an underlying knowledge graph to inherit common sense knowledge available in Wikidata to improve its usability.