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The use of “open data” can help the public find value in various areas of interests. Many governments have created and published a huge amount of open data; however, people have a hard time using open data because of data quality issues. The UK, the USA and Korea have created and published open data; however, the rate of open data implementation and level of open data impact is very low because of data quality issues like incompatible data formats and incomplete data. This study aims to compare the statuses of data quality from open government sites in the UK, the USA and Korea and also present guidelines for publishing data format and enhancing data completeness.

This study uses statistical analysis of different data formats and examination of data completeness to explore key issues of data quality in open government data.

Findings show that the USA and the UK have published more than 50 per cent of open data in level one. Korea has published 52.8 per cent of data in level three. Level one data are not machine-readable; therefore, users have a hard time using them. The level one data are found in portable document format and hyper text markup language (HTML) and are locked up in documents; therefore, machines cannot extract out the data. Findings show that incomplete data are existing in all three governments’ open data.

Governments should investigate data incompleteness of all open data and correct incomplete data of the most used data. Governments can find the most used data easily by monitoring data sets that have been downloaded most frequently over a certain period.

As international users increase rapidly, multilingual systems have become a very important service for global users. The purpose of this paper is to design and implement an ontology‐driven medical information retrieval (OMIR) system by building a medical ontology based on the Centers for Disease Control and Prevention's (CDC) medical records.

A traditional cataloging scheme is used as a navigation menu in the CDC system. This traditional cataloging scheme is transformed to a unique medical ontology for global users in the OMIR system. An experimental study was conducted on both an ontology‐driven medical information system (OMIR) and the CDC system.

The medical ontology can be used to filter out unsuitable resources based on semantic relationships. In addition, the recommended resources can be categorized and provide the patron with different languages to access resources. The OMIR system provides better relevancy and shorter search times compared with alternative systems.

The OMIR system is currently implemented for medical resources from the CDC. The developed method may also be applied to other domain areas.

This paper represents a practical method of building a multilingual medical information retrieval system and explains the functional use of ontological knowledge. This study provides insights into medical information seeking performance on the medical database systems.