Malawi, as a country with very limited resources, needs to have educational policies in place to maximize effectiveness of the public education system. Policymakers depend on accurate data, but variations in data between sources leaves policymakers uncertain as they attempt to craft policies to address the growing educational crisis in Malawi. A desk study was performed to evaluate the policy implications of employing data from multiple sources using primary school teacher supply and demand in Malawi as an illustration. This study examined one national organization, Malawi's Ministry of Education, Science, and Technology (MoEST); three international aid and assistance organizations (IAAOs), including The Department for International Development (DIFD) from the UK, Japan International Cooperation Agency (JICA), and the United States Agency for International Development (USAID); and one global organization, The United Nations Educational, Scientific and Cultural Organization (UNSECO). The study documented differences and similarities between the data sources. Among the factors considered were the nature of each institution and the effect it could have on data collection, aggregation, analysis and reporting; the definitions used by each organization, and their implications for data use; and each organization's methods of collection, aggregation, analysis and reporting. The study found significant variations in the teacher supply and demand data presented by the five organizations, with variations of up to 333% between sources. To address this problem, it is recommended that the Government of Malawi (GoM) establish a central agency to standardize education data. Three policy scenarios are detailed, presenting the probable outcome of various actions the GoM could take regarding this recommendation.

The ever increasing amount of Semantic Web data gives rise to the question: How complete is the data? Though generally data on the Semantic Web is incomplete, many parts of data are indeed complete, such as the children of Barack Obama and the crew of Apollo 11. This thesis aims to study how to manage and consume completeness information about Semantic Web data. In particular, we first discuss how completeness information can guarantee the completeness of query answering. Next, we propose optimization techniques of completeness reasoning and conduct experimental evaluations to show the feasibility of our approaches. We also provide a technique to check the soundness of queries with negation via reduction to query completeness checking. We further enrich completeness information with timestamps, enabling query answers to be checked up to when they are complete. We then introduce two demonstrators, i.e., CORNER and COOL-WD, to show how our completeness framework can be realized. Finally, we investigate an automated method to generate completeness statements from text on the Web via relation cardinality extraction.

The ever increasing amount of Semantic Web data gives rise to the question: How complete is the data? Though generally data on the Semantic Web is incomplete, many parts of data are indeed complete, such as the children of Barack Obama and the crew of Apollo 11. This thesis aims to study how to manage and consume completeness information about Semantic Web data. In particular, we first discuss how completeness information can guarantee the completeness of query answering. Next, we propose optimization techniques of completeness reasoning and conduct experimental evaluations to show the feasibility of our approaches. We also provide a technique to check the soundness of queries with negation via reduction to query completeness checking. We further enrich completeness information with timestamps, enabling query answers to be checked up to when they are complete. We then introduce two demonstrators, i.e., CORNER and COOL-WD, to show how our completeness framework can be realized. Finally, we investigate an automated method to generate completeness statements from text on the Web via relation cardinality extraction.