Computer Science

The current decade is a witness to an enormous explosion of data being published on the Web as Linked Data to max-imise its reusability. Answering questions that users speak or write in natural language is an increasingly popular application scenario for Web Data, especially when the domain of the questions is not limited to a domain where dedicated curated datasets exist, like in medicine. The increasing use of Web Data in this and other settings has highlighted the importance of assessing its quality. While quite some work has been done with regard to assessing the quality of Linked Data, only few efforts have been dedicated to quality assessment of linked data from the question answering (QA) perspective. From the linked data quality metrics that have so far been well documented in the literature, we have identified those that are most relevant for QA. We apply these quality metrics, implemented in the Luzzu framework, to subsets of two datasets of crucial importance to open domain QA – DBpedia and Wikidata – and thus present the first assessment of the quality of these datasets for QA. From these datasets, we assess slices covering the specific domains of restaurants, politicians, films and soccer players. The results of our experiments suggest that for most of these domains , the quality of Wikidata with regard to the majority of relevant metrics is higher than that of DBpedia.

Data published on the Web is growing every year. However, most of this data does not have semantic representation. Web tables are an example of structured data on the Web that has no clear semantics. While there is an emerging research effort in lifting tabular data into semantic web formats, most of the work is focused around entity recognition in tables with simple structure. In this work we explore how capture the semantics of complex tables and transform them to knowledge graph. These complex tables include contextual information about statements, such as time or provenance. Hence, we need to use contextualized knowledge graphs to represent the information of the tables. We explore how this contextual information is represented in tables, and relate it to previous classifications of web tables, and how to encode it in RDF using different approaches. Finally, we present a prototype tool that converts web tables from Wikipedia into RDF, trying to cover all existing approaches.