From Queriability to Informativity, Assessing “Quality in Use” of DBpedia and YAGO
- Cite this paper as:
- Ruan T., Li Y., Wang H., Zhao L. (2016) From Queriability to Informativity, Assessing “Quality in Use” of DBpedia and YAGO. In: Sack H., Blomqvist E., d'Aquin M., Ghidini C., Ponzetto S., Lange C. (eds) The Semantic Web. Latest Advances and New Domains. ESWC 2016. Lecture Notes in Computer Science, vol 9678. Springer, Cham
In recent years, an increasing number of semantic data sources have been published on the web. These sources are further interlinked to form the Linking Open Data (LOD) cloud. To make full use of these data sets, it is necessary to learn their data qualities. Researchers have proposed several metrics and have developed numerous tools to measure the qualities of the data sets in LOD from different dimensions. However, there exist few studies on evaluating data set quality from the users’ usability perspective and usability has great impacts on the spread and reuse of LOD data sets. On the other hand, usability is well studied in the area of software quality. In the newly published standard ISO/IEC 25010, usability is further broadened to include the notion of “quality in use” besides the other two factors, namely, internal and external. In this paper, we first adapt the notions and the methods used in software quality to assess the data set quality. Second, we formally define two quality dimensions, namely, Queriability and Informativity from the perspective of quality in use. The two proposed dimensions correspond to querying and answering, respectively, which are the most frequent usage scenarios for accessing LOD data sets. Then we provide a series of metrics to measure the two dimensions. Last, we apply the metrics to two representative data sets in LOD (i.e., YAGO and DBpedia). In the evaluating process, we select dozens of questions from both QALD and WebQuestions and ask a group of users to construct queries as well as to check the answers with the help of our usability testing tool. The findings during the assessment not only illustrate the capability of our method and metrics but also give new insights on data quality of the two knowledge bases.