Linguistic Summaries of Graph Datasets Using Ontologies: An Application to Semantic Web.

This paper presents a new approach to performing linguistic summaries of graph datasets with the use of ontologies. Linguistic summarization is a well known data mining technique, aimed to discover patterns in data and present them in natural language. So far, this method has been applied only to relational databases. However amount of available graph datasets with associated ontologies is growing fast, hence we have investigated the problem of applying linguistic summaries in this scenario. As our first contribution, we propose to use an ontological class as subject of a summary, showing that its class taxonomy has to be used to properly select objects for summarization. Our second contribution is an extension to a summarizer, by analysis of set of ontological superclasses. We then propose extensions to quality measures \(T_1\) and \(T_2\), measuring informativeness of a summary in the context of ontological class taxonomy. We also show that our approach can create more general summarizations (higher in class taxonomy). We verify our proposals by performing linguistic summarization on Semantic Web, which is a vast distributed graph dataset with several associated ontologies. We conclude the paper with showing the possibilities of future work.