The Web of Data, and in particular Linked Data, has seen tremendous growth over the past years. However, reuse and take-up of these rich data sources is often limited and focused on a few well-known and established RDF datasets. This can be partially attributed to the lack of reliable and up-to-date information about the characteristics of available datasets. While RDF datasets vary heavily with respect to the features related to quality, coverage, dynamics and currency, reliable information about such features is essential to enable dataset discovery in tasks such as entity linking, distributed query, search or question answering. Even though there exists a wealth of works contributing to the problem of dataset profiling in general, these works are spread across a wide range of communities. In this survey, we provide a first comprehensive survey of the RDF dataset profile features, methods, tools and vocabularies. We organize these building blocks of dataset profiling in a taxonomy and emphasize the links between the dataset profiling and feature extraction approaches and several application domains. The survey is aimed towards data practitioners, data providers and scientists, spanning a large range of communities and drawing from different fields such as dataset profiling, assessment, summarization and characterization. Ultimately, this work is intended to facilitate the reader to identify and locate the relevant features for building a dataset profile for intended applications together with the tools capable of extracting these features from the data.

Abstract. With the growing quantity and diversity of publicly available web datasets, most notably Linked Open Data, recommending datasets, which meet specific criteria, has become an increasingly important, yet challenging problem. This task is of particular interest when addressing issues such as entity retrieval, semantic search and data linking. Here, we focus on that last issue. We introduce a dataset recommendation approach to identify linking candidates based on the presence of schema overlap between datasets. While an understanding of the nature of the content of specific datasets is a crucial prerequisite, we adopt the notion of dataset profiles, where a dataset is characterized through a set of schema concept labels that best describe it and can be potentially enriched by retrieving their textual descriptions. We identify schema overlap by the help of a semantico-frequential concept similarity measure and a ranking criterium based on the tf*idf cosine similarity. The experiments, conducted over all available linked datasets on the Linked Open Data cloud, show that our method achieves an average precision of up to 53% for a recall of 100%. As an additional contribution, our method returns the mappings between the schema concepts across datasets -a particularly useful input for the data linking step.