Many institutions publish datasets as Open Data in catalogs, however, their retrieval remains problematic issue due to the absence of dataset search benchmarking. We propose a framework for evaluating findability of datasets, regardless of retrieval models used. As task-agnostic labeling of datasets by ground truth turns out to be infeasible in the general domain of open data datasets, the proposed framework is based on evaluation of entire retrieval scenarios that mimic complex retrieval tasks. In addition to the framework we present a proof of concept specification and evaluation on several similarity-based retrieval models and several dataset discovery scenarios within a catalog, using our experimental evaluation tool. Instead of traditional matching of query with metadata of all the datasets, in similarity-based retrieval the query is formulated using a set of datasets (query by example) and the most similar datasets to the query set are retrieved from the catalog as a result.

Companies and institutions now realize the potential of Linked Open Data (LOD) and they start publishing their own data as LOD. However, publishing LOD is still a challenging task. One of the main reasons is a lack of user friendly tooling which would properly support the whole LOD publishing process. The process typically consists of source data extraction, transformation to RDF, alignment with commonly used vocabularies, linking to other datasets, computing metadata, publishing on the web as a dump, loading into a triplestore and recording the dataset in a data catalog such as CKAN. In this paper we present LinkedPipes ETL, a tool for ETL-like LOD publishing, which mainly focuses on supporting such LOD publishing workflows in a user friendly way. In addition, the tool also eases consumption of already existing LOD data sources as it addresses some of the practical issues associated with it. Finally, the tool itself uses Linked Data technologies for representation of the ETL processes. We describe LinkedPipes ETL and its main distinguishing features in context of the use cases in which the tool has already been deployed. They include an institution of public administration, a municipality, a university, a software company and an open data initiative.