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Bias in online information has recently become a pressing issue, with search engines, social networks and recommendation services being accused of exhibiting some form of bias. In this vision paper, we make the case for a systematic approach towards measuring bias. To this end, we discuss formal measures for quantifying the various types of bias, we outline the system components necessary for realizing them, and we highlight the related research challenges and open problems.

For ordinary users, the task of accessing knowledge graphs through structured query languages like SPARQL is rather demanding. As a result, various approaches exploit the simpler and widely used keyword-based search paradigm, either by translating keyword queries to structured queries, or by adopting classical information retrieval (IR) techniques. In this paper, we study and adapt Elasticsearch, an out-ofthe-box document-centric IR system, for supporting keyword search over RDF datasets. Contrary to other works that mainly retrieve entities, we opt for retrieving triples, due to their expressiveness and informativeness. We specify the set of functional requirements and study the emerging questions related to the selection and weighting of the triple data to index, and the structuring and ranking of the retrieved results. Finally, we perform an extensive evaluation of the different factors that affect the IR performance for four different query types. The reported results are promising and offer useful insights on how different Elasticsearch configurations affect the retrieval effectiveness and efficiency.

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