Abstract : Finding commonalities between descriptions of data or knowledge is a fundamental task in Machine Learning. The formal notion characterizing precisely such commonalities is known as least general generalization of descriptions and was introduced by G. Plotkin in the early 70's, in First Order Logic. Identifying least general generalizations has a large scope of database applications ranging from query optimization (e.g., to share commonalities between queries in view selection or multi-query optimization) to recommendation in social networks (e.g., to establish connections between users based on their commonalities between profiles or searches). To the best of our knowledge, this is the first work that re-visits the notion of least general generalizations in the entire Resource Description Framework (RDF) and popular con-junctive fragment of SPARQL, a.k.a. Basic Graph Pattern (BGP) queries. Our contributions include the definition and the computation of least general generalizations in these two settings, which amounts to finding the largest set of com-monalities between incomplete databases and conjunctive queries, under deductive constraints. We also provide an experimental assessment of our technical contributions.