Bahar Ghadiri Bashardoost

arXiv2020

Knowledge Translation: Extended Technical Report

We introduce Kensho, a tool for generating mapping rules between two Knowledge Bases (KBs). To create the mapping rules, Kensho starts with a set of correspondences and enriches them with additional semantic information automatically identified from the structure and constraints of the KBs. Our approach works in two phases. In the first phase, semantic associations between resources of each KB are captured. In the second phase, mapping rules are generated by interpreting the correspondences in a way that respects the discovered semantic associations among elements of each KB. Kensho's mapping rules are expressed using SPARQL queries and can be used directly to exchange knowledge from source to target. Kensho is able to automatically rank the generated mapping rules using a set of heuristics. We present an experimental evaluation of Kensho and assess our mapping generation and ranking strategies using more than 50 synthesized and real world settings, chosen to showcase some of the most important applications of knowledge translation. In addition, we use three existing benchmarks to demonstrate Kensho's ability to deal with different mapping scenarios.

cs.DB↗

arXiv2020

Data Lake Organization

We consider the problem of creating a navigation structure that allows a user to most effectively navigate a data lake. We define an organization as a graph that contains nodes representing sets of attributes within a data lake and edges indicating subset relationships among nodes. We present a new probabilistic model of how users interact with an organization and define the likelihood of a user finding a table using the organization. We propose the data lake organization problem as the problem of finding an organization that maximizes the expected probability of discovering tables by navigating an organization. We propose an approximate algorithm for the data lake organization problem. We show the effectiveness of the algorithm on both real data lakes containing data from open data portals and on benchmarks that emulate the observed characteristics of real data lakes. Through a formal user study, we show that navigation can help users discover relevant tables that cannot be found by keyword search. In addition, in our study, 42% of users preferred the use of navigation and 58% preferred keyword search, suggesting these are complementary and both useful modalities for data discovery in data lakes. Our experiments show that data lake organizations take into account the data lake distribution and outperform an existing hand-curated taxonomy and a common baseline organization.

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