Graph first, semantics follows
Abstract: Organizations that want to build new applications using the relationships in their data are confronted with a choice between RDF and property graph models. Today, this choice may have long-ranging ramifications (e.g., when applications must interoperate with other systems), where instead it would be desirable to have users benefit from the best of each world. Motivated by real use cases in the context of Amazon Neptune, a fully managed graph database service that supports both SPARQL queries for RDF as well as Apache TinkerPop Gremlin queries for property graphs, we will highlight aspects that cause organizations to prefer property graphs over RDF (or vice versa) when building their graph applications. Advocating a “graph first, semantics follows” paradigm, we encourage the community to work towards a unification of existing graph data modeling and management approaches. Alongside, this includes exploring improvements of the Semantic Web technology stack for graph use cases (e.g., advanced path queries, graph analytics, and machine learning) and a stronger focus on its usage in the enterprise context. With the ongoing proliferation of graph databases in the industry, a shift in focus towards interdisciplinary, enterprise grade graph data management research could open up a unique chance for the community to make Semantic Web technologies mainstream, starting out from within the enterprise rather than the Web.
Bio: Michael Schmidt is a Principal Engineer at AWS, leading the technical design and development of Amazon Neptune’s query language and optimization stack. He holds a PhD in Computer Science from Albert Ludwig University of Freiburg. His academic work focuses on the interface between Semantic Web query languages and traditional database management approaches, aiming to lay the foundations for building scalable, high-performance RDF data management systems. For his seminal work “Foundations of SPARQL Query Optimization”, Michael was awarded the ICDT 2020 Test of Time award. Throughout his industrial career Michael has been pursuing the mission to make Semantic Technologies and graph databases mainstream, working on both building applications on top of these technology stacks as well as designing and implementing enterprise-ready data management solutions.