As we countdown to the annual Lucene/Solr Revolution conference in Austin this October, we’re highlighting talks and sessions from past conferences. Today, we’re highlighting Tummarello Delbru and Giovanni Renaud’s session on querying knowledge graphs with Solr/SIREn:

Knowledge Graphs have recently gained press coverage as information giants like Google, Facebook, Yahoo and Microsoft, announced having deployed Knowledge Graphs at the core of their search and data management capabilities. Very richly structured datasets like “Freebase” or “DBPedia” can be said to be examples of these. In this talk we discuss a reference architecture for high performance structured querying and search on knowledge graphs. While graph databases, e.g., Triplestores or Graph Stores, have a role in this scenario, it is via Solr along with its schemaless structured search plugin SIREn that it is possible to deliver fast and accurate entity search with rich structured querying. During the presentation we will discuss an end to end example case study, a tourism social data use case. We will cover extraction, graph databases, SPARQL, JSON-LD and the role of Solr/SIREn both as search and as high speed structured query component. The audience will leave this session with an understanding of the Knowledge Graph idea and how graph databases, SPARQL, JSON-LD and Solr/SIREn can be combined together to implement high performance real world applications on rich and diverse structured datasets.

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Renaud Delbru, Ph. D., CTO and Founder at SindiceTech, is leading the research and development of the SIREn engine and of all aspects related to large scale data retrieval and analytics. He is author of over a dozen academic works in the area of semi-structured information retrieval and big data RDF processing. Prior to SindiceTech, Renaud completed his Ph.D. on Information Retrieval for Semantic Web data at the Digital enterprise Research Institute, Galway where he worked on the semantic search engine project. Among his achievements, he led the team that won the Entity Search track of the Yahoo’s Semantic Search 2011.

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