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We present a scalable, open-source platform that "distills" a potentially large text collection into a knowledge graph. Our platform takes documents stored in Apache Solr and scales out the Stanford CoreNLP toolkit via Apache Spark integration to extract mentions and relations that are then ingested into the Neo4j graph database. The raw knowledge graph is then enriched with facts extracted from an external knowledge graph. The complete product can be manipulated by various applications using Neo4j's native Cypher query language: We present a subgraph-matching approach to align extracted relations with external facts and show that fact verification, locating textual support for asserted facts, detecting inconsistent and missing facts, and extracting distantly-supervised training data can all be performed within the same framework.

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Anserini is an open-source information retrieval toolkit built around Lucene to facilitate replicable research. In this demonstration, we examine different architectures for Solr integration in order to address two current limitations of the system: the lack of an interactive search interface and support for distributed retrieval. Two architectures are explored: In the first approach, Anserini is used as a frontend to index directly into a running Solr instance. In the second approach, Lucene indexes built directly with Anserini can be copied into a Solr installation and placed under its management. We discuss the tradeoffs associated with each architecture and report the results of a performance evaluation comparing indexing throughput. To illustrate the additional capabilities enabled by Anserini/Solr integration, we present a search interface built using the open-source Blacklight discovery interface.

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