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Semantic search and Data Analysis with graphs - Iryna Feuerstein

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The document discusses practical experiences with the Neo4j graph database and its application in semantic search beyond full-text queries. It emphasizes the utility of graph structures for intelligent data investigation and improved search insights. The content excludes theoretical aspects of semantic search and focuses on real-world implementations and examples.

Technology
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Semantic Web
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© PRODYNA 2018 Slide 1 SEMANTIC SEARCH AND DATA ANALYSIS WITH GRAPHS SEPTEMBER 18, 2018
© PRODYNA 2018 Slide 2 WHAT IS THIS TALK ABOUT? • Practical experiences with the graph data base Neo4j • Implementing search application
© PRODYNA 2018 Slide 3 WHAT IS THIS TALK ABOUT? • Practical experiences with the graph data base Neo4j • Implementing search application • Not only full text search • Searching for relevant parts • Searching for similiar content
© PRODYNA 2018 Slide 4 WHAT IS THIS TALK NOT ABOUT? • Theory of semantic search • Theory of semantic web • RDF, OWL, natural language, etc
© PRODYNA 2018 Slide 5 DATA • German legislation https://www.gesetze-im-internet.de/ • Legal documents provided by https://openjur.de/ • Extractions of norm occurrences in didactic material kindly provided by Hanjo Hamann
© PRODYNA 2018 Slide 6 GRAPH DATA MODEL
© PRODYNA 2018 Slide 7 GRAPH DATA MODEL
© PRODYNA 2018 Slide 8 GRAPH DATA MODEL
© PRODYNA 2018 Slide 9 BY USING SECTION NUMBER XY FROM CODE ABC, FOLLOWING SECTIONS COULD BE OF INTEREST: ... RECOMMENDATIONS
© PRODYNA 2018 Slide 10 FIND SIMILIAR DOCUMENTS
© PRODYNA 2018 Slide 12 SUMMARY • Graph structures help me to investigate my data • Relationships help to make the queries more intelligent • As a result better insight and easier search
© PRODYNA 2018 Slide 13 CONTACT DETAILS @ira_res iryna.feuerstein@prodyna.com https://irare.github.io/graph-of-codes/

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Semantic search and Data Analysis with graphs - Iryna Feuerstein

  • 1. © PRODYNA 2018 Slide 1 SEMANTIC SEARCH AND DATA ANALYSIS WITH GRAPHS SEPTEMBER 18, 2018
  • 2. © PRODYNA 2018 Slide 2 WHAT IS THIS TALK ABOUT? • Practical experiences with the graph data base Neo4j • Implementing search application
  • 3. © PRODYNA 2018 Slide 3 WHAT IS THIS TALK ABOUT? • Practical experiences with the graph data base Neo4j • Implementing search application • Not only full text search • Searching for relevant parts • Searching for similiar content
  • 4. © PRODYNA 2018 Slide 4 WHAT IS THIS TALK NOT ABOUT? • Theory of semantic search • Theory of semantic web • RDF, OWL, natural language, etc
  • 5. © PRODYNA 2018 Slide 5 DATA • German legislation https://www.gesetze-im-internet.de/ • Legal documents provided by https://openjur.de/ • Extractions of norm occurrences in didactic material kindly provided by Hanjo Hamann
  • 6. © PRODYNA 2018 Slide 6 GRAPH DATA MODEL
  • 7. © PRODYNA 2018 Slide 7 GRAPH DATA MODEL
  • 8. © PRODYNA 2018 Slide 8 GRAPH DATA MODEL
  • 9. © PRODYNA 2018 Slide 9 BY USING SECTION NUMBER XY FROM CODE ABC, FOLLOWING SECTIONS COULD BE OF INTEREST: ... RECOMMENDATIONS
  • 10. © PRODYNA 2018 Slide 10 FIND SIMILIAR DOCUMENTS
  • 11. © PRODYNA 2018 Slide 12 SUMMARY • Graph structures help me to investigate my data • Relationships help to make the queries more intelligent • As a result better insight and easier search
  • 12. © PRODYNA 2018 Slide 13 CONTACT DETAILS @ira_res iryna.feuerstein@prodyna.com https://irare.github.io/graph-of-codes/

Editor's Notes