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Describing Scholarly Contributions semantically with the Open Research Knowledge Graph

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Describing Scholarly Contributions semantically with the Open Research Knowledge Graph

  1. 1. Prof. Dr. Sören Auer Leibniz University of Hannover TIB Technische Informationsbibliothek Describing Scholarly Contributions semantically with the Open Research Knowledge Graph
  2. 2. Page 2 How did information flows change in the digital era?
  3. 3. Page 3 How does it work today? The World of Publishing & Communication has profundely changed • New means adapted to the new possibilities were developed, e.g. „zooming“, dynamics • Business models changed completely • More focus on data, interlinking of data / services and search in the data • Integration, crowdsourcing, data curation play an important role
  4. 4. Page 4 What about Scholarly Communication?
  5. 5. Page 5 Scholarly Communication has not changed (much) 17th century 19th century 20th century 21th century Meanwhile other information intense domains were completely disrupted: mail order catalogs, street maps, phone books, …
  6. 6. Page 6 Challenges we are facing: We need to rethink the way how research is represented and communicated [1], [2] M. Baker: 1,500 scientists lift the lid on reproducibility, Nature, 2016. [3] Science and Engineering Publication Output Trends, National Science Foundation, 2018. [4] J. Couzin-Frankel: Secretive and Subjective, Peer Review Proves Resistant to Study. Science, 2013. Digitalisation of Science  Data integration and analysis  Digital collaboration Monopolisation by commercial actors  Publisher look-in effects  Maximization of profits [1] Reproducibility Crisis  Majority of experiments are hard or not reproducible [2] Proliferation of publications  Publication output doubled within a decade  continues to rise [3] Deficiency of Peer Review  Deteriorating quality [4]  Predatory publishing
  7. 7. Page 7 How can we avoid duplication if the terminology, research problems, approaches, methods, characteristics, evaluations, … are not properly defined and identified? How would you build an engine / building without properly defining their parts, relationships, materials, characteristics …? Duplication and Inefficiency Source: 4-visible-8-engine-plastic_1_d2162f52c3fa3a6f72d2722f6c50b7b2.jpg Source: models-cad-and-revit-design
  8. 8. Page 8 Lack of… Root Cause – Deficiency of Scholarly Communication? Transparency information is hidden in text Integratability fitting different research results together Machine assistance unstructured content is hard to process Identifyability of concepts beyond metadata Collaboration one brain barrier Overview Scientists look for the needle in the haystack
  9. 9. Page 9 How good is CRISPR (wrt. precision, safety, cost)? What specifics has genome editing with insects? Who has applied it to butterflies? Search for CRISPR: > 238.000 Results Source:, 04.2019
  10. 10. Page 10 How can we fix it?
  11. 11. Page 11 Realizing Vannevar Bush‘s vision of Memex Source: Source:
  12. 12. Page 12 Mathematics • Definitions • Theorems • Proofs • Methods • … Physics • Experiments • Data • Models • … Chemistry • Substances • Structures • Reactions • … Computer Science • Concepts • Implemen- tations • Evaluations • … Technology • Standards • Processes • Elements • Units, Sensor data Architecture • Regulations • Elements • Models • … Concepts Overarching Concepts  Research problems  Definitions  Research approaches  Methods Artefacts  Publications  Data  Software  Image/Audio/Video  Knowledge Graphs / Ontologies Domain specific Concepts
  13. 13. Page 13 KGs are proven to capture factual knowledge [1] Research Challenge: Manage • Uncertainty & disagreement • Varying semantic granularity • Emergence, evolution & provenance • Integrating existing domain models But maintain flexibility and simplicity Cognitive Knowledge Graphs for scholarly knowledge Towards Cognitive Knowledge Graphs • Fabric of knowledge molecules – compact, relatively simple, structured units of knowledge • Can be incrementally enriched, annotated, interlinked … [1] S Auer et al.: DBpedia: A nucleus for a web of open data. 6th Int. Semantic Web Conf. (ISWC) – 10-year best paper award. cf. also knowledge graphs from: WikiData, BBC, Google, Bing, Thomson Reuters, AirBnB, BNY Mellon …
  14. 14. Page 14 Factual Base entities Real world Granularity Atomic Entities Evolution Addition/deletion of facts Collaboration Fact enrichment From Factual Knowledge Graphs Today
  15. 15. Page 15 Factual Cognitive Base entities Real world Conceptual Granularity Atomic Entities Interlinked descriptions (molecules) with annotations (provenance) Evolution Addition/deletion of facts Concept drift, varying aggregation levels Collaboration Fact enrichment Emergent semantics From Factual to Cognitive Knowledge Graphs Today Needed for SKG
  16. 16. Page 16 Chemistry Example: CRISPR Genome Editing Source:
  17. 17. Page 17 1. Original Publication Chemistry Example: Populating the Graph 2. Adaptive Graph Curation & Completion Author Robert Reed Research Problem Genome editing in Lepidoptera Methods CRISPR / cas9 Applied on Lepidoptera Experimental Data 3. Graph representation CRISPR / cas9 editing in Lepidoptera Robert Reed Genome editing in Lepidoptera Experimental Data adresses CRSPRS/cas9isEvaluatedWith Genome editing
  18. 18. Page 18 Research Challenge: • Intuitive exploration leveraging the rich semantic representations • Answer natural language questions Exploration and Question Answering Question parsing Named Entity Recognition (NER) & Linking (NEL) Relation extraction Query con- struction Query execution Result rendering Q: How do different genome editing techniques compare? SELECT Approach, Feature WHERE { Approach adresses GenomEditing . Approach hasFeature Feature } [1] K. Singh, S. Auer et al: Why Reinvent the Wheel? Let's Build Question Answering Systems Together. The Web Conference (WWW 2018). Q: How do different genome editing techniques compare?
  19. 19. Page 19 Engineered Nucleases Site-specificity Safety Ease-of-use / costs/ speed zinc finger nucleases (ZFN) ++ 9-18nt + -- $$$: screening, testing to define efficiency transcription activator-like effector nucleases (TALENs) +++ 9-16nt ++ ++ Easy to engineer 1 week / few hundred dollar engineered meganucleases +++ 12-40 nt 0 -- $$$ Protein engineering, high-throughput screening CRISPR system/cas9 ++ 5-12 nt - +++ Easy to engineer few days / less 200 dollar Result: Automatic Generation of Comparisons / Surveys Q: How do different genome editing techniques compare?
  20. 20. Demo: Open Research Knowledge Graph
  21. 21. Recent ORKG Developments
  22. 22. Page 28 Business Logic (Data input/output, consistency) REST API (Interface to the outside world) SPARQL (Data Query Language) GraphQL (API Query Language) Neo4j (Linked Property Graph) Virtuoso (Triple Store) Domain Model (Statements, Resouces, etc.) AuthN & AuthZ (ORCID or other SSO) Contribute Curate Explore Third-party Apps ? (Other Database) User Interface PersistenceDomainApplication High-Level Architecture: Neo4j Graph Application
  23. 23. Page 29 Domain specific Research Contribution Templates
  24. 24. Page 30
  25. 25. Page 31 Survey Table Extraction
  26. 26. Page 32
  27. 27. Page 33
  28. 28. Page 34 Publishing State-of-the-Art Comparisons
  29. 29. Page 35 Organize Research Contributions along research problems
  30. 30. Page 36 Highlighting Contributors and Authors
  31. 31. Page 37 Domain Observatories Make organizations supporting the curation of research in a certain domain more visible
  32. 32. Page 38 Showcase contributing organizations
  33. 33. Page 39 The Team Prof. (Univ. S. Bolivar) Dr. Maria Esther Vidal Software Development Dr. Kemele Endris Collaborators TIB Scientific Data Mgmt. Group Leaders PostDocs Project Management Doctoral Researchers Dr. Markus Stocker Dr. Gábor Kismihók Dr. Javad Chamanara Dr. Jennifer D’Souza Allard Oelen Yaser Jaradeh Manuel Prinz Alex Garatzogianni Collaborators InfAI Leipzig / AKSW Dr. Michael Martin Natanael Arndt Dr. Lars Vogt Vitalis Wiens Kheir Eddine FarfarMuhammad Haris Administration Katja Bartel Simone Matern
  34. 34. Page 40 Conclusions Stay tuned   Consider creating an ORKG observatory for your domain  Mailinglist/group:!forum/orkg  Open Research Knowledge Graph:  ERC Consolidator Grant ScienceGRAPH on the topic  We need to reinvent scholarly communication  Knowledge Graphs are perfectly suited to capture research contributions in a structured and semantic way making them human and machine interpretable  With our Open Research Knowledge Graph initiative we aim to establish a registry for research contributions  Curation and synergistic combination of human, expert and machine intelligence is a challenge
  35. 35. Contact Prof. Dr. Sören Auer TIB & Leibniz University of Hannover

Editor's Notes

  • Kemele M. Endris, Mikhail Galkin, Ioanna Lytra, Mohamed Nadjib Mami, Maria-Esther Vidal, Sören Auer: MULDER: Querying the Linked Data Web by Bridging RDF Molecule Templates. DEXA (1) 2017: 3-18
  • D. Diefenbach, K. Singh, A. Both, D. Cherix, C. Lange, S. Auer. 2017. The Qanary Ecosystem: Getting New Insights by Composing Question Answering Pipelines. Int. Conf. on Web Engineering ICWE 2017.
    K. Singh, A. Sethupat, A. Both, S. Shekarpour, I. Lytra, R. Usbeck, A. Vyas, A. Khikmatullaev, D. Punjani, C. Lange, M.-E. Vidal, J. Lehmann, S. Auer: Why Reinvent the Wheel-Let's Build Question Answering Systems Together. The Web Conference (WWW 2018).
    S. Shekarpour, E. Marx, S. Auer, A. P. Sheth: RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem. AAAI 2017: 3936-3943
    D. Lukovnikov, A. Fischer, J. Lehmann, S. Auer: Neural Network-based Question Answering over Knowledge Graphs on Word and Character Level. WWW 2017: 1211-1220