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Knowledge Graph Introduction

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Knowledge Graph Introduction

  1. 1. Prof. Dr. Sören Auer Knowledge Graphs Winter School February 23rd, 2021 Introduction to Knowledge Graphs
  2. 2. Page 2 About me - Prof. Dr. Sören Auer Now: Professor for Data Science and Digital Libraries, Leibniz University of Hannover Director TIB Leibniz Information Center for Science & Technology • TIB is with >500 employees the largest science and technology information centre world-wide • Strategy: organizing research data and information using knowledge graphs • Member of the board of L3S research center – a world-leading responsible AI Previously: U Bonn, Fraunhofer, U Leipzig, U Pennsylvania, Ural State Uni Ekaterinburg, TU Dresden Publications in major venues: Web Conf., IJCAI, AAAI, ISWC, ESWC, K-CAP, TPDL, JWS, SWJ, JDIQ  H-index: 57, >21.000 citations, >15 best paper awards incl. test-of-time and 10-year awards Major scientific contributions: • Technology platforms: OntoWiki & DBpedia, LOD2 Linked Data and BigDataEurope software stacks • Acquisition of >20M€ for my research groups in Leipzig, Bonn and Hannover • Strategic projects: ERC ScienceGraph, LOD2, BigDataEurope, Marie Curie ITN WDAqua Impact & Transfer: W3C standards, 5 students now professors, successful spin-off company, portfolio of open-source software, Int. Data Spaces Initiative, Big Data Value Association
  3. 3. --- VERTRAULICH --- Zuse Z3: the beginning of Computing – close to the hardware Foto: Konrad Zuse Internet Archiv/Deutsches Museum/DFG
  4. 4. © Fraunhofer
  5. 5. --- VERTRAULICH --- We can make things more intuitive Picture: The illustrated recipes of lucy eldridge http://thefoxisblack.com/2013/ 07/18/the-illustrated-recipes- of-lucy-eldridge/
  6. 6. Computing more inuitive: procedural programming
  7. 7. Sören Auer 7
  8. 8. Computing more inuitive: OO programming
  9. 9. Sören Auer 9
  10. 10. Sören Auer 10 Computing even more inuitive: with cognitive data?!
  11. 11. Page 11 Machine Learning and Big Data http://www.spacemachine.net/views/2016/3/datasets-over-algorithms  AI is not just the next hype after Big Data, Big Data is the reason why we have AI!
  12. 12. Page 12 Source: Gesellschaft für Informatik The Three “V” of Big Data - Variety often Neglected
  13. 13. Page 13 Tackling the Variety Dimension with the FAIR and Linked Data Principles 1. Use URIs to identify the “things” in your data 2. Use http:// URIs so people (and machines) can look them up on the web 3. When a URI is looked up, return a description of the thing in the W3C Resource Description Format (RDF) 4. Include links to related things http://www.w3.org/DesignIssues/LinkedData.html
  14. 14. Page 14 1. Graph based RDF data model consisting of S-P-O statements (facts) RDF & Linked Data in a Nutshell WinterSchool dbpedia: Paderborn 23.02.2021 KnowGraphs conf:organizes conf:starts conf:takesPlaceIn 2. Serialised as RDF Triples: KnowGraphs conf:organizes WinterSchool . WinterSchool conf:starts “2021-02-23”^^xsd:date . WinterSchool conf:takesPlaceAt dbpedia:Paderborn . 3. Publication under URL in Web, Intranet, Extranet Subject Predicate Object
  15. 15. Page 15 Creating Knowledge Graphs with RDF Linked Data located in label industry headquarters full name DHL Post Tower 162.5 m Bonn Logistics Logistik DHL International GmbH height 物流 label
  16. 16. Page 16 Graph consists of:  Resources (identified via URIs)  Literals: data values with data type (URI) or language (multilinguality integrated)  Attributes of resources are also URI-identified (from vocabularies) Various data sources and vocabularies can be arbitrarily mixed and meshed URIs can be shortened with namespace prefixes; e.g. dbp: → http://dbpedia.org/resource/ RDF Data Model (a bit more technical) gn:locatedIn rdfs:label dbo:industry ex:headquarters foaf:name dbp:DHL_International_GmbH dbp:Post_Tower "162.5"^^xsd:decimal dbp:Bonn dbp:Logistics "Logistik"@de "DHL International GmbH"^^xsd:string ex:height "物流"@zh rdfs:label rdf:value unit:Meter ex:unit
  17. 17. Page 17 Knowledge Graph Example: DBpedia • Automatically extracted from Wikipedia infoboxes • Crystalization point of the LOD Cloud https://lod-cloud.net/
  18. 18. Vocabularies – Breaking the mold! • Semantic data virtualization allows for continuous expansion and enhancement of data and metadata across data sources without loosing the overall perspective Relational data models 1:1 Relation between Data Model und Application Graph based data model Subject Predicate Object / Subject Predicate Object / Subject 1:n Relation between Data Model and Application
  19. 19. RDF mediates between different Data Models & bridges between Conceptual and Operational Layers Id Title Screen 5624 SmartTV 104cm 5627 Tablet 21cm Prod:5624 rdf:type Electronics Prod:5624 rdfs:label “SmartTV” Prod:5624 hasScreenSize “104”^^unit:cm ... Electronics Vehicle Car Bus Truck Vehicle rdf:type owl:Thing Car rdfs:subClassOf Vehicle Bus rdfs:subClassOf Vehicle ... Tabular/Relational Data Taxonomic/Tree Data Logical Axioms / Schema Male rdfs:subClassOf Human Female rdfs:subClassOf Human Male owl:disjointWith Female ... Sören Auer 19
  20. 20. Seite 20 Example: Mapping of Research Data to Ontologies Krankheit Symptom Prävalenz Grippe Fieber 1000 Krebs Blutung 30 ... ... ... Disease ICD-10 Symptoms Medication Influenza J10 Fever Amantadin Cancer C00-C97 Bleeding Chemotherapy ... ... ... ... Symptom Disease ICD-10 Code Prevalence ICD-10 Code Type Drug Name Classification Concepts Attributes hasSymptom ... ... ... hasTreatment Vocabulary Layer Data Layer Relations Mappings
  21. 21. Seite 21 Example: Semantic Research Data in Engineering
  22. 22. Page 22 • collaborative, community activity to create, maintain, and promote schemas for structured data on the Internet • can be used with many different encodings, including RDFa, Microdata and JSON-LD • covers entities, relationships between entities and actions • can easily be extended through a well-documented extension model • >10 million sites use Schema.org to markup their web pages and email messages • Founded by Google, Microsoft, Yahoo and Yandex Vocabulary Example: Schema.org
  23. 23. Die Semantic Web Layer Cake 2001 http://www.w3.org/2001/10/03-sww-1/slide7-0.html • Monolithisch basierend auf XML • Fokus auf schwergewichtige Semantik (Ontologien, Logic, Reasoning)
  24. 24. The Semantic Web Layer Cake now – Bridging between Data Unicode URIs XML JSON CSV RDB HTML RDF RDF/XML JSON-LD CSV2RDF R2RML RDFa RDF Data Shapes RDF-Schema Vocabularies Ontologies SKOS Thesauri Logic Rules SPARQL (Access control), Signatur, Encryption (HTTPS/CERT/DANE), • Lingua Franca of Data integration with many technology interfaces (XML, HTML, JSON, CSV, RDB,…) • Focus on lightweight vocabularies, rules, thesauri etc. • Less “invasive”
  25. 25. RDF - the Lingua Franca of Data Integration • RDF is simple • We can easily encode and combine all kinds of data models (relational, taxonomic, graphs, object-oriented, …) • RDF supports distributed data and schema • We can seamlessly evolve simple semantic representations (vocabularies) to more complex ones (e.g. ontologies) • Small representational units (URI/IRIs, triples) facilitate mixing and mashing • RDF can be viewed from many perspectives: facts, graphs, ER, logical axioms, graphs, objects • RDF integrates well with other formalisms - HTML (RDFa), XML (RDF/XML), JSON (JSON-LD), CSV, … • Linking and referencing between different knowledge bases, systems and platforms facilitates the creation of sustainable data ecosystems 25
  26. 26. Page 26 • Fabric of concept, class, property, relationships, entity descriptions • Uses a knowledge representation formalism (typically RDF, RDF-Schema, OWL) • Holistic knowledge (multi-domain, source, granularity): • instance data (ground truth), • open (e.g. DBpedia, WikiData), private (e.g. supply chain data), closed data (product models), • derived, aggregated data, • schema data (vocabularies, ontologies) • meta-data (e.g. provenance, versioning, documentation licensing) • comprehensive taxonomies to categorize entities • links between internal and external data • mappings to data stored in other systems and databases Knowledge Graphs – A definition Smart Data for Machine Learning
  27. 27. Page 27 Manual • Curation / Crowdsourcing Markup • schema.org Mapping Structured Data • R2RML/RML Leveraging Natural Language Processing (NLP) from text • Named Entity Recognition • Relation Extraction Knowledge Graph Creation Ignaz Wanders: Build your own Knowledge Graph: From unstructured dark data to valuable business insights https://medium.com/vectrconsulting/build-your-own-knowledge-graph- 975cf6dde67f
  28. 28. Page 28 Querying Knowledge Graphs Graph Patterns Corresponding SPARQL Query: SELECT ?ev, ?vn1, ?vn2 WHERE { ?ev a Food_Festival . ?ev venue ?vn1 . ?ev venue ?vn2 . } A. Hogan, E. Blomqvist, M. Cochez, C. d'Amato, G. de Melo, C. Gutierrez, J. E. Labra Gayo, S. Kirrane, S. Neumaier, A. Polleres, R. Navigli, A.-C. Ngonga Ngomo, S. M. Rashid, A. Rula, L. Schmelzeisen, J. Sequeda, S. Staab, Antoine Zimmermann: Knowledge Graphs, arXiv:2003.02320 [cs.AI]
  29. 29. Page 29 Knowledge Graph Reasoning Reveals implicit information
  30. 30. Page 30 Knowledge Graph Refinement Completion • Filling missing edges • Often addressed with link prediction • Special tasks: type and identity prediction Correction • Fact validation • Inconsistency repair
  31. 31. Page 31 Knowledge Graph Quality [1] Zaveri, Rula, Maurino, Auer, Lehmann: Quality Assessment for Linked Open Data. Semantic Web Journal, 2015 A1: server responds to a SPARQL query A2: RDF dump is available A3: detection of dereferenceability of URIs A4: HTTP response header with appropriate content type A5: dereferenceability of all forward links CM1: schema completeness: ratio of represented classes/properties CM2: property completeness CM3: population completeness: ratio of real-world objects CM4: interlinking completeness: ratio of interlinked instances Data quality is “fitness for use” Use cases vary  various quality criteria/measures organized along various dimensions
  32. 32. Page 32
  33. 33. Page 33 Instances in DBpedia & Wikidata Knowledge Graphs on the Web -- an Overview N. Heist, S. Hertling, D. Ringler, H. Paulheim
  34. 34. Page 34 Search Engine Optimization & Web-Commerce  Schema.org used by >20% of Web sites  Major search engines exploit semantic descriptions Pharma, Lifesciences  Mature, comprehensive vocabularies and ontologies  Billions of disease, drug, clinical trial descriptions Digital Libraries  Many established vocabularies (DublinCore, FRBR, EDM)  Millions of aggregated from thousands of memory institutions in Europeana, German Digital Library Emerging Knowledge Graphs & Data Spaces
  35. 35. Page 35 Initiatives for decentral, semantic data spaces Web/Ecommerce Digital Libraries Life Sciences Industry Open Government Data Vocabularies schema.org Europeana Data Model DCAT, DC, PROV- O, FOAF, VoiD DCAT, IDS Vocabulary DCAT Participants ~30% of Web pages Memory Institutions (2000 in Germany) Pharma companies 80 companies (SAP, Siemens, Telekom, PWC) EU, Countries, Cities, Counties License Governance CC-BY-SA GitHub, Google, Microsoft, Ya ndex... CC0 Europeana Association CC-BY-SA IDS Association Open Data Applications Google Knowledge Graph (Produkte, Personen, ...) DDB.de, Europeana.eu OpenPhacts.org Industrial Data Space Transparency, Mobility, Budget, Planing
  36. 36. Page 36 The Trinity of Semantic Integration Knowledge Graphs • Complex fabric of concepts & relationships • Focus on heterogenous, multi-domain knowledge representation Data Spaces • Community of organizations agreeing on standards for data access/ security/ semantics/ governance/ licenses • Focus on data sharing & exchange Semantic Data Lakes • Storage facility for enterprise/research data • Use Big Data (HDFS) management • Focus on scalable data access Use in a single organization Intra-organizational use
  37. 37. Page 37 Industry Knowledge Graph Adoption https://www.slideshare.net/ Frank.van.Harmelen/adopti on-of-knowledge-graphs- late-2019 Eccenca aims at making KGs a commodity
  38. 38. Page 38 Knowledge Graph Challenges & Opportunities Knowledge graphs typically cover • Multiple domains • Various levels of granularity • Data from multiple sources • Various degrees of structure Challenges • Quality • Coherence • Co-evolution • Update propagation • Curation & interaction Opportunities • Background knowledge for various applications (e.g. question answering, data integration, machine learning) • Facilitate intra-organizational data exchange (data value chains) 38 Knowledge Graphs on the Web -- an Overview N. Heist, S. Hertling, D. Ringler, H. Paulheim DBpedia YAGO WikiData BabelNet Cyc NELL CaLiGraph Voldemort
  39. 39. Page 39 Comparison of various enterprise data integration paradigms Paradigm Data Model Integr. Strategy Conceptual/ operational Hetero- geneous data Intern./ extern. data No. of sources Type of integr. Domain coverage Se- mantic repres. XML Schema DOM trees LaV operational   medium both medium high Data Warehouse relational GaV operational - partially medium physical small medium Data Lake various LaV operational   large physical high medium MDM UML GaV conceptual - - small physical small medium PIM / PCS trees GaV operational partially partially - physical medium medium Enterprise search document - operational  partially large virtual high low EKG RDF LaV both   medium both high very high [1] M. Galkin, S. Auer, M.-E. Vidal, S. Scerri: Enterprise Knowledge Graphs: A Semantic Approach for Knowledge Management in the Next Generation of Enterprise Information Systems. ICEIS (2) 2017: 88-98
  40. 40. Page 40 Knowledge Graph Technology 4
  41. 41. Example Enterprise Data Integration with A Semantic Data Lake
  42. 42. Page 42 Perspectives on data turn into silos Parts of data are being curated, duplicated, annotated and simply changed over time, making reconciliation and interpretation a challenge Engineering Manufactur. Logistics Marketing . . .
  43. 43. Page 43 Integrate Using RDF & Vocabularies Engineering Manufactur. Logistics Marketing
  44. 44. App. 1 App. 2 App. 3 App. 1 App. 2 App. 3 Data Access limited to connected source Exploding cost of ETL Full Access to All Data Lean Architecture Great Synergies in data lifting Knowledge Graph based Enterprise Data Innovation Architecture The future of data management is semantic! Enterprise Integration with a Semantic Data Lake The Problem today
  45. 45. Management Accounting Risk Management Regulatory Reporting Treasury Marketing Accounting Corporate Memory Inbound Data Sources Outbound and Consumption Inbound Raw Data Store Knowledge Graph for Meta Data, KPI Definition and Data Models Frontend to Access Relationship and KPI Definition / Documentation Frontend to Access (ad hoc) Reports Outbound Data Delivery to Target Systems Big Data DWH- Infrastructur e High Level Architecture Corporate Memory
  46. 46. Interlinking/ Fusing Classification / Enrichment Quality Analysis Evolution / Repair Search/ Browsing/ Exploration Extraction Storage/ Querying Manual revision/ authorin g Covering the Linked Data Life Cycle • Extraction / Mapping • Storage / Querying • Manual Revision / Authoring • Linking / Fusion • Classification / Enrichment • Quality / Evolution • Search / Browse / Explore Triple/Quad Store Backend RDB2RDF UI Framework Repair UI Framework DataPlatform Data Manager Data Integration Ontology Learner DataPlatform RDB2RDF DataManager NLP Core eccenca USPs: • Provenance Tracking • Graph Replication • Data Mapping • Versioning • Access Control © eccenca GmbH 2018
  47. 47. © eccenca GmbH 2018
  48. 48. Organizing Scholarly Communication with Knowledge Graphs
  49. 49. Page 49 How did information flows change in the digital era?
  50. 50. Page 50 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
  51. 51. Page 51 What about Scholarly Communication?
  52. 52. Page 52 Scholarly Communication has not changed (much) 17th century 19th century 20th century 21th century Meanwhile other information intense domains were completely disrupted:
  53. 53. Page 53 Challenges we are facing: We need to rethink the way how research is represented and communicated [1] http://thecostofknowledge.com, https://www.projekt-deal.de [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
  54. 54. Page 54 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
  55. 55. Page 55 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: https://scholar.google.de/scholar?hl=de&as_sdt=0%2C5&q=CRISPR&btnG=, 04.2019
  56. 56. Page 56 How can we fix it?
  57. 57. Page 57 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
  58. 58. Page 58 KGs are proven to capture factual knowledge 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 …
  59. 59. Page 59 Factual Base entities Real world Granularity Atomic Entities Evolution Addition/deletion of facts Collaboration Fact enrichment From Factual Knowledge Graphs Today
  60. 60. Page 60 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
  61. 61. Page 61 Chemistry Example: CRISPR Genome Editing Source: https://cacm.acm.org/system/assets/0002/2618/021116_Google_KnowledgeGraph.large.jpg?1476779500&1455222197
  62. 62. Page 62 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 https://doi.org/10.5281/zenodo.89691 6 3. Graph representation CRISPR / cas9 editing in Lepidoptera https://doi.org/10.1101/130344 Robert Reed https://orcid.org/0000-0002-6065-6728 Genome editing in Lepidoptera Experimental Data https://doi.org/10.5281/zenodo.896916 adresses CRSPRS/cas9 isEvaluatedWith Genome editing https://www.wikidata.org/wiki/Q24630389
  63. 63. Page 63 Research Challenge: • Intuitive exploration leveraging the rich semantic representations • Answer natural language questions Exploration and Question Answering Questi on parsin g Named Entity Recogniti on (NER) & Linking (NEL) Relatio n extracti on Query con- structi on Query executi on Result renderi ng 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?
  64. 64. Page 64 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?
  65. 65. Conclusion
  66. 66. Page 72 Hybrid AI – combination of smart data (knowledge graphs) and smart analytics Distributed semantic technologies – knowledge representation using vocabularies, ontologies Question Answering • Open Question Answering architecture – flexible, knowledge-based integration architecture for QA components and pipelines • Dialogue Systems - combination of language models and goal-driven question answering Integration with Crowdsourcing Knowlege Graphs, Semantic Data Lakes Robotics – usage of semantics for actuation Agile Interoperability – leveraging community driven vocabulary development Cognitive Data challenges where Knowledge Graphs can make a difference
  67. 67. Page 73 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 Farfar Muhammad Haris Administration Katja Bartel Simone Matern
  68. 68. https://de.linkedin.com/in/soerenauer https://twitter.com/soerenauer https://www.xing.com/profile/Soeren_Auer http://www.researchgate.net/profile/Soeren_Auer TIB & Leibniz University of Hannover auer@tib.eu Prof. Dr. Sören Auer

Hinweis der Redaktion

  • Die Z3 war der erste funktionsfähige Digitalrechner weltweit und wurde 1941 von Konrad Zuse in Zusammenarbeit mit Helmut Schreyer in Berlin gebaut. Die Z3 wurde in elektromagnetischer Relaistechnik mit 600 Relais für das Rechenwerk und 1400 Relais für das Speicherwerk ausgeführt.
  • Longquan stoneware incense burner, China, 12th-13th century AD. Part of the Percival David Collection of Chinese Ceramics.
  • Breakthroughs in AI come after data is available, not after algorithmic discoveries
    If you think about AI, think about the data, not algorithms
    Fun fact: most major AI companies share their internal deep learning toolkits
  • Map the silos to their domain appropriate schemas
    Link the nodes (Linked Data)
    The schema can be virtual – multiple schemas/views may be appropriate
  • You could argue: That MDM & BI Hub-Spoke systems have had the objective of the “Solution Tomorrow”, but were never able to fulfill on this promise due to their reliance on relational paradigm that prevent them from having the flexibility to truly provide an unlimited amount of perspectives on the same data. MDM & BI Hubs in the opposite have required all perspectives to be aligned with the one single truth that was physically incorporated in the backbone and paradigm of these respective approaches.
  • 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

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