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Leveraging Knowledge Graphs in your Enterprise Knowledge Management System
The document discusses the capabilities of PoolParty 7.0, a platform for managing knowledge graphs throughout the linked data life cycle, and highlights its applications across various industries such as healthcare, HR, and life sciences. It emphasizes the integration of unstructured and structured data using semantic AI to solve complex business problems and enhance data management. The document also outlines the features of PoolParty, including improved ontology management and user interface, and encourages starting new knowledge graph projects.
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Leveraging Knowledge Graphs in your Enterprise Knowledge Management System
- 1. Andreas Blumauer CEO, SemanticWeb Company Leveraging Knowledge Graphs in your Enterprise Knowledge Management System Use PoolParty 7.0 to manage Knowledge Graphs along the whole Linked Data Life Cycle
- 2. CMS/DMS /DAM/.. Graph-based Introduction 2 Semantic Web Company Founder & CEOof Andreas Blumauer developer & vendor of 2004founded 7.0version active at based on headquartered part of Knowledge Graphs manages standard for part of >200 serves customers Taxonomies Ontologies standard for graduates Text Mining used for Graph database integrates with PoolParty Software Ltd Director of parent company of London located named by Vienna Gartner KMWorld Search engine
- 3. Current Status of theGraph Market Moving towards Semantic AI
- 4. 4Knowledge Graph Adoption Source: Collapsingthe IT Stack: Clearing a path for AI adoption Alan Morrison (Sr. Research Fellow at PwC)
- 5. 5Hype Cycle for Artificial Intelligence, 2018 “Oncestructured in the form of a knowledge graph, unstructured data can be queried, thereby preprocessing it for analysis.”
- 6. 6Semantic AI Fusing Machine Learningwith Knowledge Graphs “...the use of graphs as a means to better generalize from one instance of a problem to another 1) .” 1) Relational inductive biases, deep learning, and graph networks 2) AI Requires More Than Machine Learning (via Forbes) 3) DARPA Embraces ‘Common Sense’ Approach to AI “The confluence of symbolic reasoning and machine learning enables the enterprise to solve an assortment of complicated business problems applicable to real-world situations -- as opposed to simply automating facile, repetitive tasks. 2) .” “The absence of common sense prevents an intelligent system from … communicating naturally with people. 3) .”
- 7. 7The fast growing GraphDatabase Market Amazon Neptune Azure Cosmos DB ▸ Stardog ▸ Marklogic ▸ AllegroGraph ▸ GraphDB ▸ Oracle Spatial&Graph ▸ Neo4j ▸ ... Property Graph RDF Graph (Triple Stores) Main use case Traverse a graph Query a graph Typical applications Path Analytics, Social Network Analysis Data Integration, Knowledge Representation Standards No standards → Gremlin, Cypher, PGQL, ... W3C Semantic Web standards → SPARQL 1.1 Additional options Shortest path calculations Inferencing
- 8. Core Principles Things -not Strings, Semantic Layer, Linked Data Life Cycle
- 9. “Things but notStrings”: Semantic Knowledge Graphs manage resources, not just terms http://www.my.com/ taxonomy/62346723 prefLabel Retina image http://www.my.com/ images/90546089 http://www.my.com/ taxonomy/ 97345854 prefLabel Funduscope altLabel Ophthalmoscope http://www.mycom.com /taxonomy/4543567 prefLabel Diagnostic Equipment has broader
- 10. Core Principle The Semantic Layercompletes the Four-layered Data & Content Architecture 10 (= Enterprise KG)
- 11. Knowledge Graphs as input forMachine Learning 11 Unstructured Data Structured Data Other Domain- Specific Graphs Machine Learning Enterprise Knowledge Graph Cognitive Applications
- 12. Use Cases for (Enterprise) KnowledgeGraphs Semantic Layer, Linked Data Life Cycle
- 13. 13Five Generic Use Casesfor Graphs 1. dealing with hierarchical or highly connected datasets 2. entity-centric views (in contrast to document-centric views) 3. exploring the connections between the entities of a graph 4. integrating heterogeneous data sources (structured & unstructured, “schema-late” approach) 5. federated (unified) views across multiple data silos within the enterprise
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- 15. 15Example: Citizen portal healthdirect.gov.au/ As acitizen I want to receive guidance to find reliable health information, including ● articles from trusted sources ● information about drugs and medicines ● medical services ● guidance along symptoms Trusted health information Australian Health Thesaurus DrugBank → Linking Structured Data and Documents to Industry Knowledge Graphs Australian Register of Therapeutic Goods
- 16. 16Example: HR Analytics As anHR manager, for upcoming training programmes, I want to identify employees who ● have a certain skill set ● have a specific degree ● have skills that are increasingly important on the labour market ● fall into a specific salary range Employee database Resumes Labour market statistics → Linking Structured to Unstructured Data
- 17. How it works 17 Employee database Resumes Labourmarket statistics PoolParty UnifiedViews RDF Graph Database PoolParty GraphSearch PoolParty Thesaurus Server PoolParty User Now I can identify employees along many dimensions.
- 18. 18Example: Research in Life Sciences Asa researcher in pharmaceutical industry, I want to plan new experiments more efficiently. I want to know what’s already available. I’m interested in former experiments where ● certain genes were tested ● under specific treatment conditions ● in a target therapeutic area ● with help from categorisation systems like ‘disease hierarchies’ UniProt, ChEMBL Experiments Documentation MeSH DrugBank → Linking Structured to Unstructured Data and to Industry Knowledge Graphs
- 19. 19Making Use of Knowledge Graphs →Knowledge Graphs serve as means to enrich unstructured information to provide a rich set of additional access points to document repositories Experiments Document Store
- 20. 20Making Use of Automated Reasoningbased on Knowledge Graphs
- 21. How to builda Knowledge Graph? Linked Data Life Cycle & Anatomy of an Enterprise Knowledge Graph
- 22. Things and URIs Venice Peggy Guggenheim Museum St.Mark’s Square http://my.com/1 http://my.com/2 http://my.com/3
- 23. Labels and basicrelations: Taxonomies and Thesauri prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Peggy Guggenheim Museum prefLabel Piazza altLabel Town Square related related prefLabel broader http://my.com/1 http://my.com/2 http://my.com/3 http://my.com/4
- 24. Classes, specific relations,restrictions: Ontologies and Custom Schemas prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day opening Hours image http://schema.org/containedInPlace prefLabel Piazza altLabel Town Square Peggy Guggenheim Museum prefLabel containedInPlace containedInPlace broader
- 25. Metadata and Graphannotations prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day opening Hours image http://schema.org/containedInPlace prefLabel Piazza altLabel Town Square Peggy Guggenheim Museum prefLabel containedInPlace containedInPlace CC BY-SA 3.0 broader
- 26. Entity linking andschema mappings: Links to other graphs prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day opening Hours image http://schema.org/containedInPlace prefLabel Piazza altLabel Town Square Peggy Guggenheim Museum prefLabel CC BY-SA 3.0 broader containedInPlace containedInPlace
- 27. Linking to dataand documents stored in other systems prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day opening Hours image http://schema.org/containedInPlace prefLabel Piazza altLabel Town Square broader Peggy Guggenheim Museum prefLabel containedInPlace CC BY-SA 3.0 The Peggy Guggenheim Collection is a modern art museum on the Grand Canal in the Dorsoduro sestiere of Venice, Italy. containedInPlace
- 28. Linked Data Life Cycle Howto build Enterprise Knowledge Graphs? 28
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- 30. PoolParty supports Knowledge Graph Building along thewhole Life Cycle 30 ▸ UnifiedViews ▸ Extractor ▸ Thesaurus Server ▸ GraphEditor ▸ GraphEditor ▸ UnifiedViews ▸ UnifiedViews ▸ Extractor ▸ Thesaurus Server ▸ Extractor ▸ UnifiedViews ▸ Semantic Classifier ▸ UnifiedViews ▸ Thesaurus Server ▸ API ▸ GraphSearch ▸ 3rd party
- 31. What’s new in PoolParty7.0? Some Highlights and new Features
- 32. PoolParty GraphEditor 32 ▸ create ontology-drivencustom editors to work with graph data ▸ use multiple graphs to create integrated views on graph data ▸ import and export of RDF graphs ▸ benefit from assisted search over graph data ▸ benefit from assisted bulk editing of RDF graphs ▸ administrate graphs based on user-friendly inline editing ▸ generate SPARQL queries based on an assistant
- 33. PoolParty Notifications 33 ▸ stay informedon changes in your project ▸ configure multiple notification settings per project ▸ get notifications via webhooks ▸ connect APIs to consume notifications
- 34. Improved Ontology Management 34 ▸ access yourontologies via a tree view ▸ apply multilingual labels to your classes, attributes and relations ▸ define user group based access rights on your ontologies and custom schemes
- 35. Improved User Management 35 ▸ accessusers/ roles/ groups via a tree view ▸ an action-based role management has been implemented ▸ define project-based roles per user
- 36. Integration of NER basedon Machine Learning ▸ With 7.0, named entities can be extracted by using the concept extract service (extract call). ▸ This complements PoolParty’s vocabulary-based entity extraction method ▸ Two methods are now supported by default ▹ Maximum Entropy classification: person, location, organisation (more specific classifiers can be added programmatically) ▹ Rule-based recognition by using regex expressions 36
- 37.
- 38. PoolParty UnifiedViews New DPUs ▸ DBpediabatch linking ▸ RML mapping ▸ R2RML mapping ▸ JSON to XML transformation ▸ XML to JSON transformation ▸ ML-based extraction (max entropy) ▸ Thomson Reuters Open Calais extraction ▸ GraphSearch content preparator ▸ Ontology based tabular data mapping ▸ Data fusion ▸ Multi-threaded concept extraction 38
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- 41. Get started Start yourown Knowledge Graph project!
- 42. Maturity Model Roadmap fora more agile Data Governance Framework 42
- 43.
- 44. Next steps 44 ▸Webinar: PoolParty 7.0 (Technical) ▸ Semantic AI - White paper ▸ Test Account ▸ PoolParty Academy
- 45. CONNECT Andreas Blumauer CEO, SemanticWeb Company GmbH Director, PoolParty Software Ltd ▸ andreas.blumauer@semantic-web.com ▸ http://linkedin.com/in/andreasblumauer ▸ https://twitter.com/semwebcompany 45 © Semantic Web Company - http://www.semantic-web.at/ and http://www.poolparty.biz/