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Introduction to Knowledge Graphs and Semantic AI

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Semantic Web Company

The document discusses the significance of knowledge graphs in managing enterprise information and enhancing AI capabilities. It highlights the challenges in data management and how knowledge graphs can integrate structured and unstructured data, improve user intent recognition, and support various applications like semantic search and data analytics. The content outlines the perspectives of knowledge engineers, data architects, and engineers on the structure and utility of knowledge graphs, along with use cases across different industries.

Data & Analytics
Related topics:
text-mining • Data Mining Insights • Data Management Insights • Data Quality Insights • Data Visualization Techniques • Knowledge Graph Insights • Semantic Web
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Andreas Blumauer CEO of Semantic Web Company Introduction to Knowledge Graphs When data learn to think outside the box
© Semantic Web Company 2019 2 Semantic Web Company Founder & CEO of Andreas Blumauer active at developer & vendor of based on part of standard for standard for graduates PoolParty Software Ltd Director of parent company of London located 2004 founded headquartered >200 serves customers Vienna CMS/DMS/DA M/.. 7.0version Graph database integrates with awarded by Gartner KMWorld Search engine Introduction manages Taxonomies Ontologies Text Mining used for Knowledge Graphs part of based on
© Semantic Web Company 2019 WHAT’S THE PROBLEM? Many Challenges in (Enterprise) Information Management & AI Why Knowledge Graphs? 33
© Semantic Web Company 2019 “Search” is still about documents only 4 … and in enterprises it’s painful
© Semantic Web Company 2019 Different shades of metadata 5
© Semantic Web Company 2019 User-agnostic and context-free data models 6 Do machines understand user intent? Do they have enough context? Which Sport-utility vehicle from France provides enough space for my family with 3 kids? The Kia Sportage is our sporty SUV packed with smart features The Peugeot 5008 breaks new ground as a large SUV with many features. Car Loadspace Max no. of seats KIA Sorento 550 litres 7 Peugeot 5008 823 litres 7 BMW X3 550 litres 5 1. Intent recognition 2. Entity linking 3. Background knowledge French SUV
© Semantic Web Company 2019 Artificial “Intelligence” 7 Perth Australia Perth is one of the most isolated major cities in the world, with a population of 2,022,044 living in Greater Perth. Australia is a member of the OECD, United Nations, G20, ANZUS, and the World Trade Organisation. Country City is a is a is located in Avoid illogical answers:distance between Commonwealth of Nations International Organisation is part of is a Support complex Q&A: Which cities located in the Commonwealth of Nations have a population of more than 2 mio. people? Background knowledge is key
© Semantic Web Company 2019 8 Machine Learning per Data Silo Lack of AI Strategy Monte Carlo TS Deep Learning Deep Learning Genetic Algorithms Neuronal networks Case based reasoning
© Semantic Web Company 2019 Desktop Data Integration 9 Who is CEO of a Bank, headquartered in Europe that generates revenue per employee higher than 400,000 Euro? Head- quarter Ticker Symbol Revenue Credit Suisse Zurich VTX: CSGN CHF 23.4b HSBC London LON: HSBA USD 60.0b Allianz Munich ETR: ALV EUR 122.3b Deutsche Bank Frankfurt ETR: DBK EUR 33.5b <Employees> <VTX:CSGN> 48,200 </VTX:CSGN> <LON:HSBA> 266,273 </LON:HSBA> <ETR: ALV> 147,425 </ETR: ALV> <ETR: DBK> 101,104 </ETR: DBK> </Employees> The task awaiting Tidjane Thiam when he takes over from Brady Dougan as the new chief executive at Credit Suisse Group AG is clear: how to pull the Swiss bank out of a post-financial crisis rut. Knowledge workers spend a large part of their working time on ad hoc data integration tasks, the so-called "research". Which parts of it could be automated?
© Semantic Web Company 2019 Many challenges in data management 10 Head- quarter Ticker Symbol Umsatz Credit Suisse Zürich VTX: CSGN CHF 23.4b HSBC London LON: HSBA USD 60.0b Allianz München ETR: ALV EUR 122.3b Deutsche Bank Frankfurt ETR: DBK EUR 33.5b <Employees> <VTX:CSGN> 48,200 </VTX:CSGN> <LON:HSBA> 266,273 </LON:HSBA> <ETR: ALV> 147,425 </ETR: ALV> <ETR: DBK> 101,104 </ETR: DBK> </Employees> The task awaiting Tidjane Thiam when he takes over from Brady Dougan as the new chief executive at Credit Suisse Group AG is clear: how to pull the Swiss bank out of a post-financial crisis rut. ▸ Heterogeneous data ▸ Implicit semantics ▸ No background knowledge ▸ Data quality difficult to measure ▸ Proprietary schemas ▸ Unstructured data ▸ Ambiguity ▸ Multilinguality ▸ Various units and currencies → From Search to QA engines
© Semantic Web Company 2019 1111 Free Universal Construction Kit (Golan Levin et al) The Free Universal Construction Kit is a matrix of adapter bricks that enable complete interoperability between ten popular children's construction toys.
‘Interoperability’ begins with people 12 Bringing various types of mindsets together Data-centric people Knowledge-centric people Service-oriented industries Asset-oriented industries Databases & Excel Content & documents Structured data Unstructured data Linked Data Knowledge Graph Algorithms Collaborative knowledge management Predictability and automation Product innovation and risk mitigation Ontologies and Machine Learning Taxonomies and Collaboration “Actionable Data!” “Better Decisions!” Understand the customer and market place better Stay or become the expert in the field
© Semantic Web Company 2019 What is a Knowledge Graph? From a Knowledge Engineer’s perspective A Knowledge Graph is a model of a knowledge domain created by subject-matter experts with the help of intelligent machine learning algorithms. From a Data Architect’s perspective Structured as an additional virtual data layer, the KG lies on top of existing databases or data sets to link all your data together at scale – be it structured or unstructured. From a Data Engineer’s perspective It provides a structure and common interface for all of your data and enables the creation of smart multilateral relations throughout your databases. 13
Other Graphs than Knowledge Graphs 14 Mind the gap! Social Graphs / Networks Wordnets
© Semantic Web Company 2019 Which Graph Model? 15 RDF versus Property Graphs → Converging approaches Stardog Oracle Spatial & Graph ▸ Amazon Neptune ▸ Marklogic ▸ AllegroGraph ▸ GraphDB ▸ Azure Cosmos DB ▸ 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 (Example) Shortest path calculations Inferencing
© Semantic Web Company 2019 WHO USES KGs? An overview over typical KG use cases Sounds interesting? Gimme some examples! 16
© Semantic Web Company 2019 Can you see the Knowledge Graph? 17 Taobao (Alibaba) PoolParty GraphViews
© Semantic Web Company 2019 Google Knowledge Graph 18
19 Knowledge Graph Adoption Source: Collapsing the IT Stack: Clearing a path for AI adoption Alan Morrison (Sr. Research Fellow at PwC)
Knowledge Graphs for Information Retrieval 20 Semantic tagging, query expansion, faceted search, classification, similarity-based recommender Semantic Search Index Now I can find all the documents in one place, and get assistance with it. Metadata harmonization Semantic tagging Entity linking
© Semantic Web Company 2019 21 Example healthdirect Australia https://www.healthdirect.gov.au/
© Semantic Web Company 2019 22 Semantic Search PowerTagging and PowerSearch for SharePoint
© Semantic Web Company 2019 Use Case Research in Life Sciences 23 As a 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
© Semantic Web Company 2019 Reusing Graphs 24 Gartner - Hype Cycle for AI (2018): “Knowledge Graphs serve as means to enrich unstructured information to provide a rich set of additional access points to document repositories” Experiments Knowledge Graphs used as a Semantic Search Index
Knowledge Graphs for Data Integration & Analytics 25 Metadata enrichment, linked data, text mining, entity-centric search, agile reporting Employee database Resumes Labour market statistics RDF Graph Database PoolParty GraphSearch Now I can identify employees along many dimensions. Enterprise Knowledge Graph
© Semantic Web Company 2019 26 Entity-centric Search & Analytics HR Analytics Dashboard
Knowledge Graphs for E-commerce/Content Mgmt. Boots Winter Water proof Leather Techlite Multi-Grip Super warm Multi-Heat Comfortable 27 Multi-Tech Neoprene Rain My latest Campaigns Rainy Summer Warm Winter Muddy Spring Mud Rules engine Personalization Personalization, recommender / Dynamic menus, campaigns / Translating jargon into client’s needs
© Semantic Web Company 2019 28 Product Recommender Wine and Cheese Harmonizer
© Semantic Web Company 2019 29 Example Electronic Arts Semantics at Play: Electronic Arts' Linked Data Journey
Knowledge Graphs for Virtual Assistants 30 Integrating Semantics into Dialog Workflows My uncle lives in the same household. Darf ich seine Betreuungskosten absetzen? Ange- hörige same household haushalts- zugehöriger Angehöriger uncle teyze ortak ev
Knowledge Graphs for KM Systems 31 Personalization, recommender, matchmaking, push services, smart assistants
© Semantic Web Company 2019 32 Use Case Semantic Matchmaking Event Advisor at SEMANTiCS
© Semantic Web Company 2019 Use Case Recommender System 33 Connecting ▸ content to content, ▸ people to content, ▸ people to people. Architecture Mediterranean Sea Wine Tasting Languedoc-Roussillon Languedoc is a significant producer of wine, and a major contributor to the surplus known as the "wine lake". Today it produces more than a third of the grapes in France, and is a focus for outside investors. The region contains the historic cities of Carcassonne, Toulouse, Montpellier, countless Roman monuments, medieval abbeys, Romanesque churches, and old castles.Occitanie, vineyard, sea, church, village I am interested in ... PowerTagging Thesaurus Server
© Semantic Web Company 2019 34 Example EIP on Water At the center of the marketplace is the matchmaking function https://www.eip-water.eu/ John Smith John SmithJohn Smith John Smith John Peter Smith
© Semantic Web Company 2019 35 Keynote How semantic technologies can support us in making better business decisions https://2019.semantics.cc/keynote-iii-0
Deep Text Analytics 36 Annotation, Extraction, Classification, Rules ▸ Corpus statistics / Word embeddings → Keyphrase extraction ▸ Graph-based annotation → Entity/Concept linking ▸ Corpus Statistics embedded in graphs → Shadow Concepts ▸ Machine-learning-based annotation → Named entity recognition (NER) ▸ Machine-learning based classification → Document Classification ▸ Annotation based on rules → Regular expressions Bain Capital is a venture capital company based in Boston, MA. Since inception it has invested in hundreds of companies. In 2018, Bain had $75b AUM. Graph-based Entity Linking ML-based Entity Recognition Regular Expressions-based Annotation Semantic Rules Engine Give me all paragraphs in documents about “US based Private Equity firms with AUM higher than $20B”
© Semantic Web Company 2019 37 Use Case Contract Intelligence Semantic analysis and pre-selection of relevant clauses in contracts TheCompany 12 Termination by TheCompany for
© Semantic Web Company 2019 38 Semantic AI Knowledge Graphs: The Third Era of Computing (Dan McCreary) Towards an Explainable AI. Fusing Knowledge Graphs, Machine Learning and NLP
© Semantic Web Company 2019 39 Fusing Symbolic & Statistical AI Artificial Intelligence (AI) Artificial Neural Network (ANN) Symbolic AI (GOFAI*) Sub-Symbolic AI Statistical AI Knowledge graphs & reasoning Natural Language Processing (NLP) Machine Learning * Good old-fashioned AI Word Embedding (Word2Vec) Deep Learning (DNN) Natural Language Understanding Entity Recognition & Linking Knowledge Extraction Semantic enhanced Text Classification
© Semantic Web Company 2019 40 Semantic Classifier ML based on semantically enriched training data PoolParty Semantic Classifier combines machine learning algorithms (SVM, Deep Learning, Naive Bayes, etc.) with Semantic Knowledge Graphs.
© Semantic Web Company 2019 41 Use Case Synthetic data generation Use KGs to generate test data for your ML (which are GDPR compliant)
KG creation based on Machine Learning 42 The Knowledge Engineer’s perspective Taxonomy & Ontology Server Entity Extractor & Semantic Classifier Data Integration & Data Linking Bain Capital is a venture capital company based in Boston, MA. Since inception it has invested in hundreds of companies including AMC Entertainment, Brookstone, and Burger King. The company was co-founded by Mitt Romney. UnifiedViews PoolParty GraphSearch Identify new candidate concepts to be included in a controlled vocabulary RDF Graph Database Factsheet Schema mapping based on ontologies Entity linking based on Knowledge Graph Unstructured Data Semi-structured Data Structured Data Controlled vocabularies as a basis for highly precise knowledge extraction and text classification
© Semantic Web Company 2019 43 Demo QA based on KGs QAnswer is a so called "knowledge (or ontology) based" QA system Which banks are headquartered in Zurich?
Types of solutions based on Knowledge Graphs Process Automation ▸ Enhanced Machine Learning ▸ Text Mining & NLP ▸ Data Classification ▸ Document & Text Validation Agile Data Integration ▸ Linked Data ▸ Integrating heterogeneous data ▸ Entity Linking Customer Experience ▸ Recommender Systems ▸ SEO ▸ Smart Helpdesk Solutions ▸ Virtual assistants/ Q&A engines Information Management ▸ Semantic Content Management ▸ Metadata Management ▸ Masterdata Management Knowledge Management ▸ Graph Analytics ▸ Personalization/Skills Mgmt. ▸ Knowledge Discovery Portals ▸ Semantic Search Knowledge Engineering ▸ Taxonomy Management ▸ Ontology Management ▸ Knowledge Graph Mgmt. ▸ Data Visualization 44 Data Content Knowledge Back-endFront-end
© Semantic Web Company 2019 THINGS, NOT STRINGS From Taxonomies over Ontologies to Knowledge Graphs Anatomy of a Knowledge Graph 4545
Things and URIs 46 This is not yet a Knowledge Graph! Venice St. Mark’s Square Peggy Guggenheim Museum http://my.com/1 http://my.com/2 http://my.com/3 ▸ Learn from text corpora ▸ Co-occurences and word embeddings ▸ Extract entitities via ML ▸ Import CSV/Excel
Labels and basic relations 47 This is the backbone of a Knowledge Graph prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Peggy Guggenheim Museum prefLabel Piazza altLabel Town Squarerelated related prefLabel broader ▸ Import known taxonomies ▸ Harvest from Knowledge Graphs like DBpedia ▸ Ingest from own DBs/XML
Classes and specific relations 48 Ontologies give your knowledge graph an additional "dimensionality" prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Monday through Sunday, all day opening Hours image prefLabel Piazza Peggy Guggenheim Museum prefLabel containedInPlace containedInPlace broader http://schema.org/City http://schema.org/ArtGallery http://schema.org/containedInPlace http://schema.org/TouristAttraction ▸ Reuse existing ontologies ▸ Interlink them with your taxonomies ▸ Map them to your DBs/XML altLabel Town Square
Metadata and Graph annotations 49 Enrich and qualify data in your knowledge graph prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Monday through Sunday, all day image prefLabel Piazza Peggy Guggenheim Museum containedInPlace containedInPlace CC BY-SA 3.0 broader opening Hours http://schema.org/City http://schema.org/ArtGallery http://schema.org/containedInPlace http://schema.org/TouristAttraction ▸ Reuse existing schemas ▸ Make use of SPARQL and reasoning ▸ Make use of constraint languages like SHACL Now open altLabel Town Square
Graph linking 50 Additional facts for your knowledge graph, nearly for free! prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Monday through Sunday, all day image prefLabel Piazza Peggy Guggenheim Museum containedInPlace containedInPlace CC BY-SA 3.0 broader opening Hours http://schema.org/City http://schema.org/ArtGallery http://schema.org/containedInPlace http://schema.org/TouristAttraction ▸ Make use of Named Graphs ▸ Use Machine Learning for automatic linking ▸ Track data provenance Now open altLabel Town Square
The Peggy Guggenheim Collection is a modern art museum on the Grand Canal in the Dorsoduro sestiere of Venice, Italy. Link all your data! 51 Bring structured/unstructured enterprise data into the knowledge graph prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Monday through Sunday, all day image prefLabel Piazza Peggy Guggenheim Museum containedInPlace containedInPlace CC BY-SA 3.0 broader opening Hours http://schema.org/City http://schema.org/ArtGallery http://schema.org/containedInPlace http://schema.org/TouristAttraction Now open altLabel Town Square ▸ Use Text Mining ▸ Based on automatic entity extraction ▸ Use R2RML Schema to Ontology mapping ▸ Make use of ML R2RML
The Peggy Guggenheim Collection is a modern art museum on the Grand Canal in the Dorsoduro sestiere of Venice, Italy. Putting the user into the graph 52 Provide all your data personalized and contextualized prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Monday through Sunday, all day image prefLabel Piazza Peggy Guggenheim Museum containedInPlace containedInPlace CC BY-SA 3.0 broader opening Hours http://schema.org/City http://schema.org/ArtGallery http://schema.org/containedInPlace http://schema.org/TouristAttraction Now open altLabel Town Square ▸ Build recommender systems ▸ Make use of graph analytics ▸ Recommender based on similarity and/or rules likes visits R2RML
© Semantic Web Company 2019 Answers instead of search results 53 Who is CEO of a Bank, headquartered in Europe that generates revenue per employee higher than 400,000 Euro? Head- quarter Ticker Symbol Revenue Credit Suisse Zurich VTX: CSGN CHF 23.4b HSBC London LON: HSBA USD 60.0b Allianz Munich ETR: ALV EUR 122.3b Deutsche Bank Frankfurt ETR: DBK EUR 33.5b <Employees> <VTX:CSGN> 48,200 </VTX:CSGN> <LON:HSBA> 266,273 </LON:HSBA> <ETR: ALV> 147,425 </ETR: ALV> <ETR: DBK> 101,104 </ETR: DBK> </Employees> The task awaiting Tidjane Thiam when he takes over from Brady Dougan as the new chief executive at Credit Suisse Group AG is clear: how to pull the Swiss bank out of a post-financial crisis rut.
The task awaiting Tidjane Thiam when he takes over from Brady Dougan as the new chief executive at Credit Suisse Group AG is clear: how to pull the Swiss bank out of a post-financial crisis rut. KGs: Linking, Mapping, Reasoning Organisation HQ Umsatz CS Zürich CHF 23.4b HSBC London USD 60.0b Allianz München EUR 122.3b Deutsche Bank Frankfurt EUR 33.5b <Employees> <VTX:CSGN>48,200</VTX:CSGN> <LON:HSBA>266,273</LON:HSBA> <ETR: ALV>147,425</ETR: ALV> <ETR: DBK>101,104</ETR: DBK> </Employees> Credit Suisse Bank is a Zurich Who is CEO of a Bank, headquartered in Europe that generates revenue per employee higher than 400,000 Euro? HQ in Financial institution Switzerland part of Insurance company is a is a Europe part of works for CEO is a 54 Tidjane Thiam
The task awaiting Tidjane Thiam when he takes over from Brady Dougan as the new chief executive at Credit Suisse Group AG is clear: how to pull the Swiss bank out of a post-financial crisis rut. Knowledge Graphs support NLU and QA Organisation HQ Umsatz CS Zürich CHF 23.4b HSBC London USD 60.0b Allianz München EUR 122.3b Deutsche Bank Frankfurt EUR 33.5b <Employees> <VTX:CSGN>48,200</VTX:CSGN> <LON:HSBA>266,273</LON:HSBA> <ETR: ALV>147,425</ETR: ALV> <ETR: DBK>101,104</ETR: DBK> </Employees> Credit Suisse Bank is a Zurich Who is CEO of a Bank, headquartered in Europe that generates revenue per employee higher than 400,000 Euro? HQ in Financial institution Switzerland part of is a is a Europe part of works for CEO is a 55 Insurance company Tidjane Thiam
© Semantic Web Company 2019 CHANGE MANAGEMENT How to implement disruptive technologies in organizations? Introducing Semantic AI 5656
© Semantic Web Company 2019 C-Level: Knowledge Graphs in various Hype Cycles 57 Hype Cycle for Artificial Intelligence Hype Cycle for the Digital Workplace Hype Cycle for Emerging Technologies
Linked Data Life Cycle 58 Data Engineers + Subject Matter Experts + Machine Learning working together Manual curation Automated Knowledge Graph Development & Machine Learning Knowledge modelling
© Semantic Web Company 2019 Our approach Semantic AI 59 Natural languages Taxonomies Schemas/Ontologies Statisticalmodels Computational Linguists Taxonomists DataScientists Ontologists
© Semantic Web Company 2019 Bringing two types of mindsets together 60 Data-centric people Knowledge-centric people Service-oriented industries Asset-oriented industries Databases & Excel Content & documents Structured data Unstructured data Linked Data Knowledge Graph Algorithms Collaborative knowledge management Predictability and automation Product innovation and risk mitigation Ontologies and Machine Learning Taxonomies and Collaboration “Actionable Data!” “Better Decisions!” Understand the customer and market place better Stay or become the expert in the field
© Semantic Web Company 2019 Find the right starting points 61
© Semantic Web Company 2019 Knowledge Graph Management 62 Data Governance along the Linked Data Life Cycle
© Semantic Web Company 2019 Semantic Web Starter Kit 63 Learn more about it!
© Semantic Web Company 2019 SEMANTIC AI Build your own applications Make things work 6464
First release in 2009 Fact sheet: PoolParty Semantic Suite Most complete and secure Semantic Middleware on the Global Market Semantic AI: Fusing Graphs, NLP, and Machine Learning W3C standards compliant Named as Sample Vendor in Gartner’s Hype Cycle for AI 2018 Current version 7.0 On-premise or cloud-based Over 200 Named as Representative Vendor in Gartner’s Market Guide for Hosted AI Services 2018 KMWorld listed PoolParty as Trend-Setting Product 2015, 2016, 2017, and 2018 installations world-wide ISO 27001:2013 certified
“PoolParty brings the power of Google into the enterprise environment through Knowledge Graphs.” Knowledge Architect at a Top 3 Oil & Gas company 66
How does it work? 67 The Data Engineer’s perspective UnifiedViews PoolParty GraphSearch Knowledge Graph Unstructured Data Semi-structured Data Structured Data ▸ Entity linking ▸ Schema mapping ▸ Data transformation ▸ Data validation ▸ Data cleansing ▸ Metadata & Semantic enrichment Database Semantic AI Application
© Semantic Web Company 2019 Create unified views of your data! 68
© Semantic Web Company 2019 Example: HR Analytics 69 Demo As an HR 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
© Semantic Web Company 2019 How it works 70 Employee database Resumes Labour market statistics PoolParty UnifiedViews RDF Graph Database PoolParty GraphSearch PoolParty Thesaurus Server PoolParty User Now I can identify employees along many dimensions.
© Semantic Web Company 2019 Components and Features 71
© Semantic Web Company 2019 PoolParty Components 72 PoolParty Taxonomy & Ontology Server PoolParty Extractor PoolParty GraphEditor PoolParty UnifiedViews PoolParty Semantic Classifier PoolParty GraphSearch
SMEs benefiting from Corpus Learning 73 The Knowledge Engineer’s perspective Taxonomy & Ontology Server Entity Extractor & Semantic Classifier Data Integration & Data Linking Bain Capital is a venture capital company based in Boston, MA. Since inception it has invested in hundreds of companies including AMC Entertainment, Brookstone, and Burger King. The company was co-founded by Mitt Romney. UnifiedViews PoolParty GraphSearch Identify new candidate concepts to be included in a controlled vocabulary RDF Graph Database Factsheet Schema mapping based on ontologies Entity linking based on Knowledge Graph Unstructured Data Semi-structured Data Structured Data Controlled vocabularies as a basis for highly precise knowledge extraction and text classification
© Semantic Web Company 2019 TECHNICAL DEEP DIVE Make your data actionable! Working with Knowledge Graphs 7474
© Semantic Web Company 2019 Why use Graph Databases? 75 Dealing with hierarchical and highly connected datasets Entity-centric views (in contrast to document-centric views) Exploring the connection between entities of a graph Integrating heterogeneous data sources (structured & unstructured in a “schema-late” approach)
© Semantic Web Company 2019 What makes a store RDF ready? 76 It offers a SPARQL endpoint to query the database using SPARQL It implements at least one of the RDF ready libraries/frameworks: ▸ Eclipse RDF4J ▸ Apache Jena
© Semantic Web Company 2019 SPARQL 77 SPARQL retrieves and manipulates data stored in an RDF graph database Watch Tutorial from PoolParty Academy
© Semantic Web Company 2019 SHACL 78 Shapes Constraint Language Data Quality and Consistency: Validating graph-based data against a set of conditions
© Semantic Web Company 2019 Example 1 79 Validating Concept labels and hierarchic structure in a SKOS taxonomy Concept labels and hierarchic structure Constraints: A concept has to connect to a concept scheme. The labels have to be disjoint and the language must be unique. Concept Concept Scheme skos:hasTopConcept / skos:narrower skos:prefLabel label1@en skos:altLabel label2@en
© Semantic Web Company 2019 Example 2 80 Compliance checks Maximum active board members Constraint: There must not be more than two active board members for each Legal Entity. Board MemberLegal Entity Active hasBoardMembership hasBoardMemberStatus
© Semantic Web Company 2019 Example 3 81 Data consistency Consistent geographic information Constraint: If a Legal Entity has a country and a city assigned, then both must be related with a skos:narrower path, so that the geo information is consistent. Legal Entity Country City isLocatedInCountry isLocatedInCity skos:narrower
© Semantic Web Company 2019 TAKE AWAYS Make your data actionable! Summarization 8282
Four-layered Information Architecture 83 Semantic AI application Semantic Layer / Knowledge Graph Metadata Layer Content & Data Layer
© Semantic Web Company 2019 From Documents To Entitities 84 Watch out for ambiguity!
© Semantic Web Company 2019 ML based on Semantic AI Engines 85 Bla bla bla bla. Bla bla bla bla The stove is on. The stove is hot! Ontological model → reasoningTaxonomical model → is-a abstractions Bla stove bla bla. Bla bla bla hot Switched on devices are dangerous devices. Switched on devices are dangerous, only if the operating temperature is above 100 degrees and the automatic shutdown mechanism is broken. The stove is on. The stove is hot! Word embeddings/cooccurences → is related The stove is on. The stove is hot! Bla bla bla bla Bla bla bla bla. Separating signal from noise
© Semantic Web Company 2019 86 doc doc doc Jaguar Cat Felidae Carnivore Keystone Species Elephant Keystone Species Wolf Carnivore Rabbit doc Jaguar Elephant Wolf Rabbit doc Carnivore Traditional approach Graph-based approach Show me all documents about Felidae doc doc doc Show me all documents about Carnivores Cat = Felidae Keystone Species Keystone Species? Traditional vs. Graph-based Metadata Management
© Semantic Web Company 2019 87 doc doc doc Jaguar Cat Felidae Carnivore Keystone Species Elephant Keystone Species Wolf Carnivore Rabbit doc Jaguar Elephant Wolf Rabbit doc Carnivore Traditional approach Graph-based approach Show me all documents about Felidae doc doc doc Show me all documents about Carnivores Cat = Felidae Keystone Species Keystone Species? Metadata per document 1. No or little network effects 2. No reuse of metadata 3. Metadata resides in silos 4. Data quality hard to measure 5. Not machine-readable Knowledge about metadata 1. Explicit knowledge models 2. Reusable and measurable 3. Metadata is machine-processable 4. Standards-based metadata 5. Linkable metadata opens silos Traditional vs. Graph-based Metadata Management
Next steps PoolParty Academy Video Tutorials Online Help & Manuals Demo Account 88
© Semantic Web Company 2019 Connect Andreas Blumauer CEO & Managing Partner, Semantic Web Company ▸ andreas.blumauer@semantic-web.com ▸ https://www.linkedin.com/in/andreasblumauer ▸ https://twitter.com/semwebcompany ▸ https://ablvienna.wordpress.com/ 89

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Introduction to Knowledge Graphs and Semantic AI

  • 1. Andreas Blumauer CEO of Semantic Web Company Introduction to Knowledge Graphs When data learn to think outside the box
  • 2. © Semantic Web Company 2019 2 Semantic Web Company Founder & CEO of Andreas Blumauer active at developer & vendor of based on part of standard for standard for graduates PoolParty Software Ltd Director of parent company of London located 2004 founded headquartered >200 serves customers Vienna CMS/DMS/DA M/.. 7.0version Graph database integrates with awarded by Gartner KMWorld Search engine Introduction manages Taxonomies Ontologies Text Mining used for Knowledge Graphs part of based on
  • 3. © Semantic Web Company 2019 WHAT’S THE PROBLEM? Many Challenges in (Enterprise) Information Management & AI Why Knowledge Graphs? 33
  • 4. © Semantic Web Company 2019 “Search” is still about documents only 4 … and in enterprises it’s painful
  • 5. © Semantic Web Company 2019 Different shades of metadata 5
  • 6. © Semantic Web Company 2019 User-agnostic and context-free data models 6 Do machines understand user intent? Do they have enough context? Which Sport-utility vehicle from France provides enough space for my family with 3 kids? The Kia Sportage is our sporty SUV packed with smart features The Peugeot 5008 breaks new ground as a large SUV with many features. Car Loadspace Max no. of seats KIA Sorento 550 litres 7 Peugeot 5008 823 litres 7 BMW X3 550 litres 5 1. Intent recognition 2. Entity linking 3. Background knowledge French SUV
  • 7. © Semantic Web Company 2019 Artificial “Intelligence” 7 Perth Australia Perth is one of the most isolated major cities in the world, with a population of 2,022,044 living in Greater Perth. Australia is a member of the OECD, United Nations, G20, ANZUS, and the World Trade Organisation. Country City is a is a is located in Avoid illogical answers:distance between Commonwealth of Nations International Organisation is part of is a Support complex Q&A: Which cities located in the Commonwealth of Nations have a population of more than 2 mio. people? Background knowledge is key
  • 8. © Semantic Web Company 2019 8 Machine Learning per Data Silo Lack of AI Strategy Monte Carlo TS Deep Learning Deep Learning Genetic Algorithms Neuronal networks Case based reasoning
  • 9. © Semantic Web Company 2019 Desktop Data Integration 9 Who is CEO of a Bank, headquartered in Europe that generates revenue per employee higher than 400,000 Euro? Head- quarter Ticker Symbol Revenue Credit Suisse Zurich VTX: CSGN CHF 23.4b HSBC London LON: HSBA USD 60.0b Allianz Munich ETR: ALV EUR 122.3b Deutsche Bank Frankfurt ETR: DBK EUR 33.5b <Employees> <VTX:CSGN> 48,200 </VTX:CSGN> <LON:HSBA> 266,273 </LON:HSBA> <ETR: ALV> 147,425 </ETR: ALV> <ETR: DBK> 101,104 </ETR: DBK> </Employees> The task awaiting Tidjane Thiam when he takes over from Brady Dougan as the new chief executive at Credit Suisse Group AG is clear: how to pull the Swiss bank out of a post-financial crisis rut. Knowledge workers spend a large part of their working time on ad hoc data integration tasks, the so-called "research". Which parts of it could be automated?
  • 10. © Semantic Web Company 2019 Many challenges in data management 10 Head- quarter Ticker Symbol Umsatz Credit Suisse Zürich VTX: CSGN CHF 23.4b HSBC London LON: HSBA USD 60.0b Allianz München ETR: ALV EUR 122.3b Deutsche Bank Frankfurt ETR: DBK EUR 33.5b <Employees> <VTX:CSGN> 48,200 </VTX:CSGN> <LON:HSBA> 266,273 </LON:HSBA> <ETR: ALV> 147,425 </ETR: ALV> <ETR: DBK> 101,104 </ETR: DBK> </Employees> The task awaiting Tidjane Thiam when he takes over from Brady Dougan as the new chief executive at Credit Suisse Group AG is clear: how to pull the Swiss bank out of a post-financial crisis rut. ▸ Heterogeneous data ▸ Implicit semantics ▸ No background knowledge ▸ Data quality difficult to measure ▸ Proprietary schemas ▸ Unstructured data ▸ Ambiguity ▸ Multilinguality ▸ Various units and currencies → From Search to QA engines
  • 11. © Semantic Web Company 2019 1111 Free Universal Construction Kit (Golan Levin et al) The Free Universal Construction Kit is a matrix of adapter bricks that enable complete interoperability between ten popular children's construction toys.
  • 12. ‘Interoperability’ begins with people 12 Bringing various types of mindsets together Data-centric people Knowledge-centric people Service-oriented industries Asset-oriented industries Databases & Excel Content & documents Structured data Unstructured data Linked Data Knowledge Graph Algorithms Collaborative knowledge management Predictability and automation Product innovation and risk mitigation Ontologies and Machine Learning Taxonomies and Collaboration “Actionable Data!” “Better Decisions!” Understand the customer and market place better Stay or become the expert in the field
  • 13. © Semantic Web Company 2019 What is a Knowledge Graph? From a Knowledge Engineer’s perspective A Knowledge Graph is a model of a knowledge domain created by subject-matter experts with the help of intelligent machine learning algorithms. From a Data Architect’s perspective Structured as an additional virtual data layer, the KG lies on top of existing databases or data sets to link all your data together at scale – be it structured or unstructured. From a Data Engineer’s perspective It provides a structure and common interface for all of your data and enables the creation of smart multilateral relations throughout your databases. 13
  • 14. Other Graphs than Knowledge Graphs 14 Mind the gap! Social Graphs / Networks Wordnets
  • 15. © Semantic Web Company 2019 Which Graph Model? 15 RDF versus Property Graphs → Converging approaches Stardog Oracle Spatial & Graph ▸ Amazon Neptune ▸ Marklogic ▸ AllegroGraph ▸ GraphDB ▸ Azure Cosmos DB ▸ 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 (Example) Shortest path calculations Inferencing
  • 16. © Semantic Web Company 2019 WHO USES KGs? An overview over typical KG use cases Sounds interesting? Gimme some examples! 16
  • 17. © Semantic Web Company 2019 Can you see the Knowledge Graph? 17 Taobao (Alibaba) PoolParty GraphViews
  • 18. © Semantic Web Company 2019 Google Knowledge Graph 18
  • 19. 19 Knowledge Graph Adoption Source: Collapsing the IT Stack: Clearing a path for AI adoption Alan Morrison (Sr. Research Fellow at PwC)
  • 20. Knowledge Graphs for Information Retrieval 20 Semantic tagging, query expansion, faceted search, classification, similarity-based recommender Semantic Search Index Now I can find all the documents in one place, and get assistance with it. Metadata harmonization Semantic tagging Entity linking
  • 21. © Semantic Web Company 2019 21 Example healthdirect Australia https://www.healthdirect.gov.au/
  • 22. © Semantic Web Company 2019 22 Semantic Search PowerTagging and PowerSearch for SharePoint
  • 23. © Semantic Web Company 2019 Use Case Research in Life Sciences 23 As a 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
  • 24. © Semantic Web Company 2019 Reusing Graphs 24 Gartner - Hype Cycle for AI (2018): “Knowledge Graphs serve as means to enrich unstructured information to provide a rich set of additional access points to document repositories” Experiments Knowledge Graphs used as a Semantic Search Index
  • 25. Knowledge Graphs for Data Integration & Analytics 25 Metadata enrichment, linked data, text mining, entity-centric search, agile reporting Employee database Resumes Labour market statistics RDF Graph Database PoolParty GraphSearch Now I can identify employees along many dimensions. Enterprise Knowledge Graph
  • 26. © Semantic Web Company 2019 26 Entity-centric Search & Analytics HR Analytics Dashboard
  • 27. Knowledge Graphs for E-commerce/Content Mgmt. Boots Winter Water proof Leather Techlite Multi-Grip Super warm Multi-Heat Comfortable 27 Multi-Tech Neoprene Rain My latest Campaigns Rainy Summer Warm Winter Muddy Spring Mud Rules engine Personalization Personalization, recommender / Dynamic menus, campaigns / Translating jargon into client’s needs
  • 28. © Semantic Web Company 2019 28 Product Recommender Wine and Cheese Harmonizer
  • 29. © Semantic Web Company 2019 29 Example Electronic Arts Semantics at Play: Electronic Arts' Linked Data Journey
  • 30. Knowledge Graphs for Virtual Assistants 30 Integrating Semantics into Dialog Workflows My uncle lives in the same household. Darf ich seine Betreuungskosten absetzen? Ange- hörige same household haushalts- zugehöriger Angehöriger uncle teyze ortak ev
  • 31. Knowledge Graphs for KM Systems 31 Personalization, recommender, matchmaking, push services, smart assistants
  • 32. © Semantic Web Company 2019 32 Use Case Semantic Matchmaking Event Advisor at SEMANTiCS
  • 33. © Semantic Web Company 2019 Use Case Recommender System 33 Connecting ▸ content to content, ▸ people to content, ▸ people to people. Architecture Mediterranean Sea Wine Tasting Languedoc-Roussillon Languedoc is a significant producer of wine, and a major contributor to the surplus known as the "wine lake". Today it produces more than a third of the grapes in France, and is a focus for outside investors. The region contains the historic cities of Carcassonne, Toulouse, Montpellier, countless Roman monuments, medieval abbeys, Romanesque churches, and old castles.Occitanie, vineyard, sea, church, village I am interested in ... PowerTagging Thesaurus Server
  • 34. © Semantic Web Company 2019 34 Example EIP on Water At the center of the marketplace is the matchmaking function https://www.eip-water.eu/ John Smith John SmithJohn Smith John Smith John Peter Smith
  • 35. © Semantic Web Company 2019 35 Keynote How semantic technologies can support us in making better business decisions https://2019.semantics.cc/keynote-iii-0
  • 36. Deep Text Analytics 36 Annotation, Extraction, Classification, Rules ▸ Corpus statistics / Word embeddings → Keyphrase extraction ▸ Graph-based annotation → Entity/Concept linking ▸ Corpus Statistics embedded in graphs → Shadow Concepts ▸ Machine-learning-based annotation → Named entity recognition (NER) ▸ Machine-learning based classification → Document Classification ▸ Annotation based on rules → Regular expressions Bain Capital is a venture capital company based in Boston, MA. Since inception it has invested in hundreds of companies. In 2018, Bain had $75b AUM. Graph-based Entity Linking ML-based Entity Recognition Regular Expressions-based Annotation Semantic Rules Engine Give me all paragraphs in documents about “US based Private Equity firms with AUM higher than $20B”
  • 37. © Semantic Web Company 2019 37 Use Case Contract Intelligence Semantic analysis and pre-selection of relevant clauses in contracts TheCompany 12 Termination by TheCompany for
  • 38. © Semantic Web Company 2019 38 Semantic AI Knowledge Graphs: The Third Era of Computing (Dan McCreary) Towards an Explainable AI. Fusing Knowledge Graphs, Machine Learning and NLP
  • 39. © Semantic Web Company 2019 39 Fusing Symbolic & Statistical AI Artificial Intelligence (AI) Artificial Neural Network (ANN) Symbolic AI (GOFAI*) Sub-Symbolic AI Statistical AI Knowledge graphs & reasoning Natural Language Processing (NLP) Machine Learning * Good old-fashioned AI Word Embedding (Word2Vec) Deep Learning (DNN) Natural Language Understanding Entity Recognition & Linking Knowledge Extraction Semantic enhanced Text Classification
  • 40. © Semantic Web Company 2019 40 Semantic Classifier ML based on semantically enriched training data PoolParty Semantic Classifier combines machine learning algorithms (SVM, Deep Learning, Naive Bayes, etc.) with Semantic Knowledge Graphs.
  • 41. © Semantic Web Company 2019 41 Use Case Synthetic data generation Use KGs to generate test data for your ML (which are GDPR compliant)
  • 42. KG creation based on Machine Learning 42 The Knowledge Engineer’s perspective Taxonomy & Ontology Server Entity Extractor & Semantic Classifier Data Integration & Data Linking Bain Capital is a venture capital company based in Boston, MA. Since inception it has invested in hundreds of companies including AMC Entertainment, Brookstone, and Burger King. The company was co-founded by Mitt Romney. UnifiedViews PoolParty GraphSearch Identify new candidate concepts to be included in a controlled vocabulary RDF Graph Database Factsheet Schema mapping based on ontologies Entity linking based on Knowledge Graph Unstructured Data Semi-structured Data Structured Data Controlled vocabularies as a basis for highly precise knowledge extraction and text classification
  • 43. © Semantic Web Company 2019 43 Demo QA based on KGs QAnswer is a so called "knowledge (or ontology) based" QA system Which banks are headquartered in Zurich?
  • 44. Types of solutions based on Knowledge Graphs Process Automation ▸ Enhanced Machine Learning ▸ Text Mining & NLP ▸ Data Classification ▸ Document & Text Validation Agile Data Integration ▸ Linked Data ▸ Integrating heterogeneous data ▸ Entity Linking Customer Experience ▸ Recommender Systems ▸ SEO ▸ Smart Helpdesk Solutions ▸ Virtual assistants/ Q&A engines Information Management ▸ Semantic Content Management ▸ Metadata Management ▸ Masterdata Management Knowledge Management ▸ Graph Analytics ▸ Personalization/Skills Mgmt. ▸ Knowledge Discovery Portals ▸ Semantic Search Knowledge Engineering ▸ Taxonomy Management ▸ Ontology Management ▸ Knowledge Graph Mgmt. ▸ Data Visualization 44 Data Content Knowledge Back-endFront-end
  • 45. © Semantic Web Company 2019 THINGS, NOT STRINGS From Taxonomies over Ontologies to Knowledge Graphs Anatomy of a Knowledge Graph 4545
  • 46. Things and URIs 46 This is not yet a Knowledge Graph! Venice St. Mark’s Square Peggy Guggenheim Museum http://my.com/1 http://my.com/2 http://my.com/3 ▸ Learn from text corpora ▸ Co-occurences and word embeddings ▸ Extract entitities via ML ▸ Import CSV/Excel
  • 47. Labels and basic relations 47 This is the backbone of a Knowledge Graph prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Peggy Guggenheim Museum prefLabel Piazza altLabel Town Squarerelated related prefLabel broader ▸ Import known taxonomies ▸ Harvest from Knowledge Graphs like DBpedia ▸ Ingest from own DBs/XML
  • 48. Classes and specific relations 48 Ontologies give your knowledge graph an additional "dimensionality" prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Monday through Sunday, all day opening Hours image prefLabel Piazza Peggy Guggenheim Museum prefLabel containedInPlace containedInPlace broader http://schema.org/City http://schema.org/ArtGallery http://schema.org/containedInPlace http://schema.org/TouristAttraction ▸ Reuse existing ontologies ▸ Interlink them with your taxonomies ▸ Map them to your DBs/XML altLabel Town Square
  • 49. Metadata and Graph annotations 49 Enrich and qualify data in your knowledge graph prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Monday through Sunday, all day image prefLabel Piazza Peggy Guggenheim Museum containedInPlace containedInPlace CC BY-SA 3.0 broader opening Hours http://schema.org/City http://schema.org/ArtGallery http://schema.org/containedInPlace http://schema.org/TouristAttraction ▸ Reuse existing schemas ▸ Make use of SPARQL and reasoning ▸ Make use of constraint languages like SHACL Now open altLabel Town Square
  • 50. Graph linking 50 Additional facts for your knowledge graph, nearly for free! prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Monday through Sunday, all day image prefLabel Piazza Peggy Guggenheim Museum containedInPlace containedInPlace CC BY-SA 3.0 broader opening Hours http://schema.org/City http://schema.org/ArtGallery http://schema.org/containedInPlace http://schema.org/TouristAttraction ▸ Make use of Named Graphs ▸ Use Machine Learning for automatic linking ▸ Track data provenance Now open altLabel Town Square
  • 51. The Peggy Guggenheim Collection is a modern art museum on the Grand Canal in the Dorsoduro sestiere of Venice, Italy. Link all your data! 51 Bring structured/unstructured enterprise data into the knowledge graph prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Monday through Sunday, all day image prefLabel Piazza Peggy Guggenheim Museum containedInPlace containedInPlace CC BY-SA 3.0 broader opening Hours http://schema.org/City http://schema.org/ArtGallery http://schema.org/containedInPlace http://schema.org/TouristAttraction Now open altLabel Town Square ▸ Use Text Mining ▸ Based on automatic entity extraction ▸ Use R2RML Schema to Ontology mapping ▸ Make use of ML R2RML
  • 52. The Peggy Guggenheim Collection is a modern art museum on the Grand Canal in the Dorsoduro sestiere of Venice, Italy. Putting the user into the graph 52 Provide all your data personalized and contextualized prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Monday through Sunday, all day image prefLabel Piazza Peggy Guggenheim Museum containedInPlace containedInPlace CC BY-SA 3.0 broader opening Hours http://schema.org/City http://schema.org/ArtGallery http://schema.org/containedInPlace http://schema.org/TouristAttraction Now open altLabel Town Square ▸ Build recommender systems ▸ Make use of graph analytics ▸ Recommender based on similarity and/or rules likes visits R2RML
  • 53. © Semantic Web Company 2019 Answers instead of search results 53 Who is CEO of a Bank, headquartered in Europe that generates revenue per employee higher than 400,000 Euro? Head- quarter Ticker Symbol Revenue Credit Suisse Zurich VTX: CSGN CHF 23.4b HSBC London LON: HSBA USD 60.0b Allianz Munich ETR: ALV EUR 122.3b Deutsche Bank Frankfurt ETR: DBK EUR 33.5b <Employees> <VTX:CSGN> 48,200 </VTX:CSGN> <LON:HSBA> 266,273 </LON:HSBA> <ETR: ALV> 147,425 </ETR: ALV> <ETR: DBK> 101,104 </ETR: DBK> </Employees> The task awaiting Tidjane Thiam when he takes over from Brady Dougan as the new chief executive at Credit Suisse Group AG is clear: how to pull the Swiss bank out of a post-financial crisis rut.
  • 54. The task awaiting Tidjane Thiam when he takes over from Brady Dougan as the new chief executive at Credit Suisse Group AG is clear: how to pull the Swiss bank out of a post-financial crisis rut. KGs: Linking, Mapping, Reasoning Organisation HQ Umsatz CS Zürich CHF 23.4b HSBC London USD 60.0b Allianz München EUR 122.3b Deutsche Bank Frankfurt EUR 33.5b <Employees> <VTX:CSGN>48,200</VTX:CSGN> <LON:HSBA>266,273</LON:HSBA> <ETR: ALV>147,425</ETR: ALV> <ETR: DBK>101,104</ETR: DBK> </Employees> Credit Suisse Bank is a Zurich Who is CEO of a Bank, headquartered in Europe that generates revenue per employee higher than 400,000 Euro? HQ in Financial institution Switzerland part of Insurance company is a is a Europe part of works for CEO is a 54 Tidjane Thiam
  • 55. The task awaiting Tidjane Thiam when he takes over from Brady Dougan as the new chief executive at Credit Suisse Group AG is clear: how to pull the Swiss bank out of a post-financial crisis rut. Knowledge Graphs support NLU and QA Organisation HQ Umsatz CS Zürich CHF 23.4b HSBC London USD 60.0b Allianz München EUR 122.3b Deutsche Bank Frankfurt EUR 33.5b <Employees> <VTX:CSGN>48,200</VTX:CSGN> <LON:HSBA>266,273</LON:HSBA> <ETR: ALV>147,425</ETR: ALV> <ETR: DBK>101,104</ETR: DBK> </Employees> Credit Suisse Bank is a Zurich Who is CEO of a Bank, headquartered in Europe that generates revenue per employee higher than 400,000 Euro? HQ in Financial institution Switzerland part of is a is a Europe part of works for CEO is a 55 Insurance company Tidjane Thiam
  • 56. © Semantic Web Company 2019 CHANGE MANAGEMENT How to implement disruptive technologies in organizations? Introducing Semantic AI 5656
  • 57. © Semantic Web Company 2019 C-Level: Knowledge Graphs in various Hype Cycles 57 Hype Cycle for Artificial Intelligence Hype Cycle for the Digital Workplace Hype Cycle for Emerging Technologies
  • 58. Linked Data Life Cycle 58 Data Engineers + Subject Matter Experts + Machine Learning working together Manual curation Automated Knowledge Graph Development & Machine Learning Knowledge modelling
  • 59. © Semantic Web Company 2019 Our approach Semantic AI 59 Natural languages Taxonomies Schemas/Ontologies Statisticalmodels Computational Linguists Taxonomists DataScientists Ontologists
  • 60. © Semantic Web Company 2019 Bringing two types of mindsets together 60 Data-centric people Knowledge-centric people Service-oriented industries Asset-oriented industries Databases & Excel Content & documents Structured data Unstructured data Linked Data Knowledge Graph Algorithms Collaborative knowledge management Predictability and automation Product innovation and risk mitigation Ontologies and Machine Learning Taxonomies and Collaboration “Actionable Data!” “Better Decisions!” Understand the customer and market place better Stay or become the expert in the field
  • 61. © Semantic Web Company 2019 Find the right starting points 61
  • 62. © Semantic Web Company 2019 Knowledge Graph Management 62 Data Governance along the Linked Data Life Cycle
  • 63. © Semantic Web Company 2019 Semantic Web Starter Kit 63 Learn more about it!
  • 64. © Semantic Web Company 2019 SEMANTIC AI Build your own applications Make things work 6464
  • 65. First release in 2009 Fact sheet: PoolParty Semantic Suite Most complete and secure Semantic Middleware on the Global Market Semantic AI: Fusing Graphs, NLP, and Machine Learning W3C standards compliant Named as Sample Vendor in Gartner’s Hype Cycle for AI 2018 Current version 7.0 On-premise or cloud-based Over 200 Named as Representative Vendor in Gartner’s Market Guide for Hosted AI Services 2018 KMWorld listed PoolParty as Trend-Setting Product 2015, 2016, 2017, and 2018 installations world-wide ISO 27001:2013 certified
  • 66. “PoolParty brings the power of Google into the enterprise environment through Knowledge Graphs.” Knowledge Architect at a Top 3 Oil & Gas company 66
  • 67. How does it work? 67 The Data Engineer’s perspective UnifiedViews PoolParty GraphSearch Knowledge Graph Unstructured Data Semi-structured Data Structured Data ▸ Entity linking ▸ Schema mapping ▸ Data transformation ▸ Data validation ▸ Data cleansing ▸ Metadata & Semantic enrichment Database Semantic AI Application
  • 68. © Semantic Web Company 2019 Create unified views of your data! 68
  • 69. © Semantic Web Company 2019 Example: HR Analytics 69 Demo As an HR 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
  • 70. © Semantic Web Company 2019 How it works 70 Employee database Resumes Labour market statistics PoolParty UnifiedViews RDF Graph Database PoolParty GraphSearch PoolParty Thesaurus Server PoolParty User Now I can identify employees along many dimensions.
  • 71. © Semantic Web Company 2019 Components and Features 71
  • 72. © Semantic Web Company 2019 PoolParty Components 72 PoolParty Taxonomy & Ontology Server PoolParty Extractor PoolParty GraphEditor PoolParty UnifiedViews PoolParty Semantic Classifier PoolParty GraphSearch
  • 73. SMEs benefiting from Corpus Learning 73 The Knowledge Engineer’s perspective Taxonomy & Ontology Server Entity Extractor & Semantic Classifier Data Integration & Data Linking Bain Capital is a venture capital company based in Boston, MA. Since inception it has invested in hundreds of companies including AMC Entertainment, Brookstone, and Burger King. The company was co-founded by Mitt Romney. UnifiedViews PoolParty GraphSearch Identify new candidate concepts to be included in a controlled vocabulary RDF Graph Database Factsheet Schema mapping based on ontologies Entity linking based on Knowledge Graph Unstructured Data Semi-structured Data Structured Data Controlled vocabularies as a basis for highly precise knowledge extraction and text classification
  • 74. © Semantic Web Company 2019 TECHNICAL DEEP DIVE Make your data actionable! Working with Knowledge Graphs 7474
  • 75. © Semantic Web Company 2019 Why use Graph Databases? 75 Dealing with hierarchical and highly connected datasets Entity-centric views (in contrast to document-centric views) Exploring the connection between entities of a graph Integrating heterogeneous data sources (structured & unstructured in a “schema-late” approach)
  • 76. © Semantic Web Company 2019 What makes a store RDF ready? 76 It offers a SPARQL endpoint to query the database using SPARQL It implements at least one of the RDF ready libraries/frameworks: ▸ Eclipse RDF4J ▸ Apache Jena
  • 77. © Semantic Web Company 2019 SPARQL 77 SPARQL retrieves and manipulates data stored in an RDF graph database Watch Tutorial from PoolParty Academy
  • 78. © Semantic Web Company 2019 SHACL 78 Shapes Constraint Language Data Quality and Consistency: Validating graph-based data against a set of conditions
  • 79. © Semantic Web Company 2019 Example 1 79 Validating Concept labels and hierarchic structure in a SKOS taxonomy Concept labels and hierarchic structure Constraints: A concept has to connect to a concept scheme. The labels have to be disjoint and the language must be unique. Concept Concept Scheme skos:hasTopConcept / skos:narrower skos:prefLabel label1@en skos:altLabel label2@en
  • 80. © Semantic Web Company 2019 Example 2 80 Compliance checks Maximum active board members Constraint: There must not be more than two active board members for each Legal Entity. Board MemberLegal Entity Active hasBoardMembership hasBoardMemberStatus
  • 81. © Semantic Web Company 2019 Example 3 81 Data consistency Consistent geographic information Constraint: If a Legal Entity has a country and a city assigned, then both must be related with a skos:narrower path, so that the geo information is consistent. Legal Entity Country City isLocatedInCountry isLocatedInCity skos:narrower
  • 82. © Semantic Web Company 2019 TAKE AWAYS Make your data actionable! Summarization 8282
  • 83. Four-layered Information Architecture 83 Semantic AI application Semantic Layer / Knowledge Graph Metadata Layer Content & Data Layer
  • 84. © Semantic Web Company 2019 From Documents To Entitities 84 Watch out for ambiguity!
  • 85. © Semantic Web Company 2019 ML based on Semantic AI Engines 85 Bla bla bla bla. Bla bla bla bla The stove is on. The stove is hot! Ontological model → reasoningTaxonomical model → is-a abstractions Bla stove bla bla. Bla bla bla hot Switched on devices are dangerous devices. Switched on devices are dangerous, only if the operating temperature is above 100 degrees and the automatic shutdown mechanism is broken. The stove is on. The stove is hot! Word embeddings/cooccurences → is related The stove is on. The stove is hot! Bla bla bla bla Bla bla bla bla. Separating signal from noise
  • 86. © Semantic Web Company 2019 86 doc doc doc Jaguar Cat Felidae Carnivore Keystone Species Elephant Keystone Species Wolf Carnivore Rabbit doc Jaguar Elephant Wolf Rabbit doc Carnivore Traditional approach Graph-based approach Show me all documents about Felidae doc doc doc Show me all documents about Carnivores Cat = Felidae Keystone Species Keystone Species? Traditional vs. Graph-based Metadata Management
  • 87. © Semantic Web Company 2019 87 doc doc doc Jaguar Cat Felidae Carnivore Keystone Species Elephant Keystone Species Wolf Carnivore Rabbit doc Jaguar Elephant Wolf Rabbit doc Carnivore Traditional approach Graph-based approach Show me all documents about Felidae doc doc doc Show me all documents about Carnivores Cat = Felidae Keystone Species Keystone Species? Metadata per document 1. No or little network effects 2. No reuse of metadata 3. Metadata resides in silos 4. Data quality hard to measure 5. Not machine-readable Knowledge about metadata 1. Explicit knowledge models 2. Reusable and measurable 3. Metadata is machine-processable 4. Standards-based metadata 5. Linkable metadata opens silos Traditional vs. Graph-based Metadata Management
  • 88. Next steps PoolParty Academy Video Tutorials Online Help & Manuals Demo Account 88
  • 89. © Semantic Web Company 2019 Connect Andreas Blumauer CEO & Managing Partner, Semantic Web Company ▸ [email protected] ▸ https://www.linkedin.com/in/andreasblumauer ▸ https://twitter.com/semwebcompany ▸ https://ablvienna.wordpress.com/ 89