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Introduction to Knowledge Graphs Introduction to Knowledge Graphs When data learn to think outside the box Andreas Blumauer CEO of Semantic Web Company Andreas Semantic Web serves customers Blumauer Company >200 graduates founded Founder & CEO 2004 of parent company of Vienna active at Director of headquartered located PoolParty Software developer & version 7.0 Ltd London vendor of Gartner Introduction awarded based on by KMWorld used for manages integrates Text Mining CMS/DMS/DA with M/.. part of based on Search engine Ontologies Graph database standard for part of Knowledge Graphs standard for Taxonomies © Semantic Web Company 2019 2 Why Knowledge Graphs? WHAT’S THE PROBLEM? Many Challenges in (Enterprise) Information Management & AI © Semantic Web Company 2019 3 “Search” is still about documents only … and in enterprises it’s painful © Semantic Web Company 2019 4 Different shades of metadata © Semantic Web Company 2019 5 User-agnostic and context-free data models Do machines understand user intent? Do they have enough context? French SUV Which Sport-utility vehicle from France provides enough space for my 1. Intent recognition family with 3 kids? 2. Entity linking 3. Background knowledge Car Loadspace Max no. of seats The Peugeot The Kia 5008 breaks Sportage is new ground as a KIA Sorento 550 litres 7 our sporty large SUV with SUV packed many features. Peugeot 5008 823 litres 7 with smart features BMW X3 550 litres 5 © Semantic Web Company 2019 6 Commonwealth is a International of Nations Organisation is part of Support complex Q&A: Australia is a Country Which cities located in the Commonwealth of Nations is located in have a population of more Artificial than 2 mio. people? “Intelligence” Perth is a City Avoid illogical answers: Background distance between knowledge is key Perth is one of the Australia is a most isolated member of the major cities in the OECD, United world, with a Nations, G20, population of ANZUS, and the 2,022,044 living World Trade in Greater Perth. Organisation. © Semantic Web Company 2019 7 Machine Learning per Data Silo Deep Learning Monte Carlo TS Neuronal Lack of AI Strategy Genetic Algorithms Deep Learning networks Case based reasoning © Semantic Web Company 2019 8 Who is CEO of a Bank, headquartered in Europe that generates revenue per employee higher than 400,000 Euro? Desktop Data Integration Knowledge workers spend Head- Ticker Revenue <Employees> a large part of their quarter Symbol <VTX:CSGN> The task awaiting Tidjane working time on ad hoc 48,200 </VTX:CSGN> Thiam when he takes over Credit Zurich VTX: CSGN CHF 23.4b from Brady Dougan as the data integration tasks, the Suisse <LON:HSBA> so-called "research". 266,273 new chief executive at </LON:HSBA> Credit Suisse Group AG is HSBC London LON: HSBA USD 60.0b <ETR: ALV> clear: how to pull the Swiss Which parts of it could be 147,425 </ETR: ALV> bank out of a Allianz Munich ETR: ALV EUR 122.3b automated? <ETR: DBK> post-financial crisis rut. 101,104 </ETR: DBK> Deutsche Frankfurt ETR: DBK EUR 33.5b </Employees> Bank © Semantic Web Company 2019 9 Head- Ticker Umsatz quarter Symbol The task awaiting Tidjane Thiam when he takes over Credit Zürich VTX: CSGN CHF 23.4b from Brady Dougan as the Suisse new chief executive at Many challenges in HSBC London LON: HSBA USD 60.0b Credit Suisse Group AG is clear: how to pull the Swiss bank out of a data management Allianz München ETR: ALV EUR 122.3b post-financial crisis rut. ▸ Heterogeneous data Deutsche Frankfurt ETR: DBK EUR 33.5b Bank <Employees> ▸ Implicit semantics <VTX:CSGN> ▸ No background knowledge 48,200 </VTX:CSGN> ▸ Data quality difficult to measure <LON:HSBA> ▸ Proprietary schemas 266,273 </LON:HSBA> ▸ Unstructured data <ETR: ALV> ▸ Ambiguity 147,425 </ETR: ALV> ▸ Multilinguality <ETR: DBK> ▸ Various units and currencies 101,104 </ETR: DBK> </Employees> → From Search to QA engines © Semantic Web Company 2019 10 The Free Universal Construction Kit is a matrix of adapter bricks that enable complete interoperability between ten popular children's construction toys. Free Universal Construction Kit (Golan Levin et al) © Semantic Web Company 2019 11 ‘Interoperability’ begins with people 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 12 What is a Knowledge Graph? From a Knowledge From a Data Architect’s From a Data Engineer’s Engineer’s perspective perspective perspective A Knowledge Graph is a model of Structured as an additional It provides a structure and a knowledge domain created by virtual data layer, the KG lies on common interface for all of your subject-matter experts with the top of existing databases or data data and enables the creation of help of intelligent machine sets to link all your data smart multilateral relations learning algorithms. together at scale – be it throughout your databases. structured or unstructured. © Semantic Web Company 2019 13 Other Graphs than Knowledge Graphs Mind the gap! Social Graphs / Networks Wordnets 14 ▸ Amazon Neptune ▸ Marklogic ▸ AllegroGraph ▸ GraphDB ▸ Azure Cosmos DB ▸ Stardog Oracle Spatial & Graph Neo4j Which Graph ▸ ... Model? Property Graph RDF Graph (Triple Stores) RDF versus Main use case Traverse a graph Query a graph Property Graphs Typical applications Path Analytics, Data Integration, → Converging approaches Social Network Analysis Knowledge Representation Standards No standards W3C Semantic Web standards → Gremlin, Cypher, → SPARQL 1.1 PGQL, ... Additional options Shortest path Inferencing (Example) calculations © Semantic Web Company 2019 15 Sounds interesting? Gimme some examples! WHO USES KGs? An overview over typical KG use cases © Semantic Web Company 2019 16 Can you see the Knowledge Graph? Taobao (Alibaba) PoolParty GraphViews © Semantic Web Company 2019 17 Google Knowledge Graph © Semantic Web Company 2019 18 Knowledge Graph Adoption Source: Collapsing the IT Stack: Clearing a path for AI adoption Alan Morrison (Sr. Research Fellow at PwC) 19 Knowledge Graphs for Information Retrieval Semantic tagging, query expansion, faceted search, classification, similarity-based recommender Now I can find all the documents in one place, and get assistance with it. Metadata harmonization Semantic tagging Entity linking Semantic Search Index 20 Example healthdirect Australia https://www.healthdirect.gov.au/ © Semantic Web Company 2019 21 Semantic Search PowerTagging and PowerSearch for SharePoint © Semantic Web Company 2019 22 As a researcher in pharmaceutical industry, I want to plan new experiments more efficiently. → Linking Structured to I want to know what’s already available. Unstructured Data and I’m interested in former experiments Use Case where to Industry Knowledge Graphs Research in ● certain genes were tested ● under specific treatment conditions Life Sciences ● in a target therapeutic area ● with help from categorisation systems like ‘disease hierarchies’ UniProt, ChEMBL Experiments Documentation MeSH DrugBank © Semantic Web Company 2019 23 Experiments Reusing Graphs Knowledge Graphs used as a Semantic Search Index 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” © Semantic Web Company 2019 24 Knowledge Graphs for Data Integration & Analytics Metadata enrichment, linked data, text mining, entity-centric search, agile reporting Employee database Enterprise Knowledge Resumes Graph Labour market statistics RDF Graph Database Now I can identify employees along many PoolParty GraphSearch dimensions. 25 Entity-centric Search & Analytics HR Analytics Dashboard © Semantic Web Company 2019 26 Knowledge Graphs for E-commerce/Content Mgmt. Personalization, recommender / Dynamic menus, campaigns / Translating jargon into client’s needs My latest Campaigns Personalization Rainy Summer Warm Winter Muddy Spring Rules engine Mud Techlite Water proof Multi-Grip Multi-Heat Multi-Tech Rain Super Boots Winter Leather Comfortable Neoprene warm 27 Product Recommender Wine and Cheese Harmonizer © Semantic Web Company 2019 28 Example Electronic Arts Semantics at Play: Electronic Arts' Linked Data Journey © Semantic Web Company 2019 29 Knowledge Graphs for Virtual Assistants Integrating Semantics into Dialog Workflows My uncle lives in the same household. Darf ich seine Betreuungskosten absetzen? teyze ortak Ange- hörige ev haushalts- zugehöriger uncle Angehöriger same household 30 Knowledge Graphs for KM Systems Personalization, recommender, matchmaking, push services, smart assistants 31 Use Case Semantic Matchmaking Event Advisor at SEMANTiCS © Semantic Web Company 2019 32 I am interested in ... Architecture Languedoc-Roussillon Use Case Mediterranean Languedoc is a significant producer Sea of wine, and a major contributor to the surplus known as the "wine Recommender Wine Tasting lake". Today it produces more than a third of the grapes in France, and System is a focus for outside investors. The region contains the historic cities of Carcassonne, Toulouse, Connecting Montpellier, countless Roman ▸ content to content, monuments, medieval abbeys, Romanesque churches,
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