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WSDM 2018 JAY PUJARA and SAMEER SINGH Introducing Presenters WSDM 2018 JAY PUJARA AND SAMEER SINGH Introducing Presenters Jay Pujara: Research Scientist at USC/ISI Sameer Singh: Assistant Professor at UCI 2 Tutorial Overview https://kgtutorial.github.io 3 Tutorial Overview https://kgtutorial.github.io Part 1: Knowledge Graphs 4 Tutorial Overview https://kgtutorial.github.io Part 1: Knowledge Graphs Part 2: Knowledge Extraction 5 Tutorial Overview https://kgtutorial.github.io Part 1: Knowledge Graphs Part 2: Part 3: Knowledge Graph Extraction Construction 6 Tutorial Overview https://kgtutorial.github.io Part 1: Knowledge Graphs Part 2: Part 3: Knowledge Graph Extraction Construction Part 4: Critical Analysis 7 Tutorial Overview https://kgtutorial.github.io Part 1: Knowledge Graphs Part 2: Part 3: Knowledge Graph Extraction Construction Part 4: Critical Analysis 8 Tutorial Overview https://kgtutorial.github.io Part 1: Knowledge Graphs Part 2: Part 3: Knowledge Extraction Graph Construction Part 4: Critical Analysis 9 Tutorial Outline 1. Knowledge Graph Primer [Jay] 2. Knowledge Extraction Primer [Jay] 3. Knowledge Graph Construction a. Probabilistic Models [Jay] Coffee Break b. Embedding Techniques [Sameer] 4. Critical Overview and Conclusion [Sameer] 10 What if I have a question? 11 Tutorial Overview Part 1: Knowledge Graphs Part 2: Part 3: Knowledge Graph Extraction Construction Part 4: Critical Analysis 12 Knowledge Graph Primer TOPICS: WHAT IS A KNOWLEDGE GRAPH? WHY ARE KNOWLEDGE GRAPHS IMPORTANT? WHERE DO KNOWLEDGE GRAPHS COME FROM? KNOWLEDGE REPRESENTATION CHOICES PROBLEM OVERVIEW 13 Knowledge Graph Primer TOPICS: WHAT IS A KNOWLEDGE GRAPH? WHY ARE KNOWLEDGE GRAPHS IMPORTANT? WHERE DO KNOWLEDGE GRAPHS COME FROM? KNOWLEDGE REPRESENTATION CHOICES PROBLEM OVERVIEW 14 What is a knowledge graph? 15 What is a knowledge graph? • Knowledge in graph form! 16 What is a knowledge graph? • Knowledge in graph form! • Captures entities, attributes, and relationships 17 What is a knowledge graph? • Knowledge in graph form! E1 • Captures entities, attributes, and relationships E2 • Nodes are entities E3 18 What is a knowledge graph? • Knowledge in graph form! A1 E 1 A2 • Captures entities, attributes, and relationships E2 • Nodes are entities A1 A • Nodes are labeled with 2 attributes (e.g., types) E3 A1 A3 19 What is a knowledge graph? • Knowledge in graph form! A1 E 1 A2 • Captures entities, attributes, and relationships E2 • Nodes are entities A1 A • Nodes are labeled with 2 attributes (e.g., types) E3 A1 • Typed edges between two A2 nodes capture a relationship between entities 20 Example knowledge graph • Knowledge in graph form! person John Lennon • Captures entities, attributes, and relationships band Beatles • Nodes are entities • Nodes are labeled with attributes (e.g., types) place Liverpool • Typed edges between two nodes capture a relationship between entities 21 Knowledge Graph Primer TOPICS: WHAT IS A KNOWLEDGE GRAPH? WHY ARE KNOWLEDGE GRAPHS IMPORTANT? WHERE DO KNOWLEDGE GRAPHS COME FROM? KNOWLEDGE REPRESENTATION CHOICES PROBLEM OVERVIEW 22 Why knowledge graphs? • Humans: •Combat information overload •Explore via intuitive structure •Tool for supporting knowledge-driven tasks • AIs: •Key ingredient for many AI tasks •Bridge from data to human semantics •Use decades of work on graph analysis 23 Applications 1: QA/Agents 24 Applications 2: Decision Support 25 Applications 3: Fueling Discovery 26 Knowledge Graphs & Industry •Google Knowledge Graph • Google Knowledge Vault •Amazon Product Graph •Facebook Graph API •IBM Watson •Microsoft Satori • Project Hanover/Literome •LinkedIn Knowledge Graph •Yandex Object Answer •Diffbot, GraphIQ, Maana, ParseHub, Reactor Labs, SpazioDati 27 Knowledge Graph Primer TOPICS: WHAT IS A KNOWLEDGE GRAPH? WHY ARE KNOWLEDGE GRAPHS IMPORTANT? WHERE DO KNOWLEDGE GRAPHS COME FROM? KNOWLEDGE REPRESENTATION CHOICES PROBLEM OVERVIEW 28 Where do knowledge graphs come from? 29 Where do knowledge graphs come from? • Structured Text ◦ Wikipedia Infoboxes, tables, databases, social nets 30 Where do knowledge graphs come from? • Structured Text ◦ Wikipedia Infoboxes, tables, databases, social nets • Unstructured Text ◦ WWW, news, social media, reference articles 31 Where do knowledge graphs come from? • Structured Text ◦ Wikipedia Infoboxes, tables, databases, social nets • Unstructured Text ◦ WWW, news, social media, reference articles • Images 32 Where do knowledge graphs come from? • Structured Text ◦ Wikipedia Infoboxes, tables, databases, social nets • Unstructured Text ◦ WWW, news, social media, reference articles • Images • Video ◦ YouTube, video feeds 33 Knowledge Graph Primer TOPICS: WHAT IS A KNOWLEDGE GRAPH? WHY ARE KNOWLEDGE GRAPHS IMPORTANT? WHERE DO KNOWLEDGE GRAPHS COME FROM? KNOWLEDGE REPRESENTATION CHOICES PROBLEM OVERVIEW 34 Hayes&McCarthy Brooks Frame Problem Subsumption Quillian ConceptNet Minsky, Filmore Semantic Frames Networks McCarthy Formalizing Commonsense Description Bobrow Logic STUDENT Simon&Newell General Problem Solver Lenant Winograd Cyc SHRDLU 2000 1990 1980 1970 1960 1950 1940 SHRUTI Rumelhart et al Series of Neural- BackPropagation Symbolic Models Minsky &Pappert McCulloch Systematicity “Perceptrons” &Pitts Debate Artificial Neurons SLIDE COURTESY OF DANIEL KHASHABI Knowledge Representation •Decades of research into knowledge representation •Most knowledge graph implementations use RDF triples • <rdf:subject, rdf:predicate, rdf:object> : r(s,p,o) • Temporal scoping, reification, and skolemization... •ABox (assertions) versus TBox (terminology) •Common ontological primitives • rdfs:domain, rdfs:range, rdf:type, rdfs:subClassOf, rdfs:subPropertyOf, ... • owl:inverseOf, owl:TransitiveProperty, owl:FunctionalProperty, ... 36 Semantic Web •Standards for defining and exchanging knowledge • RDF, RDFa, JSON-LD, schema.org • RDFS, OWL, SKOS, FOAF •Annotated data provide critical resource for automation •Major weakness: annotate everything? "LINKING OPEN DATA CLOUD DIAGRAM 2014, BY MAX SCHMACHTENBERG, CHRISTIAN BIZER, ANJA JENTZSCH AND RICHARD CYGANIAK. HTTP://LOD-CLOUD.NET/" 37 Information Extraction from Text •Focus of this tutorial! •Answer to the knowledge acquisition bottleneck •Many challenges: • chunking • polysemy/word sense disambiguation • entity coreference • relational extraction 38 Knowledge Graph Primer TOPICS: WHAT IS A KNOWLEDGE GRAPH? WHY ARE KNOWLEDGE GRAPHS IMPORTANT? WHERE DO KNOWLEDGE GRAPHS COME FROM? KNOWLEDGE REPRESENTATION CHOICES PROBLEM OVERVIEW 39 What is a knowledge graph? • Knowledge in graph form! A1 E 1 A2 • Captures entities, attributes, and relationships E2 • Nodes are entities A1 A • Nodes are labeled with 2 attributes (e.g., types) E3 A1 • Typed edges between two A2 nodes capture a relationship between entities 40 Basic problems A1 E 1 A2 E2 A1 A2 E3 A1 A2 41 Basic problems A1 E • Who are the entities 1 A2 (nodes) in the graph? E2 A1 A2 E3 A1 A2 42 Basic problems A1 E • Who are the entities 1 A2 (nodes) in the graph? • What are their attributes E2 and types (labels)? A1 A2 E3 A1 A2 43 Basic problems A1 E • Who are the entities 1 A2 (nodes) in the graph? • What are their attributes E2 and types (labels)? A1 A2 • How are they related E3 (edges)? A1 A2 44 Basic problems A1 E • Who are the entities 1 A2 (nodes) in the graph? • What are their attributes E2 and types (labels)? A1 A2 • How are they related E3 (edges)? A1 A2 45 Knowledge Graph Construction Knowledge Graph Extraction Construction 46 Two perspectives Knowledge Extraction Graph Construction • Who are the entities • Who are the entities (nodes) in the graph? (nodes) in the graph? • Named Entity Recognition • Entity Linking • Entity Coreference • Entity Resolution • What are their attributes • What are their attributes and types (labels)? and types (labels)? • Named Entity Recognition • Collective Classification • How are they related • How are they related (edges)? (edges)? • Relation Extraction • Link Prediction • Semantic Role Labeling 47 Tutorial Outline 1. Knowledge Graph Primer [Jay] 2. Knowledge Extraction Primer [Jay] 3. Knowledge Graph Construction a. Probabilistic Models [Jay] Coffee Break b. Embedding Techniques [Sameer] 4. Critical Overview and Conclusion [Sameer] 48.
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