Introduc)on to Knowledge Graphs and Rich Seman)c Search. Peter Haase, metaphacts Barry Norton, Bri4sh Museum Denny Vrandečić, Google / Wikimedia

Transcription

1 Introduc)on to Knowledge Graphs and Rich Seman)c Search Peter Haase, metaphacts Barry Norton, Bri4sh Museum Denny Vrandečić, Google / Wikimedia

2 Speaker Introduc4on A Knowledge Graph Perspec3ve

3 Outline What are Knowledge Graphs? Freebase, the Google Knowledge Graph and Wikidata Knowledge Graphs from a Cultural Heritage Perspec4ve Technical Founda4ons of Knowledge Graphs metaphacts Knowledge Graph PlaNorm Rich Seman4c Search Hands- On

4 WHAT ARE KNOWLEDGE GRAPHS?

5 A (very small) Knowledge Graph hvp:// rdf11- primer /example- graph.jpg

6 What are Knowledge Graphs? Seman)c descrip)ons of en))es and their rela)onships En))es: real world objects (things, places, people) and abstract concepts (genres, religions, professions) Rela)onships: graph- based data model where rela4onships are first- class Seman)c descrip)ons: types and proper4es with a well- defined meaning (e.g. through an ontology) Possibly axioma4c knowledge (e.g. rules) to support automated reasoning

7 Why (Knowledge) Graphs? We need a structured and formal representa4on of knowledge We are surrounded by en44es, which are connected by rela4ons Graphs are a natural way to represent en44es and their rela4onships Graphs can be managed efficiently

8 Google Knowledge Graph

9 Google Knowledge Graph: En4ty Search and Summariza4ons 9

10 Google Knowledge Graph: Discovering Related En44es

11 Google Knowledge Graph: Discovering Related En44es

12 Google Knowledge Graph: Factual Answers

13 LinkedIn Economic Graph 13

14 Public Knowledge Graphs

15 Freebase

16 Wikidata Collec)ng structured data. Unlike the Wikipedias, which produce encyclopedic ar4cles, Wikidata collects data, in a structured form. Collabora)ve. The data in Wikidata is entered and maintained by Wikidata editors, who decide on the rules of content crea4on and management in Wikidata suppor4ng the no4on of verifiability. Free. The data in Wikidata is published under the Crea4ve Commons Large. 16 million en44es 34 million statements 80 million labels 350 languages >400 million triples

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19 Histropedia

20 KNOWLEDGE GRAPHS FROM CULTURAL HERITAGE PERSPECTIVE

21 Knowledge Graph- based Disambigua4on

22 Knowledge Graph- based Disambigua4on

23 Knowledge Graph- based Disambigua4on

24 Knowledge Graph- based Disambigua4on

25 Knowledge Graph- based Disambigua4on

26 Metadata from Knowledge Graph

27 Comparison with Wikipedia/ DBpedia Looks fairly familiar

28 Comparison with Wikipedia/ DBpedia Extracted to form a graph of fairly similar contents But

29 Comparison with Wikipedia/ DBpedia What is 114cm tall?

30 Comparison with Freebase

31 Comparison with Freebase

32 Comparison with Freebase

33 ResearchSpace The ResearchSpace project: is funded by the Andrew Mellon Founda4on; develops a set of cultural heritage research tools; uses Metaphacts planorm as a basis to reuse and combine these tools for each project using ResearchSpace, for applica4ons beyond cultural heritage; configures and specialises these tools for data integrated using the CIDOC CRM ontology.

34 ResearchSpace Graph Resources are typed into classes in rich poly- hierarchies: crm:e18_physical_thing crm:e71_man- Made_Thing crm:e19_physical_object crm:e24_physical_man- Made_Thing object/yca62958 crm:e22_man- Made_Object rdf:type rdfs:subclassof

35 ResearchSpace Graph Different structural parts are represented as separate resources: object/yca62958/inscription/3 crm:e34_inscription object/yca62958/inscription/2 rdf:type crm:p65_shows_visual_item object/yca62958 object/yca62958/inscription/1

36 ResearchSpace Graph The same separa4on of iden4ty exists for non- tangible resources: object/yca62958/production crm:p108i_was_produced_by object/yca62958/acquisition object/yca62958 crm:p24i_changed_ownership_through crm:p30i_custody_transferred_through

37 ResearchSpace: Knowledge Graphs for Cultural Heritage Using the planorm as collabora4on environment for researchers in Cultural Heritage expert users: researchers, curators Based on CIDOC- CRM: very rich, expressive ontology Large, cross- museum data sets E.g. Bri4sh Museum: 100s millions of triples Advanced search capabili4es Suppor4ng query construc4on Sharing of searches, results, visualiza4ons Data annota4on Discussions around cultural heritage annota4ons Argumenta4on support: Representa4on of conflic4ng views and opionions

38 ResearchSpace PlaNorm

39 Perspec4ves on Construc4ng Knowledge Graphs Extrac4on of structured knowledge from unstructured content From Wikipedia: DBpedia, Yago Publishing structured databases on the web Scien4fic database, e.g. Bio2RDF MusicBrainz, IMDB LinkedIn Economic Graph Collabora4ve, community- driven authoring Wikidata, Freebase Integra4on of different sources with filtering and cura4on Google Knowledge Graph

40 USE CASES FOR KNOWLEDGE GRAPHS

41 Ques4on Answering and Structured Results in Web Search

42 Intelligent Assistants Examples: Google Now, Apple Siri, Microsoq Cortana Knowledge- is- power (E.A. Feigenbaum in 1977): knowledge of the specific task domain in which the program is to do its problem solving was more important as a source of power for competent problem solving than the reasoning method employed Knowledge Graphs to support Automated Reasoning and Planning Augmented with natural language interfaces, dialog system, personaliza4on, emo4on

43 Google Now Contextual awareness: to know where you are and what you might need there Based on knowledge about more than 100 million places physical layout and geometry, when are they busy, when are they open, what are you likely to need when you re there

44 METAPHACTS KNOWLEDGE GRAPH PLATFORM

45 Why Knowledge Graphs Organiza)on and distribu)on of knowledge Central knowledge repository to represent, simplify and connect the knowledge about relevant en44es in the enterprise Unified structure, data model and seman4cs Openness, transparency and accessibility of knowledge Shared and agreed iden4fiers for harmonized access to enterprise data sources Simplified integra4on across silos without the need to replace exis4ng databases Collabora)on and sharing of knowledge Individuals are empowered to share knowledge and to make decisions based on comprehensive knowledge Simplified publishing and sharing of data leads to improved communica4on, increasing knowledge sharing and reduced redundancy of work Cross- organiza4onal data analysis become possible Enrichment and contextualiza)on of knowledge Bridging internal knowledge with open knowledge Transparent reuse of public sources

46 PorNolio: Soqware, solu4ons & services Storing and querying of knowledge graphs Scalable databases for big graphs, building on Blazegraph High- performance graph analy)cs, based on MapGraph (GPU accelerated) Light- weight reasoning with large- scale knowledge graphs Crea)on and cura)on of knowledge graphs Semi- automa)c crea)on of knowledge graphs from exis4ng sources Data integra)on and ontology- based data access Collabora)ve management of knowledge graphs Applica)on development u4lizing knowledge graphs Rapid development of end- user oriented applica)ons Visualiza)on of knowledge graphs, seman4c search Mobile applica)ons & augmented and virtual reality

47 Frontend Layer Service Layer Database Layer metaphacts platform Integrate & consolidate data Manage repositories Visualize and explore data Application Building metaphacts planorm metaphacts End User Apps create External apps & integrated use of metaphacts service Visualization Exploration Collaborative editing & publishing Custom services Query catalog Access control Rules and events Workflow Data & information access: query, read, update, inferencing Graph analytics Integrated knowledge graph Fulltext search Provenance / RDR Data source management: Virtual & warehouse data integration, transformation, linkage Open data sources 47 Company-internal data sources

48 Geo/Spa4al 48

49 Geo/Spa4al 49

50 Graph algorithms 50

51 Graph algorithms 51

52 Graph Algorithms: Ancestor Rela4ons

53 Wikidata Ontology

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56 Countries distinct region in geography; a broad term that can include political divisions or regions associated with distinct political characteristics administra.itorial entity - r y 1ormer3,intry commgs1ate te Boer.blics constit.country state with lie recognition Instances distributed across the subclasses Filter Results out former country country island nation landlocked country empire state with limited recognition transcontinental country constituent country communist state country of the United Kingdom count

57 Demo Wikidata

58 RICH SEMANTIC SEARCH

59 Seman4c Search: a defini4on Seman4c search is a retrieval paradigm that Makes use of the structure of the data or explicit schemas to understand user intent and the meaning of content Exploits this understanding at some part of the search process Combina4on of unstructured elements and seman4c rela4onships Unstructured elements Names, labels and descrip4ons Metadata may be embedded inside documents Structured, seman4c elements Structured data Types Links and rela4onships

60 Seman4c Search a Process View Knowledge Representation Query Construc4on Keywords Forms NL Formal language Knowledge Graph Resources Query Processing IR- style matching & ranking DB- style precise matching KB- style matching & inferences Result Presenta4on Query visualiza4on Document and data presenta4on Summariza4on Documents Query Refinement Implicit feedback Explicit feedback Incen4ves Document Representation

61 Search with Autocomple4on and Seman4c Disambigua4on

62 Use in Search Widget Component

63 Visual Disambigua4on

64 Natural Language Search

65 Natural Language to SPARQL SELECT DISTINCT?result?label WHERE { { {?subject0 rdfs:label "California"@en. } UNION {?subject0 skos:altlabel "California"@en. } } { {?predicate1 rdfs:label "capital"@en. } UNION {?predicate1 skos:altlabel "capital"@en. } }?predicate1 a wikibase:property.?predicate1 wikibase:directclaim?directpredicate2.?subject0?directpredicate2?result. } OPTIONAL {?result rdfs:label?label FILTER (LANG(?label) = "en"). }

66 Natural Language Search

67 Interac4ve Construc4on and Refinement of Structured Queries

68 Structured Search over Wikidata

69 Wikidata: Fundamental Categories Object: classes, instances Person: classes, instances Organiza)on: classes, instances Loca)on: classes, instances Event: classes, instances

70 Fundamental Categories and Rela4ons

71 HANDS ON / EXERCISES 71

72 Hands On / Exercises Wikidata System for exercises: hvp://wikidata.metaphacts.com/

73 Exercises: Structured Search Example: Search for people educated at Stanford

74 Exercises: Structured Search Find people born in your home town Find people who were born and died in London Find people who par4cipated both in the 2012 Summer Olympics as well as the 2014 Winter Olympics Find things manufactured by Ford, use Facets to restrict to air planes Find objects located at the Bri4sh Museum, use facets to restrict to sculptures Find the Rembrandts at the Rijksmuseum Find events located in your home town or country Find states that share a border with the state of Colorado

75 Links and References Wikidata hvp://wikidata.org/ Wikidata Query Service Beta hvps://wdqs- beta.wmflabs.org metaphacts planorm hvp://metaphacts.com/ metaphacts planorm on Wikidata hvp://wikidata.metaphacts.com/ ResearchSpace hvp://researchspace.org/

76 Reading Material Wikidata: A Free Collabora4ve Knowledgebase Denny Vrandečić, Markus Krötzsch Communica4ons of the ACM, Vol. 57 No. 10, Pages hvp://cacm.acm.org/magazines/2014/10/ wikidata/fulltext KDD14 Construc4ng and Mining Web- scale Knowledge Graphs Facebook and Google hvp:// construc4ng- and- mining- webscale- knowledge- graphs