Introduction to Knowledge Graphs with Grakn and Graql
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Cognitive/AI systems process knowledge that is far too complex for current databases. They require an expressive data model and an intelligent query language to perform knowledge engineering over complex datasets. In this talk, we will discuss how Grakn, a database to organise complex networks of data and make it queryable, provides the knowledge graph foundation for intelligent systems to manage complex data. We will discuss how Graql, Grakn's reasoning (through OLTP) and analytics (through OLAP) query language, provides the tools required to do the job: a knowledge schema, a logical inference language, a distributed analytics framework. And finally, we will discuss how Graql’s language serves as unified data representation of data for cognitive systems.
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Introduction to Knowledge Graphs with Grakn and Graql
- 1. Tomás Sabat Stöfsel Introductionto Knowledge Graphs with Grakn and Graql Welcome everyone! Please leave your name and where you’re calling in from in the chat.
- 4. Civilization advances by extending the number of important operations we can perform without thinking about them. -- Alfred North Whitehead Author of Principia Mathematica
- 5. Inspiration
- 6. Inspiration Knowledge Representation & Automated Reasoning
- 7. Inspiration 𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒 𝑎, 𝑋 ⇒ 𝑎 ∈ 𝑋 Mammal Human Cat Alice Bob sub sub instance instance 𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒 𝐴𝑙𝑖𝑐𝑒, 𝐻𝑢𝑚𝑎𝑛 ⇒ 𝐴𝑙𝑖𝑐𝑒 ∈ 𝑀𝑎𝑚𝑚𝑎𝑙 𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒 𝐵𝑜𝑏, 𝐶𝑎𝑡 ⇒ 𝐵𝑜𝑏 ∈ 𝑀𝑎𝑚𝑚𝑎𝑙 𝑠𝑢𝑏 𝑋, 𝑌 ⇒ 𝑋 ⊆ 𝑌 Knowledge Representation (KR): a system to encode domain knowledge consistently, for logical interpretation, potentially by a computer
- 8. Inspiration 𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒 𝑆𝑜𝑐𝑟𝑎𝑡𝑒𝑠, 𝑀𝑎𝑛 ⇒ 𝑆𝑜𝑐𝑟𝑎𝑡𝑒𝑠 ∈ 𝑀𝑎𝑛 𝑆𝑜𝑐𝑟𝑎𝑡𝑒𝑠 ∈ 𝑀𝑜𝑟𝑡𝑎𝑙 𝑠𝑢𝑏 𝑀𝑎𝑛, 𝑀𝑜𝑟𝑡𝑎𝑙 ⇒ 𝑀𝑎𝑛 ⊆ 𝑀𝑜𝑟𝑡𝑎𝑙 Automated Reasoning (AR): a system to derive logical conclusions from two or more propositions that are believed to be true Man “Socrates” sub instance Mortal
- 12. Inspiration Problem Product Destination Plane Train Truck Delivery Employee Availability Pricing Discount Order Customer Regulation Policy Clearance job Transport Warehouse Driver Department Stock Holiday Damage Shift Returns Location City Country Region Manager
- 13. Inspiration Problem Product Destination Plane Train Truck Delivery Employee Availability Pricing Discount Order Customer Regulation Policy Clearance job Transport Warehouse Driver Department Stock Holiday Damage Shift Returns Location City Country Region Manager TOO COMPLEX
- 15. Inspiration Problem What makes data complex?
- 16. Inspiration Problem Type Hierarchies Animal Mammal Reptile sub sub Human Cat sub sub Crocodile sub Employment Temporary Employment Permanent Employment sub sub Entity Type Hierarchies Relation Type Hierarchies
- 17. Inspiration Problem Unary/Binary Relations Person TeachesReflexive Relations Symmetric Relations Husband Married Wife Transitive Relations UK Located EuropeLondon Located Located
- 18. Inspiration Problem Ternary/N-ary and Infinitary Relations Buyer Purchase Seller Product Ternary Relations Owner Joint Venture … Venture Infinitary Relations Owner Owner
- 20. Inspiration Problem Equivalent Relations Parent Parentship Child Siblingship Parentship Child Child Child
- 21. Inspiration Problem What makes querying complex networks challenging?
- 22. Inspiration Problem Charity University Company Office Laboratory Warehouse Kings Cross London UK loc loc Which organisations have premises in the UK?
- 23. Inspiration Problem Charity University Company Office Laboratory Warehouse Kings Cross London UK loc loc Which organisations have premises in the UK?
- 24. Inspiration Problem Charity University Company Office Laboratory Warehouse Kings Cross London UK loc loc Queries become highly permutative and verbose
- 25. Inspiration Problem Which movies and actors in the same network (cluster)? Titanic Kate Leonardo Scarface Al Pacino The Godfather cast cast cast cast
- 26. Inspiration Problem Which movies and actors in the same network (cluster)? Titanic Kate Leonardo Scarface Al Pacino The Godfather cast cast cast cast ”Connected-Component” (Clustering) Pregel Algorithm Reference implementatio n is 200+ lines of Java code
- 27. Inspiration Problem BSP (MapReduce & Pregel) algorithms become hard to reuse Titanic Kate Leonardo Scarface Al Pacino The Godfather cast cast cast cast
- 28. Inspiration Problem Cause Why are we not able to handle complexity?
- 29. Inspiration Problem Cause SQL: too low-level, 3NF, killed by JOINs
- 30. Inspiration Problem Cause Graph/NoSQL: no schema, no constraint, too low-level
- 31. Inspiration Problem Cause RDF/OWL: too low-level, verbose, built for the web (open- world), designed for logicians
- 32. Inspiration Problem Cause Current database languages are too low-level to handle complexity Could not model complex domains Could not simplify verbose queries Could not reuse analytics algorithms
- 33. Inspiration Problem Cause Solution What’s the solution?
- 34. Inspiration Problem Cause Solution Grakn & Graql A Knowledge Representation System and Language
- 35. Inspiration Problem Cause Solution Grakn & Graql A Knowledge Representation System and Language A 5th Generation Language A [query] language to declare logic & constraints to the computer, rather than to instruct algorithms
- 36. Inspiration Problem Cause Solution The Knowledge Model A knowledge schema needs to represent: type hierarchies, hyper-relations and rules Thing Entity Attribute Relation Role sub sub subhas plays relates sub sub sub sub Rule
- 37. Inspiration Problem Cause Solution company person employment name has has employer employee
- 38. Inspiration Problem Cause Solution company person employment name has has employer employee startup customer sub sub
- 39. Inspiration Problem Cause Solution company person employment name has has employer employee startup customer sub sub has sub has sub
- 40. Inspiration Problem Cause Solution company person employment name has has employer employee startup customer sub sub marriage husband wife
- 41. Inspiration Problem Cause Solution person: Alice customer: Bob company: IBM startup: Grakn e e m employer employer employee employee wife husband commit: success
- 42. Inspiration Problem Cause Solution person: Charlie company: Applem wife husband
- 43. Inspiration Problem Cause Solution person: Charlie company: Applem wife husband commit: failed Schema Constraints are Back!
- 44. Reflexive Relations Inspiration Problem Cause Solution person: Alice teaches teacher taught
- 45. Inspiration Problem Cause Solution person: Alice Married person: Bob spouse spouse Symmetric Relations
- 46. person: Alice purchase company: Amazon movie: Titanic Ternary Relations Inspiration Problem Cause Solution product buyer seller
- 47. company: A joint-venture … company: Co Infinitary Relations company: B company: C Inspiration Problem Cause Solution owner owned
- 48. person: Alice marriage person: Bob locates city: London Inspiration Problem Cause Solution Nested Relations wife husband located locating
- 49. UK EuropeLondon locates Transitive Relations Inspiration Problem Cause Solution locates locates located located located locating locatinglocating
- 50. UK EuropeLondon locates Transitive Relations Inspiration Problem Cause Solution locates locates located located located locating locatinglocating
- 51. Parent Parentship Child Siblingship Parentship Child Child Child Equivalent Relations Inspiration Problem Cause Solution
- 52. Parent Parentship Child Siblingship Parentship Child Child Child Equivalent Relations Inspiration Problem Cause Solution
- 53. Charity University Company Office Laboratory Warehouse Kings Cross London UK loc loc The Reasoning Query A reasoning query needs to retrieve: explicitly stored data, as well as implicitly derived information Inspiration Problem Cause Solution
- 54. Charity University Company Office Laboratory Warehouse Kings Cross London UK loc loc The Reasoning Query A reasoning query needs to retrieve: explicitly stored data, as well as implicitly derived information Inspiration Problem Cause Solution
- 56. The difference is the number of important operations we can perform without thinking about them
- 57. Inspiration Problem Cause Solution Which movies and actors in the same network (cluster)? Titanic Kate Leonardo Scarface Al Pacino The Godfather cast cast cast cast ”Connected-Component” (Clustering) Pregel Algorithm Reference implementatio n is 200+ lines of Java code
- 58. Inspiration Problem Cause Solution Which movies and actors in the same network (cluster)? Titanic Kate Leonardo Scarface Al Pacino The Godfather cast cast cast cast ”Connected-Component” (Clustering) Pregel Algorithm
- 59. Inspiration Problem Cause Solution BSP (MapReduce & Pregel) algorithms as native Graql OLAP queries Titanic Kate Leonardo Scarface Al Pacino The Godfather cast cast cast cast More to come: PageRank, Triangle Count, Density, Cliques, and so on
- 60. Inspiration Problem Cause Solution What makes Grakn a Knowledge Base (aka. Knowledge Graph) Knowledge schema Flexible Entity-Relationship concept-level schema to build build knowledge model Automated Reasoning Automated deductive reasoning Distributed Analytics Automated distributed algorithms algorithms (BSP) as a language language (OLAP) Higher-Level Language Strong abstraction over low-level level constructs and complex relationships
- 61. Inspiration Problem Cause Solution What’s our progress?
- 62. Inspiration Problem Cause Solution What’s our progress? Schema: expressive constructs to build knowledge models
- 63. Inspiration Problem Cause Solution What’s our progress? Schema: expressive constructs to build knowledge models Language: simple language to write expressive queries
- 64. Inspiration Problem Cause Solution What’s our progress? Schema: expressive constructs to build knowledge models Language: simple language to write expressive queries Reasoning: deductive logic to eliminate permutative queries
- 65. Inspiration Problem Cause Solution What’s our progress? Schema: expressive constructs to build knowledge models Language: simple language to write expressive queries Reasoning: deductive logic to eliminate permutative queries Architecture: distributed server and multi-language clients
- 66. Inspiration Problem Cause Solution What’s our progress? Schema: expressive constructs to build knowledge models Language: simple language to write expressive queries Reasoning: deductive logic to eliminate permutative queries Architecture: distributed server and multi-language clients Build: multi-repo build-test-release automation system
- 67. Inspiration Problem Cause Solution What’s our progress? Schema: expressive constructs to build knowledge models Language: simple language to write expressive queries Reasoning: deductive logic to eliminate permutative queries Architecture: distributed server and multi-language clients Build: multi-repo build-test-release automation system Performance: query throughput and execution at scale
- 68. Inspiration Problem Cause Solution What’s our progress? Schema: expressive constructs to build knowledge models Language: simple language to write expressive queries Reasoning: deductive logic to eliminate permutative queries Architecture: distributed server and multi-language clients Build: multi-repo build-test-release automation system Performance: query throughput and execution at scale Analytics: robust BSP (OLAP) algorithms at scale
- 69. Inspiration Problem Cause Solution Applications
- 70. Inspiration Problem Cause Solution Applications Future “ForacomputertopassaTuringTest,itneedsto possess: - Natural LanguageProcessing, - KnowledgeRepresentation, - Automated Reasoning, and - MachineLearning” Peter Norvig (Research Director, Google) and Stuart J. Russell (CS Professor, UCBerkeley),“Artificial Intelligence: A Modern Approach”, 1994
- 71. Inspiration Problem Cause Solution Applications Future Comprehension and production of language: communication Natural Language Processing Reasoning, problem solving, logical deduction, and decision making Automated Reasoning Expression, Conceptualisation, memory and understanding Knowledge Representation Judgment and evaluation: To adapt to new circumstances and to detect and extrapolate new patterns Machine Learning Information Retrieval, Natural Language Understanding: User data, Enterprise data, Financial data, Web data, etc. Knowledge Acquisition
- 72. Inspiration Problem Cause Solution Applications Future Comprehension and production of language: communication Natural Language Processing Storage of knowledge (i.e. complex information), and retrieval of explicitly stored data and derive new conclusions. Knowledge Base/Graph Judgment and evaluation: To adapt to new circumstances and to detect and extrapolate new patterns Machine Learning Information Retrieval, Natural Language Understanding: User data, Enterprise data, Financial data, Web data, etc. Knowledge Acquisition
- 73. Inspiration Problem Cause Solution Applications Future Natural Language Processing Knowledge Base/Graph Machine LearningKnowledge Acquisition
- 74. Inspiration Problem Cause Solution Applications Future Natural Language Processing Knowledge Base/Graph Machine LearningKnowledge Acquisition
- 75. Thank you for attending this webinar! Follow us on: @graknlabs @tasabat Join our chatroom on: https://discord.gg/graknlabs
Editor's Notes
- Reflexive relation on set is a binary element in which every element is related to itself. A symmetric relation is a type of binary relation. An example is the relation "is equal to", because if a = b is true then b = a is also true. Formally, a binary relation R over a set X is symmetric if and only if: As a nonmathematical example, the relation "is an ancestor of" is transitive. For example, if Amy is an ancestor of Becky, and Becky is an ancestor of Carrie, then Amy, too, is an ancestor of Carrie.