Knowledge Graphs: In Theory and Practice
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1 Knowledge Graphs: In Theory and Practice Nitish Aggarwal, IBM Watson, USA, Sumit Bhatia, IBM Research, India Saeedeh Shekarpour, Knoesis Research Centre Ohio, USA Amit Sheth, Knoesis Research Centre Ohio, USA
2 The material presented in this tutorial represents the personal opinion of the presenters and not of IBM and affiliated organization.
3 Outline of the tutorial Part 1: Knowledge Graph Construction Introduction DBpedia: Knowledge extraction Approaches to extend knowledge graph Knowledge extraction from scratch Part 2: Knowledge Graph Analytics Finding entities of interest Entity exploration Upcoming challenges
4 What is Knowledge Graph The Knowledge Graph is a knowledge base used by Google to enhance its search engine's search results with semantic-search information gathered from a wide variety of sources.
5 What is Knowledge Graph The Knowledge Graph is a knowledge base used by Google to enhance its search engine's search results with semantic-search information gathered from a wide variety of sources. A Knowledge graph (i) mainly describes real world entities and interrelations, organized in a graph (ii) defines possible classes and relations of entities in a schema (iii) allows potentially interrelating arbitrary entities with each other [Paulheim H.] We defines a Knowledge Graph as an RDF graph consists of a set of RDF triples where each RDF triple (s,p,o) is an ordered set of following RDF term. [Pujara J. al al.]
6 What is Knowledge Graph No single formal definition Defines real world entities Provides relationships between them
7 What is Knowledge Graph No single formal definition Defines real world entities Provides relationships between them Contains rules defines through ontologies Enable reasoning to infer new knowledge
8 Why Knowledge Graph Building an intelligent system that can interact with human, requires knowledge about real world entities.
9 Why Knowledge Graph Building an intelligent system that can interact with human, requires knowledge about real world entities. Enhance search results. Enhance ad sense. Help in language understanding. Enables knowledge discovery.
10 Is there existing knowledge graph ready to use for my application?
11 LinkedIn Knowledge Graph Amazon Product Graph Facebook Entity Graph Microsoft Satori Google Knowledge Graph
12
13 DBpedia: Knowledge extraction
14 DBpedia: Knowledge extraction The City of New York, often called New York City or simply New York, is the most populous city in the United States.
15 DBpedia: Knowledge extraction The City of New York, often called New York City or simply New York, is the most populous city in the United States. <New York City>, <CityIn> <United States>. <City Name>, <locatedin> <Country Name>.
16 DBpedia: Knowledge extraction The City of New York, often called New York City or simply New York, is the most populous city in the United States.
17 DBpedia: Knowledge extraction
18 DBpedia: Knowledge extraction <head entity>, <rel> < tail entity >
19 DBpedia: Knowledge extraction <head entity>, <rel> < tail entity > Wikipedia Infobox
20
21 DBpedia: Knowledge extraction
22 DBpedia: Knowledge extraction
23 DBpedia: Knowledge extraction
24 DBpedia: Knowledge extraction Ontology (Classes, properties) dbr:ibm dbp:foundedby dbr:charles_ranlett_flint dbr:ibm dbp:foundedby dbr:charles_ranlett_flint Parsers dbr:ibm dbp:foundedby dbr:charles_ranlett_flint
25 (Research) problems in knowledge graphs Incomplete knowledge Missing entities Missing relations Limited entity and relation types
26 (Research) problems in knowledge graphs Incomplete knowledge Missing entities Missing relations Limited entity and relation types Incorrect knowledge Wrong entity label recognition Wrong entity and relation type Wrong facts
27 (Research) problems in knowledge graphs Incomplete knowledge Missing entities Missing relations Limited entity and relation types Incorrect knowledge Wrong entity label recognition Wrong entity and relation type Wrong facts Inconsistency in knowledge Different labels for same entity Merging entities with same labels
28 Approaches to extend knowledge graphs Extracting knowledge from Wikipedia tables Large amount of raw data in form of tables Tables have some implicit structure/patterns
29 Approaches to extend knowledge graphs Extracting knowledge from Wikipedia tables Large amount of raw data in form of tables Tables have some implicit structure/patterns Wiki:AFC_Ajax containing relations between players, their shirt number, and country
30 Approaches to extend knowledge graphs <Wiki:AFC_Ajax, dbp:rel, Wiki:Andre_Onana> 80% entities in the table have relation dbp:rel with the Wikipedia title entity Wiki_AFC_Ajax Other 20% entities are likely to have the same relationship dbp:rel with Wiki_AFC_Ajax [Munoz E. at al.] Using Linked Data to Mine RDF from Wikipedia's Tables, WSDM 2014
31 Approaches to extend knowledge graphs Features Article features: no. of tables, length Table features: no. of rows, no. of columns Column features: no. of entities in column, potential relations Cell features: no. of entities in a cell, length of cell Many others Combines using classification method Prec. Rec. F1 Rule-based SVM Logistic [Munoz E. at al.] Using Linked Data to Mine RDF from Wikipedia's Tables, WSDM 2014
32 Approaches to extend knowledge graphs Features Article features: no. of tables, length Table features: no. of rows, no. of columns Column features: no. of entities in column, potential relations Cell Rules/heuristics features: no. of entities based in methods a cell, length makes of cell mistakes, and hard to create Many others one rule for everyone. Even though combining different features achieves 80% accuracy, it introduces 20% noise. Combines using classification method Prec. Rec. F1 Rule-based SVM Logistic [Munoz E. at al.] Using Linked Data to Mine RDF from Wikipedia's Tables, WSDM 2014
33 Table data is limited, we need to go beyond
34 Approaches to extend knowledge graphs Missing entity/literal for a relation Christopher A. Welty is an American computer scientist, who works at Google Research in NY <dbr:chris_welty> <employedby> <?> "Tom Cruise and Brad Pitt appear in Interview with the Vampire" <dbr:brad_pitt> <?> <dbr:tom_cruise>
35 Approaches to extend knowledge graphs Missing entity/literal for a relation Christopher A. Welty is an American computer scientist, who works at Google Research in NY <dbr:chris_welty> <employedby> <?> "Tom Cruise and Brad Pitt appear in Interview with the Vampire" <dbr:brad_pitt> <?> <dbr:tom_cruise> Knowledge Base Completion Similar to link prediction in social network but a bit more challenging Need to identify relation type in addition to binary output.
36 Approaches to extend knowledge graphs Knowledge Base Completion TransE: learn the entity and relation embeddings by assuming that translation of entity embeddings correspond to their relation embeddings. [Bordes et at. 2013] S + R T, where <S, R, T> TransH: Learn different entity embedding for different relationships [Wang at el. 2014] TransR: Learn entity and relation embeddings in different space, following by translation perform in relation space. [Lin Y. at el. 2015] Many more methods [Nickel M. at al, 2015]
37 Knowledge base completion approaches focus on finding missing entities/relations
38 Need to add new entities from external sources
39 Need to add new entities from external sources Entity recognition in external text resource Many Named Entity Recognition systems Link extracted entity to KG or create a new node if it does not have a corresponding entity TAC-KBP (Entity Discovery and Linking task) [Ji H. at el. 2016]
40 Building knowledge graph such as DBpedia requires lot of manual efforts.
41 Building knowledge graph such as DBpedia requires lot of manual efforts. Many applications require domain/data specific custom knowledge graphs. Creating schema with class structure and constraints for each KG is difficult.
42 How to create a knowledge graph from unstructured text?
43 Jonathon Watson works at IBM. He has more than 50 patents, and won best inventor award for his invention Neural Chip by Jon Watson et al.
44 Jonathon Watson works at IBM. He has more than 50 patents, and won best inventor award for his invention Neural Chip by Jon Watson et al. Entity extraction Relation extraction Noise reduction KG Jonathon Watson IBM Jon Watson employedby(jona thon Watson, IBM) Jon Watson Jonathon Watson, Jon Watson
45 Relation extraction Supervised methods Predefined schema (employedby, bornon, BirthPlace ) Training data employedby Jonathon Watson works at IBM. employedby Jonathon Watson joined IBM.
46 Relation extraction Supervised methods Predefined schema (employedby, bornon, BirthPlace ) Training data employedby Test data Jonathon Watson works at IBM. employedby? Jonathon Watson is manager at IBM. Jonathon Watson joined IBM.
47 Relation extraction Supervised methods Predefined schema (employedby, bornon, BirthPlace ) Training data employedby Test data Jonathon Watson works at IBM. employedby employedby Jonathon Watson is manager at IBM. Jonathon Watson joined IBM.
48 Relation extraction Supervised methods Pros: High accuracy and less noise Cons: Hard and expensive to build labeled data
49 Relation extraction Supervised methods Distantly supervised methods Jon Watson works at IBM. employedby (Jon Watson, IBM) affiliated(michael Decker,, SMU) Jon Watson becomes VP at IBM.. Michael Decker joins Data Science group at SMU. Michael Decker won a national funding award at SMU..
50 Relation extraction Supervised methods Distantly supervised methods Training sentences Jon Watson works at IBM. employedby (Jon Watson, IBM) affiliated(michael Decker,, SMU) Jon Watson becomes VP at IBM.. Michael Decker joins Data Science group at SMU. Michael Decker won a national funding award at SMU..
51 Relation extraction Supervised methods Distantly supervised methods Pros: Overcome the effort of labeling data Cons: Dependency of existing knowledge graph and corresponding. text
52 Relation extraction Supervised methods Distantly supervised methods Unsupervised methods (OpenIE, Universal Schema) work Jonathon Watson works at IBM. (ROOT (S (NP (Jon Watson)) (VP (VBZ works) (PP (IN at) (NP IBM))) join Jonathon Watson joined IBM. (ROOT (S (NP (Jon Watson)) (VP (VBD joined) (NP IBM))
53 Relation extraction Supervised methods Distantly supervised methods Unsupervised methods (OpenIE, Universal Schema) Pros: eliminates the effort of labeling data Cons: Noisy, large number of relations
54 Relation extraction Supervised methods Distantly supervised methods Unsupervised methods (OpenIE, Universal Schema) Relation1 Relation2 Relation3 Works employer Company employedby. livesin currentcity Country. Vice President executive Board member.
55 Relation extraction (Universal Schema) employeby affiliated Leader of Jon x x Michael x Steve x x Joyce x x x Clustering using vector similarity Matrix completion and fill the empty values [Yao L. at el., 2012]
56 Entity types identification (Universal Schema) director musician actor Jon x x Michael x x Steve x Joyce x x Clustering using vector similarity Matrix completion and fill the empty values [Yao L. at el., 2012]
57 Relation extraction in domain Supervised methods Need domain experts to label the data Distantly supervised methods Hard to find corresponding text Unsupervised methods (OpenIE, Universal Schema) Noisy Symptom A 59-year-old African American man with a past medical history of hypertension, benign prostatic hypertrophy, type II diabetes mellitus for the past 15 years, and chronic back pain presents to the hospital with gross hematuria. The patient states that he noticed blood in his urine last night. The patient also reports mild, intermittent flank pain. The patient states that his diabetes and blood pressure are well controlled with medications, and that he has managed his chronic back pain with 2 aspirin per day for the past 4 years. Vital signs are Temp F, BP- 124/82 mm/hg, pulse- 88/min, and RR- 14/min. Blood work is notable for HbA1C of 6.5%. A pyelogram reveals a ring sign. His current fasting glucose is 140mmol/L.<br /><br />What is the most likely etiology of hematuria in this patient?
58 Knowledge graphs in domain Domain specific entity extraction is more challenging Limited relation types Less explicit mention of entity and relation types in text Creating simple schema requires domain experts
59 Knowledge graph - Simple Jonathon Watson works at IBM. Jonathon Watson Michael Decker joined IBM. SMU Michael Decker IBM Michael Decker attends SMU
60 Knowledge graph Simple + Schema Jonathon Watson works at IBM. Jonathon Watson affiliated Michael Decker joins IBM. SMU affiliated Michael Decker IBM affiliated Michael Decker attended SMU
61 Knowledge graph Simple + Schema + Ontology Domain, range, constraint Jonathon Watson works at IBM. Jonathon Watson affiliated Michael Decker joined IBM. SMU affiliated Michael Decker IBM affiliated Michael Decker attends SMU
62 Summary Simple knowledge graph works for many applications Identify the requirement before finding the solution. Many knowledge graphs are publically available
63 e
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