Knowledge Expansion over Probabilistic Knowledge Bases. Yang Chen, Daisy Zhe Wang
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1 Knowledge Expansion over Probabilistic Knowledge Bases Yang Chen, Daisy Zhe Wang {yang,daisyw}cise.ufl.edu Computer and Information Science and Engineering University of Florida ProbKB SIGMOD, Snowbird, UT Jun 25, 2014 Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
2 Outline 1 Introduction Knowledge Bases Knowledge Expansion 2 The ProbKB System Probabilistic Knowledge Bases ProbKB Architecture Grounding Quality Control 3 Conclusion Conclusion Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
3 Knowledge Bases A knowledge base is a collection of entities, facts, and relationships that conforms with a certain data model. Allows machines to interpret human information in a principled manner. Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
4 Knowledge Bases A knowledge base is a collection of entities, facts, and relationships that conforms with a certain data model. Allows machines to interpret human information in a principled manner. Figure: Google knowledge graph Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
5 Knowledge Bases A knowledge base is a collection of entities, facts, and relationships that conforms with a certain data model. Allows machines to interpret human information in a principled manner. But they are often incomplete. Figure: Google knowledge graph Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
6 Knowledge Base Construction Review 1 Human collaboration: DBPedia, Freebase, Google Knowledge Graph, YAGO. 2 Automatic construction: DeepDive, Knowledge Vault, Nell, OpenIE, ProBase, YAGO. 3 Knowledge integration: Knowledge Fusion, Knowledge Vault, PIDGIN. Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
7 Inferring Implicit Information Kale is rich in Calcium Calcium helps prevent Osteoporosis Kale helps prevent Osteoporosis. Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
8 Inferring Implicit Information Kale is rich in Calcium Calcium helps prevent Osteoporosis Kale helps prevent Osteoporosis. Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
9 Inferring Implicit Information IsHeadquarteredIn(Company, State) :- IsBasedIn(Company, City) IsLocatedIn(City, State); Contains(Food, Chemical) :- IsMadeFrom(Food, Ingredient) Contains(Ingredient, Chemical); Reduce(Medication, Factor) :- KnownGenericallyAs(Medication, Drug) Reduce(Drug, Factor); ReturnTo(Writer, Place) :- Make(Company1, Device) :- BornIn(Writer, City) CapitalOf(City, Place); Buy(Company1, Company2) Make(Company2, Device); Figure: Sherlock Horn clauses learner. Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
10 Knowledge Expansion Problem Contributions Inferring implicit knowledge in KBs. Efficiency. We use DBMSes to model knowledge bases; We design a SQL-based algorithm to apply inference rules in batches; We use MPP databases to parallelize the inference process. Quality. We identify major error sources and combine state-of-the-art methods to detect and recover from errors; We use semantic constraints to identify errors and ambiguities; We clean the rule set based on their statistical properties. Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
11 Knowledge Expansion Problem Contributions Inferring implicit knowledge in KBs. Efficiency. We use DBMSes to model knowledge bases; We design a SQL-based algorithm to apply inference rules in batches; We use MPP databases to parallelize the inference process. Quality. We identify major error sources and combine state-of-the-art methods to detect and recover from errors; We use semantic constraints to identify errors and ambiguities; We clean the rule set based on their statistical properties. Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
12 Knowledge Expansion Problem Contributions Inferring implicit knowledge in KBs. Efficiency. We use DBMSes to model knowledge bases; We design a SQL-based algorithm to apply inference rules in batches; We use MPP databases to parallelize the inference process. Quality. We identify major error sources and combine state-of-the-art methods to detect and recover from errors; We use semantic constraints to identify errors and ambiguities; We clean the rule set based on their statistical properties. Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
13 Knowledge Expansion Problem Contributions Inferring implicit knowledge in KBs. Efficiency. We use DBMSes to model knowledge bases; We design a SQL-based algorithm to apply inference rules in batches; We use MPP databases to parallelize the inference process. Quality. We identify major error sources and combine state-of-the-art methods to detect and recover from errors; We use semantic constraints to identify errors and ambiguities; We clean the rule set based on their statistical properties. Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
14 Knowledge Expansion Problem Contributions Inferring implicit knowledge in KBs. Efficiency. We use DBMSes to model knowledge bases; We design a SQL-based algorithm to apply inference rules in batches; We use MPP databases to parallelize the inference process. Quality. We identify major error sources and combine state-of-the-art methods to detect and recover from errors; We use semantic constraints to identify errors and ambiguities; We clean the rule set based on their statistical properties. Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
15 Knowledge Expansion Problem Contributions Inferring implicit knowledge in KBs. Efficiency. We use DBMSes to model knowledge bases; We design a SQL-based algorithm to apply inference rules in batches; We use MPP databases to parallelize the inference process. Quality. We identify major error sources and combine state-of-the-art methods to detect and recover from errors; We use semantic constraints to identify errors and ambiguities; We clean the rule set based on their statistical properties. Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
16 Knowledge Expansion Problem Contributions Inferring implicit knowledge in KBs. Efficiency. We use DBMSes to model knowledge bases; We design a SQL-based algorithm to apply inference rules in batches; We use MPP databases to parallelize the inference process. Quality. We identify major error sources and combine state-of-the-art methods to detect and recover from errors; We use semantic constraints to identify errors and ambiguities; We clean the rule set based on their statistical properties. Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
17 Knowledge Expansion Problem Contributions Inferring implicit knowledge in KBs. Efficiency. We use DBMSes to model knowledge bases; We design a SQL-based algorithm to apply inference rules in batches; We use MPP databases to parallelize the inference process. Quality. We identify major error sources and combine state-of-the-art methods to detect and recover from errors; We use semantic constraints to identify errors and ambiguities; We clean the rule set based on their statistical properties. Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
18 Knowledge Expansion Problem Contributions Inferring implicit knowledge in KBs. Efficiency. We use DBMSes to model knowledge bases; We design a SQL-based algorithm to apply inference rules in batches; We use MPP databases to parallelize the inference process. Quality. We identify major error sources and combine state-of-the-art methods to detect and recover from errors; We use semantic constraints to identify errors and ambiguities; We clean the rule set based on their statistical properties. Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
19 Outline 1 Introduction Knowledge Bases Knowledge Expansion 2 The ProbKB System Probabilistic Knowledge Bases ProbKB Architecture Grounding Quality Control 3 Conclusion Conclusion Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
20 Probabilistic Knowledge Bases Example (Probabilistic Knowledge Bases) We define a probabilistic knowledge base to be a 5-tuple Γ = (E, C, R, Π, L): Entities E Classes C Relations R Ruth Gruber, New York City, Brooklyn W (Writer) = {Ruth Gruber}, C (City) = {New York City}, P (Place) = {Brooklyn} born in(w, P ), born in(w, C), live in(w, P ), live in(w, C), locate in(p, C) Facts Π 0.93 born in(ruth Gruber, Brooklyn) 0.96 born in(ruth Gruber, New York City) Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
21 Probabilistic Knowledge Bases Example (ReVerb-Sherlock KB Cont.) Rules L 1.40 x W y P (live in(x, y) born in(x, y)) 1.53 x W y C (live in(x, y) born in(x, y)) 2.68 x W y P (grow up in(x, y) born in(x, y)) 0.74 x W y C (grow up in(x, y) born in(x, y)) 0.32 x P y C z W (locate in(x, y) live in(z, x) live in(z, y)) 0.52 x P y C z W (locate in(x, y) born in(z, x) born in(z, y)) x C y C z W (born in(z, x) born in(z, y) x = y) Table: Probabilistic KB from ReVerb-Sherlock. Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
22 MLN: The State-of-the-Art DB Program MLN (Weighted rules) Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
23 ProbKB In-Database Architecture Inference Engine (e.g., GraphLab) RDMBS Factor Graph SQL UDF/UDA Query Optimizer & Execution Engine MLN Entities Facts Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
24 ProbKB In-Database Architecture Inference Engine (e.g., GraphLab) RDMBS Factor Graph SQL UDF/UDA Query Optimizer & Execution Engine MLN Entities Facts Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
25 Relational ProbKB born in(ruth Gruber, Brooklyn) 0.93 born in(ruth Gruber, New York City) 0.96 Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
26 Relational ProbKB born in(ruth Gruber, Brooklyn) 0.93 born in(ruth Gruber, New York City) 0.96 I R x C 1 y C 2 w 1 born in RG W Br P born in RG W NYC C 0.96 T Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
27 Relational ProbKB Definition Two first-order clauses are defined to be structurally equivalent if they differ only in the entities, classes, and relations symbols. x W y P (live in(x, y) born in(x, y)) 1.40 x W y C (live in(x, y) born in(x, y)) 1.53 x W y P (grow up in(x, y) born in(x, y)) 2.68 x W y C (grow up in(x, y) born in(x, y)) 0.74 R 1 R 2 C 1 C 2 w live in born in W P 1.40 live in born in W C 1.53 grow up in born in W P 2.68 grow up in born in W C 0.74 M 1 Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
28 Relational ProbKB Definition Two first-order clauses are defined to be structurally equivalent if they differ only in the entities, classes, and relations symbols. x W y P (live in(x, y) born in(x, y)) 1.40 x W y C (live in(x, y) born in(x, y)) 1.53 x W y P (grow up in(x, y) born in(x, y)) 2.68 x W y C (grow up in(x, y) born in(x, y)) 0.74 R 1 R 2 C 1 C 2 w live in born in W P 1.40 live in born in W C 1.53 grow up in born in W P 2.68 grow up in born in W C 0.74 M 1 Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
29 Relational ProbKB Definition Two first-order clauses are defined to be structurally equivalent if they differ only in the entities, classes, and relations symbols. x P y C z W (locate in(x, y) live in(z, x) live in(z, y)) 0.32 x P y C z W (locate in(x, y) born in(z, x) born in(z, y)) 0.52 R 1 R 2 R 3 C 1 C 2 C 3 w located in live in live in P C W 0.32 located in born in born in P C W 0.52 M 3 Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
30 Relational ProbKB Definition Two first-order clauses are defined to be structurally equivalent if they differ only in the entities, classes, and relations symbols. x P y C z W (locate in(x, y) live in(z, x) live in(z, y)) 0.32 x P y C z W (locate in(x, y) born in(z, x) born in(z, y)) 0.52 R 1 R 2 R 3 C 1 C 2 C 3 w located in live in live in P C W 0.32 located in born in born in P C W 0.52 M 3 Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
31 Grounding SELECT M1.R1 AS R, T.x AS x, T.C1 AS C1, T.y AS y, T.C2 AS C2 FROM M1 JOIN T ON M1.R2 = T.R AND M1.C1 = T.C1 AND M1.C2 = T.C2; T I R x C 1 y C 2 w 1 born in RG W Br P born in RG W NYC C live in RG W NYC C 4 grow up in RG W NYC C 5 live in RG W Br P 6 grow up in RG W Br P M 1 R 1 R 2 C 1 C 2 w live in born in W P 1.40 live in born in W C 1.53 grow up in born in W P 2.68 grow up in born in W C 0.74 Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
32 Grounding SELECT M1.R1 AS R, T.x AS x, T.C1 AS C1, T.y AS y, T.C2 AS C2 FROM M1 JOIN T ON M1.R2 = T.R AND M1.C1 = T.C1 AND M1.C2 = T.C2; T I R x C 1 y C 2 w 1 born in RG W Br P born in RG W NYC C live in RG W NYC C 4 grow up in RG W NYC C 5 live in RG W Br P 6 grow up in RG W Br P M 1 R 1 R 2 C 1 C 2 w live in born in W P 1.40 live in born in W C 1.53 grow up in born in W P 2.68 grow up in born in W C 0.74 Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
33 Grounding SELECT M1.R1 AS R, T.x AS x, T.C1 AS C1, T.y AS y, T.C2 AS C2 FROM M1 JOIN T ON M1.R2 = T.R AND M1.C1 = T.C1 AND M1.C2 = T.C2; T I R x C 1 y C 2 w 1 born in RG W Br P born in RG W NYC C live in RG W NYC C 4 grow up in RG W NYC C 5 live in RG W Br P 6 grow up in RG W Br P M 1 R 1 R 2 C 1 C 2 w live in born in W P 1.40 live in born in W C 1.53 grow up in born in W P 2.68 grow up in born in W C 0.74 Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
34 Grounding SELECT M1.R1 AS R, T.x AS x, T.C1 AS C1, T.y AS y, T.C2 AS C2 FROM M1 JOIN T ON M1.R2 = T.R AND M1.C1 = T.C1 AND M1.C2 = T.C2; T I R x C 1 y C 2 w 1 born in RG W Br P born in RG W NYC C live in RG W NYC C 4 grow up in RG W NYC C 5 live in RG W Br P 6 grow up in RG W Br P M 1 R 1 R 2 C 1 C 2 w live in born in W P 1.40 live in born in W C 1.53 grow up in born in W P 2.68 grow up in born in W C 0.74 Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
35 Grounding SELECT M1.R1 AS R, T.x AS x, T.C1 AS C1, T.y AS y, T.C2 AS C2 FROM M1 JOIN T ON M1.R2 = T.R AND M1.C1 = T.C1 AND M1.C2 = T.C2; T I R x C 1 y C 2 w 1 born in RG W Br P born in RG W NYC C live in RG W NYC C 4 grow up in RG W NYC C 5 live in RG W Br P 6 grow up in RG W Br P M 1 R 1 R 2 C 1 C 2 w live in born in W P 1.40 live in born in W C 1.53 grow up in born in W P 2.68 grow up in born in W C 0.74 Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
36 Grounding SELECT M3.R1 AS R, T2.y AS x, T2.C2 AS C1, T3.y AS y, T3.C2 AS C2 FROM M3 JOIN T T2 ON M3.R2 = T2.R AND M3.C3 = T2.C1 AND M3.C1 = T2.C2 JOIN T T3 ON M3.R3 = T3.R AND M3.C3 = T3.C1 AND M3.C2 = T3.C2 WHERE T2.x = T3.x; T I R x C 1 y C 2 w 1 born in RG W Br P born in RG W NYC C live in RG W NYC C 4 grow up in RG W NYC C 5 live in RG W Br P 6 grow up in RG W Br P 7 located in Br P NYC C M 3 R 1 R 2 R 3 C 1 C 2 C 3 w located in live in live in P C W 0.32 located in born in born in P C W 0.52 Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
37 Grounding SELECT M3.R1 AS R, T2.y AS x, T2.C2 AS C1, T3.y AS y, T3.C2 AS C2 FROM M3 JOIN T T2 ON M3.R2 = T2.R AND M3.C3 = T2.C1 AND M3.C1 = T2.C2 JOIN T T3 ON M3.R3 = T3.R AND M3.C3 = T3.C1 AND M3.C2 = T3.C2 WHERE T2.x = T3.x; T I R x C 1 y C 2 w 1 born in RG W Br P born in RG W NYC C live in RG W NYC C 4 grow up in RG W NYC C 5 live in RG W Br P 6 grow up in RG W Br P 7 located in Br P NYC C M 3 R 1 R 2 R 3 C 1 C 2 C 3 w located in live in live in P C W 0.32 located in born in born in P C W 0.52 Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
38 Grounding SELECT M3.R1 AS R, T2.y AS x, T2.C2 AS C1, T3.y AS y, T3.C2 AS C2 FROM M3 JOIN T T2 ON M3.R2 = T2.R AND M3.C3 = T2.C1 AND M3.C1 = T2.C2 JOIN T T3 ON M3.R3 = T3.R AND M3.C3 = T3.C1 AND M3.C2 = T3.C2 WHERE T2.x = T3.x; T I R x C 1 y C 2 w 1 born in RG W Br P born in RG W NYC C live in RG W NYC C 4 grow up in RG W NYC C 5 live in RG W Br P 6 grow up in RG W Br P 7 located in Br P NYC C M 3 R 1 R 2 R 3 C 1 C 2 C 3 w located in live in live in P C W 0.32 located in born in born in P C W 0.52 Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
39 Grounding SELECT M3.R1 AS R, T2.y AS x, T2.C2 AS C1, T3.y AS y, T3.C2 AS C2 FROM M3 JOIN T T2 ON M3.R2 = T2.R AND M3.C3 = T2.C1 AND M3.C1 = T2.C2 JOIN T T3 ON M3.R3 = T3.R AND M3.C3 = T3.C1 AND M3.C2 = T3.C2 WHERE T2.x = T3.x; T I R x C 1 y C 2 w 1 born in RG W Br P born in RG W NYC C live in RG W NYC C 4 grow up in RG W NYC C 5 live in RG W Br P 6 grow up in RG W Br P 7 located in Br P NYC C M 3 R 1 R 2 R 3 C 1 C 2 C 3 w located in live in live in P C W 0.32 located in born in born in P C W 0.52 Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
40 Grounding Efficiency Sherlock-ReVerb KB Tuffy State-of-the-art MLN inference engine; ReVerb 400K extracted facts from web text corpus; Sherlock 31K inference rules learned from ReVerb. # relations 82,768 # rules 30,912 # entities 277,216 # facts 407,247 Table: Sherlock-ReVerb KB statistics Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
41 Grounding Efficiency Sherlock-ReVerb KB Systems Load Round 1 Round 2 Round 3 Round 4 ProbKB-p ProbKB Tuffy-T # records 396K 420K 456K 580K 1.5M Table: ReVerb-Sherlock case study Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
42 Grounding Efficiency Synthetic KBs Execution time/ 10 3 s Tuffy T ProbKB ProbKB p # Inferred (a) # Rules/ # Inferred facts/ 10 6 (a) Varying # Rules: 311xspeedup Execution time/ 10 3 s Tuffy T ProbKB ProbKB p # Inferred (b) # Facts/ # Inferred facts/ 10 6 (b) Varying # Facts: 237xspeedup Execution time/ 10 3 s ProbKB ProbKB pn ProbKB p # Inferred (c) # Facts/ # Inferred facts/ 10 6 (c) MPP Improvements: 6.3x-speedup Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
43 Quality Control Inference Errors capital of(berlin, Germany) born in(freud, Berlin) hub of(berlin, Germany) born in(freud, Germany) capital of(baltimore, Germany) born in(mandel, Berlin) located in(baltimore, Berlin) born in(mandel, Baltimore) born in(freud, Baltimore) live in(rothman, Germany) born in(freud, Berlin) born in(rothman, Baltimore) live in(rothman, Baltimore) Incorrect extractions; Incorrect rules; Ambiguous entities; Propagated errors. Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
44 Quality Control Semantic constraints and ambiguity detection: Functional Relations Violating Facts Ambiguous Entities born in grow up in located in capital of born in(mandel, Berlin) born in(mandel, New York City) born in(mandel, Chicago) grow up in(miller, Placentia) grow up in(miller, New York City) grow up in(miller, New Orleans) located in(regional office, Glasgow) located in(regional office, Panama City) located in(regional office, South Bend) capital of(delhi, India) capital of(calcutta, India) Leonard Mandel Johnny Mandel Tom Mandel (futurist) Dustin Miller Alan Gifford Miller Taylor Miller McCarthy & Stone regional offices OCHA regional offices Indiana Landmarks regional offices (Incorrect extraction) Statistical Rule Cleaning: P (Head(...) Body(...)) P (Head(...)) Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
45 Results Precision of inferred facts No SC RC RC top 20% RC top 10% SC only SC RC top 50% SC RC top 20% (a) Estimated number of correct facts (a) 0.6 higher precision. Ambiguous join keys 24% Incorrect rules 33% (b) (b) Error sources. Ambiguities (detected) 34% Synonyms 1% General types 2% Incorrect extractions 6% Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
46 Outline 1 Introduction Knowledge Bases Knowledge Expansion 2 The ProbKB System Probabilistic Knowledge Bases ProbKB Architecture Grounding Quality Control 3 Conclusion Conclusion Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
47 Conclusion We present ProbKB, a Probabilistic Knowledge Base system. We design a novel relational model and an efficient SQL-based inference algorithm that applies inference rules in batches. We use MPP databases to parallelize the inference process. We combine state-of-the-art data cleaning techniques to improve knowledge quality. Future work will focus on rules and constraints learning. ProbKB Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
48 Related Work Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS OpenIE: Open Information Extraction Sherlock: Learning First-Order Horn Clauses from Web Text sherlock-hornclauses Leibniz: Identifying Functional Relations in Web Text http: //knowitall.cs.washington.edu/leibniz Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
49 Introduction The ProbKB System Conclusion Thank you! Yang Chen: yang at UF: Questions? Knowledge Expansion over Probabilistic Knowledge Bases Jun 25, /28
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