https://www.youtube.com/watch?v=uBijGs1NJCE&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=13 Semantic and AI research communities have a strong body of work focuses on extracting facts from the web automatically and represent them in a graph based representation. NELL and Knowledge Vault are two prominent knowledge graphs of that kind. However, due to the inherent noise of the web the resulting knowledge also contain noisy data. With the huge volume of the facts extracted from the web, it is impractical to use traditional reasoning approaches to capture the inconsistencies in these knowledge graphs. This work addresses this issue by using semantics in the form of schema knowledge together with statistics in the form of confidence value of facts derived from information extraction techniques. They use probabilistic soft logic which is a recently introduced statistical learning approach which allows to assign weights to the logical statement and their dependencies. The weighted soft logic rules are represented in a probabilistic graphical model with their dependencies to identify the different interpretations of a KG and pick the most consistent KG. References Pujara, Jay, et al. "Using Semantics and Statistics to Turn Data into Knowledge." AI Magazine 36.1 (2015): 65-74. Pujara, Jay, et al. "Knowledge graph identification." International Semantic Web Conference. Springer Berlin Heidelberg, 2013. Lise Getoor “Combining Statistics and Semantics to Turn Data into Knowledge” ESWC Keynote 2015

1. 1
Using Semantics and Statistics to
Turn Data into Knowledge
Based on: Pujara, Jay, et al. "Using Semantics and Statistics to Turn Data into Knowledge." AI
Magazine 36.1 (2015): 65-74.
And
Pujara, Jay, et al. "Knowledge graph identification." International Semantic Web Conference.
Springer Berlin Heidelberg, 2013.
Presented By: Sarasi Lalithsena
Semantic-Cognitive-Perceptual Computing Class -
Summer 2016

2. Knowledge graphs (KGs)
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A Graph representation of facts where entities are connected by relationships
Image credit: http://searchengineland.com/laymans-visual-
guide-googles-knowledge-graph-search-api-241935
Google knowledge graph NELL
Mitchell, T, et al. AAAI 2015

3. Automatic Knowledge Graph Construction
Existing work on KG construction can be categorized broadly into
these groups,
• build on Wikipedia infoboxes and other structured data sources -
YAGO, DBpedia, Freebase, WikiData
• extract information from the entire web but uses a fixed
ontology/schema - NELL, Knowledge Vault
• extract information from the entire web but does not use a
schema - Reverb, OLLIE
• construct taxonomies - Probase
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4. Automatic Knowledge Graph Extraction
Jay Pujara, Hui Miao, Lise Getoor, William Cohen, "Knowledge Graph Identification", Research Talk ISWC 2013 4

5. Challenge
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Jay Pujara, Hui Miao, Lise Getoor, William Cohen, "Knowledge Graph Identification", Research Talk ISWC 2013

6. Example of NELL errors
• Entity co-reference error
Jay Pujara, Hui Miao, Lise Getoor, William Cohen, "Knowledge Graph Identification", Research Talk ISWC 2013 6

7. Example of NELL errors
• Missing and incorrect types (labels)
Jay Pujara, Hui Miao, Lise Getoor, William Cohen, "Knowledge Graph Identification", Research Talk ISWC 2013 7

8. Example of NELL errors
• Missing and incorrect relations
Jay Pujara, Hui Miao, Lise Getoor, William Cohen, "Knowledge Graph Identification", Research Talk ISWC 2013 8

9. Violation of the schema knowledge
• Equivalence of co-referent entities (owl:sameAs)
– sameEntity(Kyrgyzstan, Kyrgyz Republic)
• Mutual exclusion of types (disjoint)
– MUT(country, bird)
• Constraint on relations (domain and range)
– LocatedIn(country, continent)
Requires reasoning jointly over the candidates
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Jay Pujara, Hui Miao, Lise Getoor, William Cohen, "Knowledge Graph Identification", Research Talk ISWC 2013

10. Problem Revisited
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Jay Pujara, Hui Miao, Lise Getoor, William Cohen, "Knowledge Graph Identification", Research Talk ISWC 2013

11. Approach – In a nutshell
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Jay Pujara, Hui Miao, Lise Getoor, William Cohen, "Knowledge Graph Identification", Research Talk ISWC 2013

12. Approach – In a nutshell
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Jay Pujara, Hui Miao, Lise Getoor, William Cohen, "Knowledge Graph Identification", Research Talk ISWC 2013

13. Approach – In a nutshell
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Jay Pujara, Hui Miao, Lise Getoor, William Cohen, "Knowledge Graph Identification", Research Talk ISWC 2013

14. Approach – In a nutshell
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Jay Pujara, Hui Miao, Lise Getoor, William Cohen, "Knowledge Graph Identification", Research Talk ISWC 2013

15. Approach – Probabilistic Soft Logic (PSL)
Statistical Learning Approach
• Capture both the structure of the knowledge graph and the
logical dependencies between the facts
• Unlike traditional reasoning systems, it can treat ontological
constraints as weighted rules using them as hints
• Can be specified using predicates and rules written in first-
order logic syntax and translated into a probabilistic graphical
model.
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16. Approach: Probabilistic Soft Logic
• A PSL model is composed of a set of weighted, first-order logic
rules, where each rule defines a set of features
• PSL associates a truth value for each ground rule
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Probabilistic Soft Logic Rule
w is the weight of the rule

17. Approach: Probabilistic Soft Logic
• Fact extraction from can be done with multiple extractors –
Structural elements, Pattern-based classifiers
WCR-T: CandRELT(E1, E2, R) => REL(E1, E2, R)
WCL-T: CandLBLT(E, L) => LBL(E, L)
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Every fact generated by each extractor has a weight

18. Approach: Probabilistic Soft Logic
• Incorporate co-reference entities
Uses soft logic formulation
Truth value is relaxed to [0,1] intervals
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Pujara, Jay, et al. "Using Semantics and Statistics to Turn Data into Knowledge." AI Magazine 36.1 (2015): 65-74.

19. Approach: Probabilistic Soft Logic
• Incorporate schema constraints
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Jian et al., ICDM 2012

20. Approach: Putting all together
Pujara, Jay, et al. "Using Semantics and Statistics to Turn Data into Knowledge." AI Magazine 36.1 (2015): 65-74. 20
Represent it in a graphical model – Each possible fact is a variable;
dependencies exist between facts

21. Approach: Putting all together
• Each ground rule has a weighted distance to satisfaction
derived from the formula’s truth value
• Out of all possible KGs, it find the best KG using the joint
distribution
• Uses convex function to deal with the scalability
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Rule SatisfactionWeighted distance
Joint probability distribution over all variables in a KG

22. Evaluation – NELL experiments
• Full KG from uncertain extractions
Baseline: NELL with ontology consistence
Compare this to the KG created with PSL
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Running time for completes in 130 minutes for 4.3 M facts for PSL
approach

23. Conclusions
• Probabilistic soft logic looks like a really interesting tool to
combine statistics and semantic
• It works well to identify a accurate KG from a noisy KG
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Thank You !!!!!