1) The document discusses using distributional semantics to build robust semantic models that can handle large amounts of data and enable semantic computing at scale. 2) It describes how distributional semantic models can be used to represent word meanings based on their linguistic contexts, allowing semantic knowledge bases to be automatically constructed from large text corpora. 3) The author proposes a schema-agnostic approach using distributional semantics to enable querying databases without prior knowledge of schemas, addressing problems of vocabulary and structural differences between queries and data.

1. Semantics at Scale:
A Distributional Approach
André Freitas
UFRJ
Rio de Janeiro, March 2015

2. Motivation

3. Semantic Computing for coping with the
long tail of data variety
frequency of use
# of entities and attributes
relational NoSQL
schema-less
unstructured
more
knowledge
Full data coverage
Full automation
Full knowledge

4. Structure/Semantics
Unstructured Data Structured Data
Consistent
Comparable
Processable
Easy to generate Easy to analyze
Semantic Computing

5. Distributional
Semantics

6. Robust Semantic Model
 Semantic intelligent behavior is highly dependent on
knowledge scale (commonsense, semantic)
Semantics
=
Formal meaning representation model
(lots of data)
+
inference model
6

7. Robust Semantic Model
 Not scalable!
1st Hard problem: Acquisition
Semantics
=
Formal meaning representation model
(lots of data)
+
inference model
7

8. Robust Semantic Model
 Not scalable!
2nd Hard problem: Consistency
Semantics
=
Formal meaning representation model
(lots of data)
+
inference model
8

9. Robust Semantic Model
 Not scalable!
3rd Hard problem: Performance
Semantics
=
Formal meaning representation model
(lots of data)
+
inference model
9

10.  “Most semantic models have dealt with particular types of
constructions, and have been carried out under very simplifying
assumptions, in true lab conditions.”
 “If these idealizations are removed it is not clear at all that modern
semantics can give a full account of all but the simplest
models/statements.”
Formal World Real World
Baroni et al. 2013
Semantics for a Complex World
10

11. Distributional Semantic Models
 Semantic Model with low acquisition effort
(automatically built from text)
Simplification of the representation
 Enables the construction of comprehensive
commonsense/semantic KBs
 What is the cost?
Some level of noise
(semantic best-effort)
Limited semantic model
11

12. Distributional Hypothesis
“Words occurring in similar (linguistic) contexts tend
to be semantically similar”
 “He filled the wampimuk with the substance, passed it
around and we all drunk some”
12 McDonald & Ramscar, 2001Baroni & Boleda, 2010Harris, 1954

13. Distributional Semantic Models (DSMs)
“The dog barked in the park. The owner of the dog put him on the
leash since he barked.”
contexts = nouns and verbs in the same
sentence
13

14. Distributional Semantic Models (DSMs)
“The dog barked in the park. The owner of the dog put him on the
leash since he barked.”
bark
dog
park
leash
contexts = nouns and verbs in the same
sentence
bark : 2
park : 1
leash : 1
owner : 1
14

15. Semantic Relatedness
car
dog
bark
run
leash
15

16. Semantic Relatedness
θ
car
dog
cat
bark
run
leash
16

17. DSMs as Commonsense Reasoning
θ
car
dog
cat
bark
run
leash
17

18. First Application
Schema-agnostic
Query Approach

19. Shift in the Database Landscape
 Very-large and dynamic “schemas”.
10s-100s attributes
1,000s-1,000,000s attributes
before 2000
circa 2015
19 Brodie & Liu, 2010

20. Databases for a Complex World
How do you query data on this scenario?
20

21. Schema-agnosticism
Abstraction
Layer
21
Who is the daughter
of Bill Clinton?
Bill
Clinton
Chelsea
Clinton
child

22. Vocabulary Problem for Databases
Who is the daughter of Bill Clinton married to?
Semantic Gap Schema-agnostic
query mechanisms
 Abstraction level differences
 Lexical variation
 Structural (compositional) differences
22

23. Proposed Approach
Who is the daughter of Bill Clinton married to?
 Abstraction level differences
 Lexical variation
 Structural (compositional) differences
23

24. Ƭ-Space: Hybrid Distributional-Relational
Semantic Model
24
A Distributional Structured Semantic Space for
Querying RDF Graph Data, IJSC 2012

25. Approach Overview
Query Planner
Ƭ
Large-scale
unstructured data
Database
Query AnalysisSchema-agnostic
Query
Query Features
Query Plan
25

26. Addressing the Vocabulary Problem for
Databases (with Distributional Semantics)
Gaelic: direction
26

27. Dataset
Dataset (DBpedia 3.6 + YAGO classes):
45,768 properties
288,316 classes
9,434,677 instances
128,071,259 triples
27

28. Simple Queries (Video)
28

29. More Complex Queries (Video)
29

30. Treo Answers Jeopardy Queries (Video)
http://bit.ly/1hWcch9

31. Relevance
31

32. Comparative Analysis
 Better recall and query coverage compared to baselines with
equivalent precision.
 More comprehensive semantic matching.
32

33. Distributional Semantics vs WordNet
 Distributional semantics provides a more comprehensive
semantic matching
33
A Distributional Approach for Terminological Semantic Search on the Linked Data
Web, ACM SAC, 2012

34. Large-scale Querying
frequency of use
# of entities and attributes
relational NoSQL
schema-less
unstructured
Schema-agnostic querying

35. Schema-agnostic Database will be
released in April 2015

36. Large-Scale Graph Extraction

37. Relation/Graph Extraction
 Now that we are schema-agnostic ...
 From Text to Knowledge Graph
 Relations + Context + Entity Linking
 Ontology-agnostic
 RDF serialization

38. Relation/Graph Extraction
In 2002, GE acquired the wind power assets of Enron.In 2002 GE acquired the wind power assets of Enron

39. Relation/Graph Extraction
General Electric Company, or GE , is an American multinational conglomerate
corporation incorporated in Schenectady , New York
A Semantic Best-Effort Approach for Extracting Structured
Discourse Graphs from Wikipedia, WoLE 2012

40. Large-scale Extraction
frequency of use
# of entities and attributes
relational NoSQL
schema-less
unstructured
Large-scale Graph Extraction

41. Approximate &
Selective Reasoning

42. Commonsense Reasoning
 Coping with KB incompleteness
- Supporting semantic approximation
 Selective (focussed) reasoning
- Selecting the relevant facts in the context of the inference
Acquisition
Scalability
Strategy: Using distributional semantics to solve both the acquisition
and scalability problems
42

43. Commonsense Reasoning
43
John Smith EngineerInstance-level
occupation
Does John Smith have a degree?

44. Commonsense Reasoning
44
John Smith EngineerInstance-level
occupation
Engineer learn
subjectof
Does John Smith have a degree?
Commonsense
KB

45. Selective Reasoning
45
John Smith EngineerInstance-level
occupation
Engineer learn
subjectof
memorization
is a
Does John Smith have a degree?
Commonsense
KB
Selective reasoning

46. Commonsense Reasoning
46
John Smith EngineerInstance-level
occupation
Engineer learn
subjectof
memorization
is a
educationhave or
involve
Does John Smith have a degree?
Commonsense
KB

47. Commonsense Reasoning
47
John Smith EngineerInstance-level
occupation
Engineer learn
subjectof
memorization
is a
education
have or
involve
university at location
Does John Smith have a degree?
Commonsense
KB

48. Coping with Incompleteness
48
John Smith EngineerInstance-level
occupation
Engineer learn
subjectof
memorization
is a
education
have or
involve
university at locationcollege
Does John Smith have a degree?
Commonsense
KB
Coping with KB
Incompleteness

49. Commonsense Reasoning
Does John Smith have a degree?
49
John Smith EngineerInstance-level
occupation
Engineer learn
subjectof
memorization
is a
education
have or
involve
university at locationcollege
degreegives
Commonsense
KB
A Distributional Semantics Approach for Selective Reasoning on
Commonsense Graph Knowledge Bases, NLDB 2014.

50. Programming in a Schema-agnostic World
50
Towards An Approximative Ontology-Agnostic Approach for Logic
Programs, FOIKS 2014.
Semantics at Scale: When Distributional Semantics meets Logic
Programming, ALP Newsletter, 2014

51. Programming in a Schema-agnostic World
frequency of use
# of entities and attributes
relational NoSQL
schema-less
unstructured
Schema-agnostic programs

52. Concluding Remarks

53.  Existing semantic technologies can address today major data
management problems
 Muiti-disciplinarity is one key:
- NLP + IR + Semantic Web + Databases
 Schema-agnosticism is a central property/functionality/goal!
 Distributional Semantics + semantics of structured data =
schema-agnosticism
 Schema-agnosticism brings major impact for information systems.
 We can tame the long tail of data variety!
 The wave is just starting. Be a part of it!
Take-away Message
53

54. Want to play with Distributional
Semantics?
http://easy-esa.org
54