The document discusses the development of a distributional semantic web stack, focusing on the challenges of acquiring and maintaining semantic knowledge and the relevance of distributional semantic models (DSMs) and distributional-relational models (DRMs). It highlights the benefits of automatic construction of semantic models from unstructured data and their applications in commonsense reasoning and semantic approximation. The authors aim to bridge various communities to enhance semantic intelligent behavior using these models.

1. Towards a Distributional Semantic Web
Stack
André Freitas, Edward Curry, Siegfried Handschuh
Insight Centre for Data Analytics
University of Passau
URSW 2014
Riva del Garda

2.  Position paper
 Model targeting semantic approximations
(from a praxis perspective)
 Interested in collecting references / creating
bridges with this community
2

3. Outline
 Motivation
 Distributional Semantic Models (DSMs)
 Distributional-Relational Models (DRMs)
 Applications
 Take-away message
3

4. Motivation
 Semantic intelligent behaviour is highly dependent on
(commonsense, semantic) knowledge scale
Semantics
=
Formal meaning representation model
(lots of data)
+
inference model
4

5. Motivation
 Scalability problems
1st Hard problem: Acquisition
Semantics
=
Formal meaning representation model
(lots of data)
+
inference model
5

6. Motivation
 Scalability problems
2nd Hard problem: Consistency
Semantics
=
Formal meaning representation model
(lots of data)
+
inference model
6

7. Semantics for a Complex World
 “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.”
Baroni et al. 2013
7

8. Distributional Semantic Models
 Semantic Model with low acquisition effort
(automatically built from text)
Simplification of the representation
(vector-based)
 Enables the construction of comprehensive
commonsense/semantic KBs
 Trades formal structure for volume of commonsense
knowledge
 What is the cost?
Some level of noise
(semantic best-effort)
8

9. 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
9

10. 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
10

11. 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
11

12. Distributional Semantic Models (DSMs)
car
dog
bark
run
leash
12

13. Semantic Similarity & Relatedness
dog
car
bark
run
leash
13
Query: cat

14. Semantic Similarity & Relatedness
θ
dog
cat
car
bark
run
leash
14
Query: cat

15. Distributional Semantic Models (DSMs)

16. DSMs as Commonsense Reasoning
Commonsense data is here
θ
car
dog
cat
bark
run
leash
Semantic Approximation is here
16

17. Distributional-Relational Models (DRMs)
 Hybrid distributional + structured data
 Semantic approximation as a first-class citizen
 Structured data + user query provides a contextual
support for the semantic approximation
17

18. Distributional-Relational Models (DRMs)
Your Algorithm
goes here
DRM
Structured Data
Text Collection
Distributional
Semantic
Model
Heuristics to minimize
the approximation
errors
18

19. Distributional-Relational Models (DRMs)
Your Algorithm
goes here
DRM
Structured Data
Structured Data (the
same or another one)
Distributional
Semantic
Model
Heuristics to minimize
the approximation
errors
19

20. DRM
20

21. Application: Flexible Querying / Semantic
Search
Freitas et al., ICSC 2011 Freitas & Curry, IUI 2014

22. Application: Selective Reasoning (1)
 Speer et al. AAAI 2009
 Freitas et al, NLDB 2014
22

23. Application: Distributional Semantics and
Logic Programming
 Pereira da Silva & Freitas, FOIKS 2014
23

24. Application: Knowledge Discovery
 Entity similarity/Entity consolidation
 Relationship discovery
 Novacek et al. ISWC 2011
 Cohen et al. T. AMIA Annu Symp 2009
 Speer et al. AAAI 2009
24

25. Distributional Semantics /
Semantic Web Stack?
25

26. 26

27. Take-away message
Effective semantic approximation that works
+
Automatic construction of comprehensive semantic
models from unstructured data
+
Simple to use
 Powerful semantic pattern in practice.
27

28. Do-it-yourself
http://easy-esa.org
28