towards a distributional semantic web stack

Towards a Distributional Semantic Web Stack

André Freitas, Edward Curry, Siegfried HandschuhInsight Centre for Data Analytics

University of Passau

URSW 2014

Riva del Garda

Position paper

Model targeting semantic approximations

(from a praxis perspective)

Interested in collecting references / creating bridges with this community

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Outline

Motivation

Distributional Semantic Models (DSMs)

Distributional-Relational Models (DRMs)

Applications

Take-away message

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Motivation Semantic intelligent behaviour is highly dependent

on (commonsense, semantic) knowledge scale

Semantics =

Formal meaning representation model (lots of data)

+ inference model

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Motivation Scalability problems

1st Hard problem: Acquisition

Semantics =

Formal meaning representation model (lots of data)

+ inference model

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Motivation Scalability problems

2nd Hard problem: Consistency

Semantics =

Formal meaning representation model (lots of data)

+ inference model

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“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

Semantics for a Complex World

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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)

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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

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Distributional Semantic Models (DSMs)“The dog barked in the park. The owner of the dog put

him on theleash since he barked.” contexts = nouns and verbs in the

same sentence

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Distributional Semantic Models (DSMs)“The dog barked in the park. The owner of the dog put

him on theleash since he barked.”

bark

dog

park

leash

contexts = nouns and verbs in the same sentence

bark : 2park : 1leash : 1owner : 1

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Distributional Semantic Models (DSMs)

car

dog

bark

run

leash

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Semantic Similarity & Relatedness

car

dog

bark

run

leash

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Query: cat

Semantic Similarity & Relatedness

θ

car

dog

cat

bark

run

leash

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Query: cat

Distributional Semantic Models (DSMs)

DSMs as Commonsense Reasoning

Commonsense data is here

θ

car

dog

cat

bark

run

leash

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Semantic Approximation is here

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

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Your Algorithm goes here

DRM

Text Collection

Structured Data

Distributional Semantic

Model

Distributional-Relational Models (DRMs)

Heuristics to minimize the

approximation errors

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Your Algorithm goes here

DRM

Structured Data (the same or another one)

Structured Data

Distributional Semantic

Model

Distributional-Relational Models (DRMs)

Heuristics to minimize the

approximation errors

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DRM

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Application: Flexible Querying / Semantic Search

Freitas et al., ICSC 2011 Freitas & Curry, IUI 2014

Application: Selective Reasoning (1)

Speer et al. AAAI 2009 Freitas et al, NLDB 2014

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Application: Distributional Semantics and Logic Programming

Pereira da Silva & Freitas, FOIKS 2014

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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

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Distributional Semantics / Semantic Web Stack?

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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.

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Do-it-yourself

http://easy-esa.org

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