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Transcript of Talking to your Data: Natural Language Interfaces for a schema-less world (Keynote at NLIWoD, ISWC...
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Talking to your Data:
Natural Language Interfaces for a
schema-less world
André Freitas
NLIWoD at ISWC 2014
Riva del Garda
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Outline
Shift in the Database Landscape
On Schema-agnosticism & Semantics
Distributional Semantics to the Help
Case Study: Treo QA System
Living in a Schema-less World
Take-away Message
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Shift in the Database
Landscape
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Big Data (Data Variety)
Vision: More complete data-based picture of the world for
systems and users.
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The Long Tail of Data Variety
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The Long Tail of Data Variety
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Data variety +
Data
Programs
Full data coverage
Full automation
Full knowledge
The Long Tail of Data Variety
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Data variety +
Data
Programs
Full data coverage
Full automation
Full knowledge
The Long Tail of Data Variety
Data generation
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Very-large and dynamic “schemas”
10s-100s attributes1,000s-1,000,000s attributes
circa 2000circa 2014
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Semantic Heterogeneity
Decentralized content generation.
Multiple perspectives (conceptualizations) of the reality.
Ambiguity, vagueness, inconsistency.
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Data variety +
Data
Programs
Full data coverage
Full automation
Full knowledge
The Long Tail of Data Variety
Data generation
Data consumption
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Databases for a Complex World
How do you query data at this scale?
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Schema-agnosticism
Ab
str
ac
tio
n
La
ye
r
User
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First-level independency
(Relational Model)
“… it provides a basis for a high level data language which will yield maximal independence between programs on the one hand and representation and organization of data on the other”
Codd, 1970
Second-level independency
(Schema-agnosticism)
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On Schema-agnosticism
& semantics
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Vocabulary Problem for Databases
Query: Who is the daughter of Bill Clinton married to?
Semantic Gap
Possible representations
Schema-agnostic query
mechanisms
Abstraction level differences
Lexical variation
Structural (compositional) differences
Operational/functional differences
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Robust Semantic Model
Semantic intelligent behaviour is highly dependent on knowledge scale (commonsense, semantic)
Semantics
=
Formal meaning representation model
(lots of data)
+
inference model
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Robust Semantic Model
Not scalable!
1st Hard problem: Acquisition
Semantics
=
Formal meaning representation model
(lots of data)
+
inference model
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Robust Semantic Model
Not scalable!
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.”
Formal World Real World
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
Enables the construction of comprehensive commonsense/semantic KBs
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 itaround 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 the
leash 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 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
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Distributional Semantic Models (DSMs)
car
dog
bark
run
leash
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Context
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Semantic Similarity & Relatedness
car
dog
bark
run
leash
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Query: cat
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Semantic Similarity & Relatedness
θ
car
dog
cat
bark
run
leash
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Query: cat
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DSMs as Commonsense Reasoning
Commonsense is here
θ
car
dog
cat
bark
run
leash
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Semantic Approximation is here
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DSMs as Commonsense Reasoning
θ
car
dog
cat
bark
run
leash
...
vs.
Semantic best-effort
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Case Study: Treo QA
System
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Approach Overview
Query Planner
Ƭ-Space
Large-scale
unstructured data
Commonsense
knowledge
Structured
Data
Distributional
semantics
Core semantic approximation &
composition operations
Query AnalysisQuery Query Features
Query Plan
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Approach Overview
Query Planner
Ƭ-Space
Wikipedia
RDF Data
Explicit Semantic
Analysis (ESA)
Core semantic approximation &
composition operations
Query AnalysisQuery Query Features
Query Plan
Commonsense
knowledge
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Ƭ-Space
e
p
r
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Core Operations
Search &
Composition
Operations
Query
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Does it work?
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Addressing the Vocabulary Problem for
Databases (with Distributional Semantics)
Gaelic: direction
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Solution (Video)
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More Complex Queries (Video)
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Treo Answers Jeopardy Queries (Video)
http://bit.ly/1hWcch939
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Relevance
Test Collection: QALD 2011.
DBpedia.
Dataset (DBpedia + YAGO links): 45,767 predicates, 9,434,677
instances, more than 200,000 classes
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Transform natural language queries into triplepatterns.
“Who is the daughter of Bill Clinton married to?”
Query Pre-Processing
(Question Analysis)
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Step 1: POS Tagging- Who/WP
- is/VBZ
- the/DT
- daughter/NN
- of/IN
- Bill/NNP
- Clinton/NNP
- married/VBN
- to/TO
- ?/.
Query Pre-Processing
(Question Analysis)
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Step 2: Core Entity Recognition- Rules-based: POS Tag + TF/IDF
Who is the daughter of Bill Clinton married to?(PROBABLY AN INSTANCE)
Query Pre-Processing
(Question Analysis)
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Step 3: Determine answer typeRules-based.
Who is the daughter of Bill Clinton married to?(PERSON)
Query Pre-Processing
(Question Analysis)
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Step 4: Dependency parsing- dep(married-8, Who-1)
- auxpass(married-8, is-2)
- det(daughter-4, the-3)
- nsubjpass(married-8, daughter-4)
- prep(daughter-4, of-5)
- nn(Clinton-7, Bill-6)
- pobj(of-5, Clinton-7)
- root(ROOT-0, married-8)
- xcomp(married-8, to-9)
Query Pre-Processing
(Question Analysis)
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Step 5: Determine Partial Ordered Dependency Structure
(PODS)
- Rules based.
• Remove stop words.
• Merge words into entities.
• Reorder structure from core entity position.
Query Pre-Processing
(Question Analysis)
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Bill Clinton daughter married to
(INSTANCE)
ANSWER
TYPE
Person
QUESTION FOCUSLower level of ambiguity,
vagueness, synonimy
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Question Analysis
Transform natural language queries into triplepatterns
“Who is the daughter of Bill Clinton married to?”
Bill Clinton daughter married to
(INSTANCE) (PREDICATE) (PREDICATE) Query Features
PODS
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Query Plan
Map query features into a query plan.
A query plan contains a sequence of core operations.
(INSTANCE) (PREDICATE) (PREDICATE) Query Features
Query Plan
(1) INSTANCE SEARCH (Bill Clinton)
(2) p1 <- SEARCH PREDICATE (Bill Clintion, daughter)
(3) e1 <- NAVIGATE (Bill Clintion, p1)
(4) p2 <- SEARCH PREDICATE (e1, married to)
(5) e2 <- NAVIGATE (e1, p2)
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Instance Search
Bill Clinton daughter married to
:Bill_Clinton
Query:
Linked
Data:
Instance Search
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Predicate Search
Bill Clinton daughter married to
:Bill_Clinton
Query:
Linked
Data::Chelsea_Clinton
:child
:Baptists:religion
:Yale_Law_School
:almaMater
...(PIVOT ENTITY)
(ASSOCIATED
TRIPLES)
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Predicate Search
Bill Clinton daughter married to
:Bill_Clinton
Query:
Linked
Data::Chelsea_Clinton
:child
:Baptists:religion
:Yale_Law_School
:almaMater
...
sem_rel(daughter,child)=0.054
sem_rel(daughter,child)=0.004
sem_rel(daughter,alma mater)=0.001
Which properties are semantically related to ‘daughter’?
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Predicate Search
Bill Clinton daughter married to
:Bill_Clinton
Query:
Linked
Data::Chelsea_Clinton
:child
:Baptists:religion
:Yale_Law_School
:almaMater
...
sem_rel(daughter,child)=0.054
sem_rel(daughter,child)=0.004
sem_rel(daughter,alma mater)=0.001
Which properties are semantically related to ‘daughter’?
(In the context of Bill Clinton)
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Navigate
Bill Clinton daughter married to
:Bill_Clinton
Query:
Linked
Data::Chelsea_Clinton
:child
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Navigate
Bill Clinton daughter married to
:Bill_Clinton
Query:
Linked
Data::Chelsea_Clinton
:child
(PIVOT ENTITY)
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Predicate Search
Bill Clinton daughter married to
:Bill_Clinton
Query:
Linked
Data::Chelsea_Clinton
:child
(PIVOT ENTITY)
:Mark_Mezvinsky
:spouse
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Results
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Core Principles
Minimize the impact of Ambiguity, Vagueness, Synonymy with
semantic pivoting.
Semantic pivoting: Address the simplest matchings first
(heuristics).
Semantic Relatedness as a primitive semantic approximation
operation.
Distributional semantics as commonsense/semantic
knowledge.
Natural Language Queries over Heterogeneous Linked Data Graphs: A Distributional-
Compositional Semantics Approach, IUI 2014
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Living in a
Schema-less World
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How do we build systems today?
Structure the domain
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Generalize and encode some rules
How do we build systems today?
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Allow some constrained interaction
How do we build systems today?
Query is here
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Siloed Systems
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Data variety +
Data
Full data coverage
Full automation
Full knowledge
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Linked Data: Datasets are easier to integrate and to
consume (data model level). However, the semantic
barrier for consumption is still there
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Data variety +
Data
Full data coverage
Full automation
Full knowledge
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Distributional DBMS
Natural Language Queries over Heterogeneous Linked Data Graphs: A Distributional-
Compositional Semantics Approach, IUI 2014
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Data variety +
Data
Full data coverage
Full automation
Full knowledge
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Simplification of Information Extraction
A Semantic Best-Effort Approach for Extracting Structured Discourse Graphs, WoLE, 2012
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Simplification of Information Extraction
General Electric Company, or GE , is an American multinational conglomerate
corporation incorporated in Schenectady , New York
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Data variety +
Data
Full data coverage
Full knowledge
Full automation
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Schema-agnostic programs
Towards An Approximative Ontology-Agnostic Approach for Logic Programs, FOIKS 2014
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Data variety +
Data
Full data coverage
Full knowledge
Full automation
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Reasoning with Distributional Semantics
A Distributional Semantics Approach for Selective Reasoning on Commonsense Graph
Knowledge Bases, NLDB 2014
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Data variety +
Data
Full data coverage
Full automation
Full knowledge
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Existing semantic technologies can address today major data
management problems
Muiti-disciplinarity is one key (and NLI people are very good at it!):- 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
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Want to play with Distributional
Semantics?
http://easy-esa.org
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Any Queries?