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Representing Texts as Contextualized

Entity-Centric Linked Data Graphs

Andre Freitas1 Joao C. P. da Silva2 Danilo S. Carvalho2

Sean O’Riain1 Edward Curry1

1DERI - Ireland2Universidade Federal Rio de Janeiro - Brazil

August 27, 2013

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Outline

Motivation & Objective

Existing Approaches

Structured Discourse Graphs (SDGs)

Representation RequirementsSemantic Model Elements & Graph PatternsSemantic Model - Formalization

Extraction

Graphia ExtractorPreliminary EvaluationExtraction Examples

Conclusion & Future Work

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Motivation & Objective

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Motivation

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Motivation

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Motivation

Integration of unstructured information into the Linked Data Web

Natural Language Texts

Terminological variation

Complex context patterns

Ambiguous sentences

Linked Data

Terminological and structural regularity

Consensual semantics - agreement between data consumers

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Motivation

Traditional Information Extraction

Triples - Primary concerns

accuracy

consistency

lexical and structural normalization (schema/ontology)

Complemented - Best-Effort Information Extraction

domain-independency

context capture

maximization of the text semantics representation

wider extraction scope

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Motivation

What is the relationship between Barack Obama and Indonesia?

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Objective

Structured Discourse Graph (SDG) Model

Improve semantic integration, representation and interpretation of

unstructured text within the context of Linked Data.

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Objective

Features

Entity-Centric Data Model

Ontology/Vocabulary Agnostic Representation

Context Representation

Graph Extraction

Interpretation (Navegational) Model

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Existing Approaches

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Existing Approaches

Simple Relations (Triples)

Discourse Representation Structure (DRS)

Ontology-based

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Our Work

Focuses on:

Providing a principled description of a representation modelcomplementary to existing approaches

Supports context representation

Defining a graph representation approach for texts which canbe easily integrated to the Linked Data Web

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Structured Discourse Graphs - SDGs

Representation Requirements

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SDGs - Representation Requirements

Entity-centric graph model

Entity pivot : named entity (subject or object)

Document Centric ⇒ Entity Centric

Maximized representation of text semantics

Information in sentence ⇒ graph representation

Resulting graph should support an algorithmic interpretation ofthe extracted SDG

Conceptual model independency

Ontology agnostic

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SDGs - Representation Requirements

Context Capture & Representation

Contextual information related to a triple statement should berepresented in the extracted graph

Contextual statements (such as temporality) define the contextin which another statement holds

Dependencies between sentences in the text should also bemade explicit

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SDGs - Representation Requirements

Pay-as-you-go semantic reference

Unstructured text may contain complex semantic dependencies

Should support the evolution and refinement of the semanticmodel

Standardized representation compatibility

Maximize compatibility with a standards-based datarepresentation format, facilitating

graph integration

interoperability on the Web

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Structured Discourse Graphs - SDGs

Semantic Model Elements & Graph Patterns

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SDGs - Semantic Model Elements

In late 1988, Obama entered Harvard Law School.

Text Segmentation: (subject,predicate,object)

Named Entities

Refer to the description of entities for which one or many rigiddesignators stands for the referent

Rigid designators: people, places, organization, biologicalspecies, substances, etc

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SDGs - Semantic Model Elements

In late 1988, Obama entered Harvard Law School.

Text Segmentation: (subject,predicate,object)

Named Entities

Refer to the description of entities for which one or many rigiddesignators stands for the referent

Rigid designators: people, places, organization, biologicalspecies, substances, etc

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SDGs - Semantic Model Elements

In late 1988, Obama entered Harvard Law School.

Text Segmentation: (subject,predicate,object)

Named Entities

Refer to the description of entities for which one or many rigiddesignators stands for the referent

Rigid designators: people, places, organization, biologicalspecies, substances, etc

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SDGs - Semantic Model Elements

In late 1988, Obama entered Harvard Law School.

Co-Referential Elements

Two types co-references: pronominal and non-pronominal

Co-references can refer to either intra or inter sentences

Substituting the co-referent term by the named entity

Can corrupt the semantics of the representation

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SDGs - Semantic Model Elements

In late 1988, Obama entered Harvard Law School.

Context Elements

A semantic interpretation may depend on different contexts(temporal context)

The main contextual information is intra-sentence

Intra-sentence context ⇒ reification

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SDGs - Semantic Model Elements

In late 1988, Obama entered Harvard Law School.

Quantifiers & Generic Operators

Quantifier: one, two, (cardinal numbers), many (much),some, all, thousands of, one of, several, only, most ofNegation: notModal: could, may, shall, need to, have to, must, maybe,always, possiblyComparative: largest, smallest, most, largest, smallest, thesame, is equal, like, similar to, more than, less than

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SDGs - Semantic Model Elements & Graph Patterns

In late 1988, Obama entered Harvard Law School.

Text segmentation: (Obama,entered,Harvard Law School)

Named entities: Obama, Harvard Law School

Resolve co-references: Barack Obama

Context representation: time

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SDGs - Semantic Model Elements & Graph Patterns

In late 1988, Obama entered Harvard Law School.

Resolved & Normalized Entities

Resolved entities: a node-substitution in the graph was madefrom a co-reference to a named entity

Normalized entities: entities are transformed to a normalizedform (September 1st of 2010 to 01/09/2010)

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SDGs - Semantic Model Elements & Graph Patterns

Later in 2007, Obama sponsored an amendment to theDefense Authorization Act to add safeguards for

personality-disorder military discharges.

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SDGs - Semantic Model Elements & Graph Patterns

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SDGs - Semantic Model Elements & Graph Patterns

He served three terms representing the 13th District in theIllinois Senate from 1997 to 2004.

Non-Named (Generic) Entities

Non-named entities map to non-rigid designators

Are more subject to vocabulary variation

Have more complex compositional patterns

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SDGs - Semantic Model Elements & Graph Patterns

Following high school, Obama moved to Los Angeles in 1979to attend Occidental College.

Triple Trees

Not all facts extracted can be represented in one triple.

Transformation from the syntactic tree to a set of triples

The sentence subject defines the root node

Interpretation: DFS traversal of the tree

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SDGs - Semantic Model Elements & Graph Patterns

He won election to the U.S. Senate in Illinois in November2004.

Pronominal Co-Reference

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SDGs - Semantic Model Elements & Graph Patterns

On Agust 23, Obama announced his election of DelawareSenator Joe Biden as his vice presidential running mate.

Pronominal Co-Reference

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SDGs - Semantic Model Elements & Graph Patterns

As a member of the Senate Foreign Relations Commitee,Obama made official trips to Eastern Europe, the Middle

East, Central Asis and Africa.

Conjunctive Co-Reference

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SDGs - Semantic Model Elements & Graph Patterns

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Structured Discourse Graphs - SDGs

Semantic Model - Formalization

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Semantic Model - Formalization

Graph pattern: atomic graph structure which maps to adiscourse structure

Named and Non-named Entities: [[ne]], [[∼ ne]] ∈ U,where U is a set of IRIs

Basic Triple: tr = (es , p, eo) where es , eo representnamed/non-named entities associated with the subject (s) andobject (o), and p represents a relation between es and eo ,interpreted by [[tr ]] = ([[es ]], [[p]], [[eo ]]) ∈ U × U × U

Core Triple: trc = (nes , p, neo)

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Semantic Model - Formalization

Reification Triple: trrei = (tr , reilink , reiobj )

tr - basic triplesreilink - relationreiobj - reification object

Temporal Reification: special reification triple

reilink : timereiobj : explicit or implicit data references

Interpretation:[[trrei ]] = ([[tr ]], [[reilink ]], [[reiobj ]]) ∈ U × U × U

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Semantic Model - Formalization

Quantifier Operators & Generic Operators:opt = (eo , oplink , op)

eo - object element in a basic triple trop - specific operator of eo wrt oplink

Interpretation:

[[opt]] = ([[proj3(tr)]], [[oplink ]], [[op]]) = ([[eo ]], [[oplink ]], [[op]])

where proj3(tr) = proj3((es , p, eo)) = eo

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Semantic Model - Formalization

Conjunctive Co-Reference: ccr =⋃n

i=0{(e, conjlinki , nei )}

Interpretation:

[[ccr ]] = {([[e]], [[conjlinki ]], [[nei ]]) ∈ U × U × U such that

[[e]] = [[proj3(tr)]] and (∧ni=0[[nei ]] sameas [[e]])}

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Semantic Model - Formalization

Possessive/Reflexive/Demonstrative Co-Reference:

pcr = {(∼ nei , coreflink , pr), (pr , coreflink , ej )}

Interpretation:

[[pcr ]] ={([[proj1(tr)]], [[coreflink ]], [[pr ]]), ([[pr ]], [[coreflink ]], [[proj3(tr)]]) ∈U × U × U such that tr = (∼ nei , p, ej)}

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Semantic Model - Formalization

Extracted Graph: set of basic and reified triples and generic,quantifier and co-reference operators.

Paths

Basic: sequence of basic triples

Reified: basic paths with some reified triples associated

Operational: basic paths with some operators associated

Complex: contains both reified and operational paths

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Semantic Model - Formalization

Context Triples: (tr , contextlink , ct) which indicates that abasic triple tr can be associated with a specific context ct[[context]] = ([[tr ]], [[contextlink ]], [[ct]]) ∈ U3 × U × U

Multi-Context Graphs: is an extracted graph with morethan one context associated to its triples

if all basic triples in a path belong to an unique (same)context, the path is an unique context (basic, reified,operational or complex) path

otherwise, we call this path a multi-context path

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Extraction

Graphia

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Graphia

http://graphia.dcc.ufrj.br

Graphia is an information extraction pipeline

Takes factual text as input and produces SDGs as output

Graphia’s modules combine state-of-art NLP tools with anefficient set of heuristics

Can build graphs for sentences andentire documents.

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Graphia - Extraction Pipeline Architecture

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Graphia - Preliminary Evaluation

1033 relations (triples) from 150 sentences from 5 randomlyselected Wikipedia articles

Manually classified the graphs

Correct Graphs: fully consistent with the semantic model

Complete Graphs: correct graph which maps all theinformation of a sentence

Interpretable Graphs: graph fragment which has the correctsemantics of its basic triple paths

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Graphia - Extraction Examples

In 2002 GE acquired the wind power assets of Enron during itsbankruptcy proceedings.

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Graphia - Extraction Examples

In 1935, GE was one of the top 30 companies tradedat the London Stock Exchange.

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Graphia - Extraction Examples

The Radio Corporation of America (RCA) was founded by GE in1919 to further international radio.

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Graphia - Extraction Examples

It acquired ScanWind in 2009.

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Graphia - Extraction Examples

The new company, named GXS, is based in Gaithersburg,Maryland.

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Conclusion & Future Work

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Conclusion & Future Work

Conclusion

SDGs provide a discourse representation as a set ofcontextualized relationships

Supports entity-centric integration between graphs

Conceptual Model Independency

Worth putting effort on enumerable patterns (timestamps,operators)

Future Work

More complex sentence patterns

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Representing Texts as Contextualized

Entity-Centric Linked Data Graphs

Andre Freitas1 Joao C. P. da Silva2 Danilo S. Carvalho2

Sean O’Riain1 Edward Curry1

1DERI - Ireland2Universidade Federal Rio de Janeiro - Brazil

August 27, 2013

Freitas, Silva, Carvalho, O’Riain, Curry 54/ 54