Cognitive Architecture for Reasoning about Adversaries

T-REX: A Domain-Independent System for Automated Cultural Information Extraction

Massimiliano AlbaneseV.S. Subrahmanian

University of Maryland Institute for Advanced Computer Studies

College Park, Maryland, USA

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Introduction

Several applications require the ability to extract fine-grained information from huge text collections

» Intelligence agencies may need detailed information about diverse cultural groups around the world in order to understand and model their behavior

» A real-time “violence-watch” around the world would require the ability to identify several attributes for every “violent event” reported in the online press

Traditional search engines

» Are not able to provide such information without sorting through a long list of documents

» Are not able to integrate information from different sources

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Key contributions

Domain-independent framework for information extraction

» A schema describing the information the user wants to extract is provided as an input

Key features

» Scalability: the system is designed to massively scale to large volumes of data

• It currently searches through 109 online news sites from 66 countries around the world, processing about 45,000 articles/day (about 10 millions distinct urls explored so far, with 7 millions triples extracted)

» Multilingual support: the system is designed to work with different languages

• English, Spanish and Chinese

» Flexibility: several elements can be easily customized

• List of sources, topics of interest, type of information to extract

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T-REX architecture

Crawling and parsing

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Multilingual Annotation Interface

Sentence being annotated

Parse tree edit panel

List of triples that can be extracted from the sentence

Constraint selection panel

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Annotation Process: Motivation

The same fact can be reported in many slightly different ways

» At least 73 civilians were killed February 1 in simultaneous suicide bombings at a Hilla market

» More than 73 civilians were massacred in February in suicide attacks at a Hilla marketplace

» 74 people were killed on February 1, 2007 in multiple bombings at a Hilla market

Other similar events may be reported through similar sentences, describing the same set of attributes

» About 23 U.S. soldiers were killed in August 2005 in a suicide attack in Baghdad

Sentences describing the same type of fact in slightly different ways can be grouped into a single class

» Learning an “extraction rule” for each class of interest to a given application enables to extract the desired information from any article

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Annotation Process: Step 1

The annotator is presented with one or more parse trees for the sample sentence

At least 73 civilians were killed February 1 in simultaneous suicide

bombings at a Hilla market

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Annotation Process: Step 2

The annotator marks as “variable” all the nodes that may have different text in other sentences of the same class

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Annotation Process: Step 3

If needed, the annotator add constraints to variable nodes

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Annotation Process: Constraints

IS_ENTITY

» restricts a noun phrase to be a “named entity”

IS_DATE

» restricts a noun phrase to be a temporal expression

X_VERBS

» restricts a verb to be any member of a class X of verbs

• e.g. the constraint MURDER_VERBS requires a verb to be any of the following: kill, assassinate, murder, execute, etc.

X_NOUNS

» restricts a noun to be any member of a class X of nouns

• e.g. the constraint ATTACK_NOUNS requires a noun to be any of the following: assault, attack, clash, etc.

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Annotation Process: Step 4

The annotator describes the semantics of the annotated sentence in term of triples, mapping attributes to variable nodes

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Annotations in Multiple Languages

English Chinese simplified (中文 )

Spanish (Español)

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Rule Extraction Engine

An extraction rule is of type Head Body A rule is learned through the following

steps

» abstraction• each variable node is assigned a numeric

identifier, its text and child nodes are removed

› the model becomes independent of the particular sentence

» body definition • the body of the rule is built by serializing

the parse tree of the annotated sentence in Treebank II Style

» head definition • the head is defined as a conjunction of

RDF statements, one for each triple defined in the last step of the annotation process

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Rule Matching Engine (1/2)

Extracts RDF triples, by matching sentence from texts being analyzed against the set of extraction rules

Continuously fetches documents relevant to the application of interest

If the parse tree of a sentence satisfies the condition in the body of a rule an

RDF triple is instantiated for each statement in the head of the rule

CompareNodes() determines if the parse tree of a sentence satisfies the

condition in the body of a rule

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Rule Matching Engine (2/2)

CompareNodes() recursively explores the parse tree of the sentence being processed and the annotated parse tree of a rule

Checks satisfaction of constraints for variable nodes

Checks constant nodes

Pairwise compares child nodes of non terminal nodes

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Example of Matching

Var#1 = “About 23” Var#2 = “U.S. soldiers”Var#3 = “were”Var#4 = “killed”Var#5 = “August 23”Var#6 = “a suicide attack”Var#7 = “Baghdad”

(KillingEvent9,victim,U.S. soldiers)(KillingEvent9,numberOfVictims,about 23)(KillingEvent9,date,August 23)(KillingEvent9,location,Baghdad)

The sentence satisfies the body of the rule

e.g. “About 23 U.S. soldiers were killed August 23 in a suicide attack in Baghdad”

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Example of extracted data (1/2)

At least 22 Hindus were killed by suspected Muslim militants in India's Jammu and Kashmir state Monday, the police said

Event data

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Example of extracted data (2/2)

Link depth 2 from Pushtuns

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T-REX implementation

The implementation of T-REX consists of several components running on different nodes of a distributed system

» Multilingual Annotation Interface: web-based tool, that is part of the web interface of T-REX (implemented as a Java Applet)

» Annotated RDF Database System for storage of annotated RDF triples: the underlying relational DBMS is PostgreSQL 8.2

» Rule Matching Engine: a pipeline of several components

• Crawler: explores news sources for relevant documents

• Parsers for every language: process sentences from relevant documents, producing constituent trees in Treebank II Style

• Extractor: implements the Rule Matching Engine logic

Distribution, Database Partitioning, and Multithreading ensure scalability

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Conclusions

We have presented a general, multi-lingual and flexible framework for information extraction» Domain specific application are enabled by targeting the

extraction to the instantiation of a schema of interest

» Addition of other languages is a relatively simple task, once a set of linguistic resources are available for those languages

We have implemented a complex prototype that has proved to» effectively extract information for different applications

» scale massively Future efforts will be devote to

» define pruning strategies to make the extraction process faster

» define strategies to manage inconsistencies in the extracted data

» extend the system to other languages (mainly Asian languages)