Cognitive Architecture for Reasoning about Adversaries T-REX: A Domain-Independent System for Automated Cultural Information Extraction Massimiliano Albanese V.S. Subrahmanian University of Maryland Institute for Advanced Computer Studies College Park, Maryland, USA

2 Cognitive Architecture for Reasoning about Adversaries 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

3 Cognitive Architecture for Reasoning about Adversaries 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

4 Cognitive Architecture for Reasoning about Adversaries T-REX architecture Crawling and parsing

5 Cognitive Architecture for Reasoning about Adversaries Multilingual Annotation Interface Sentence being annotated Parse tree edit panel List of triples that can be extracted from the sentence Constraint selection panel

6 Cognitive Architecture for Reasoning about Adversaries 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

7 Cognitive Architecture for Reasoning about Adversaries 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

8 Cognitive Architecture for Reasoning about Adversaries Annotation Process: Step 2 The annotator marks as “variable” all the nodes that may have different text in other sentences of the same class

9 Cognitive Architecture for Reasoning about Adversaries Annotation Process: Step 3 If needed, the annotator add constraints to variable nodes

10 Cognitive Architecture for Reasoning about Adversaries 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.

11 Cognitive Architecture for Reasoning about Adversaries Annotation Process: Step 4 The annotator describes the semantics of the annotated sentence in term of triples, mapping attributes to variable nodes

12 Cognitive Architecture for Reasoning about Adversaries Annotations in Multiple Languages English Chinese simplified ( 中文 ) Spanish (Español)

13 Cognitive Architecture for Reasoning about Adversaries 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

14 Cognitive Architecture for Reasoning about Adversaries 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

15 Cognitive Architecture for Reasoning about Adversaries 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

16 Cognitive Architecture for Reasoning about Adversaries 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”

17 Cognitive Architecture for Reasoning about Adversaries 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

18 Cognitive Architecture for Reasoning about Adversaries Example of extracted data (2/2) Link depth 2 from Pushtuns

19 Cognitive Architecture for Reasoning about Adversaries 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

20 Cognitive Architecture for Reasoning about Adversaries 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)