University of Economics Prague Ontology-based information extraction: progresses and perspectives of...

University of Economics Prague

Ontology-based information extraction: progresses and perspectives of the Ex tool

Martin Labský[email protected]

KEG seminar, May 29, 2008

ISMIS 2008 Combining Multiple Sources of Evidence in Web IE

Agenda

1. Motivation for Web Information Extraction (IE)2. Difficulties in practical applications3. Extraction Ontologies4. Extraction process5. Experimental results: contact information6. Future work and Conclusion


Motivation for Web IE (1/4): online products


Motivation for Web IE (2/4): contact information


Motivation for Web IE (3/4): seminars, events


Motivation for Web IE (4/4)

Store the extracted results in a DB to enable structured search over documents– information retrieval– database-like querying– e.g. online product search engine– e.g. building a contact DB

Support for web page quality assessment– involved in an EU project MedIEQ to support medical

website accreditation agencies Source documents

– internet, intranet, emails– can be very diverse


Agenda

1. Motivation for Web Information Extraction (IE)2. Difficulties in practical IE applications3. Extraction Ontologies4. Extraction process5. Experimental results: contact information6. Future work and Conclusion


Difficulties in practical applications (1/3)

Requirements– be able to extract some information quickly

not necessarily with the best accuracyoften needed for a proof-of-concept applicationthen more work can be done to boost accuracy

– the extraction model changesmeaning of to-be-extracted items may shift, new items are often added



Training data– most state-of-the-art trainable IE systems require large amounts

of training data: these are almost never available– when training data is collected, it is not easy to adapt it to

changed or additional criteria– active learning helps reduce training data collection efforts but

users often need to spend time annotating trivial examples that could be easily covered by manual rules

– this is our experience from experiments with extraction of bicycle descriptions using Hidden Markov Models

Wrappers– cannot rely on wrapper-only systems when extracting from

multiple websites– non-wrapper systems often do not utilize regular formatting

cues Purely manual rules

– just writing extraction rules manually is not easily extensible when training data are collected in later phases



It seems to be difficult to exploit at the same time– extraction knowledge from domain experts– training data– formatting regularities

within a documentwithin a group of documents from the same source

We attempt to address this with the approach of extraction ontologies


Agenda



Extraction ontologies

An extraction ontology is a part of a domain ontology transformed to suit extraction needs

Contains classes composed of attributes– more like UML class diagrams, less like

ontologies where e.g. relations are standalone

– also contains axioms related to classes or attributes

Classes and attributes are augmented with extraction evidence– manually provided patterns for content

and context– value or length ranges– links to external trainable classifiers

Personname {1}degree {0-5}email {0-2}phone {0-3}

Responsible


Extraction evidence provided by domain expert (1)

Patterns– for attributes and classes– for their content and context– patterns may be defined at the following levels:

word and character-level, formatting tag level level of labels (e.g. sentence breaks, POS tags)

Attribute value constraints– word length constraints, numeric value ranges– possible to attach units to numeric attributes

Axioms– may enforce relations among attributes– interpreted using JavaScript scripting language

Simple co-reference resolution rules


Extraction evidence provided by domain expert (2)

Axioms class level attribute level

Patterns class content attribute value attribute context class context

Value constraints word length numeric value


Extraction evidence from classifiers

Links to trainable classifiers– may classify attributes only– binary or multi-class

Training (if not done externally) uses these features– re-use all evidence provided by expert– induce binary features based on word n-grams

Feature induction– candidate features are all word n-grams of given lengths occurring

inside or near training attribute values– pruning parameters:

point-wise mutual information thresholds: minimal absolute occurrence count maximum number of features

)()(

),(

clsPfP

clsfPpwmi

classifier usage

classifier definition


Probabilistic model to combine evidence

Each piece of evidence E is equipped with 2 probability estimates with respect to predicted attribute A:– evidence precision P(A|E) ... prediction confidence– evidence coverage P(E|A) ... necessity of evidence (support)

Each attribute is assigned some low prior probability P(A) Let be the set of evidence applicable to A Assume conditional independence among :

Using Bayes formula we compute P(A | its evidence values) as:

where

AA


Agenda



The extraction process (1/5)

1. Tokenize, build HTML formatting tree, apply sentence splitter, POS tagger

2. Match patterns3. Create Attribute Candidates (ACs)

For each created AC, let PAC=

prune ACs below threshold build document AC lattice, score ACs by log(PAC)

Washington , DC

......



4. Evaluate coreference resolution rules for each pair of ACs e.g. “Dr. Burns” “John Burns” possible coreferring groups are remembered in attribute’s value section:

5. Compute the best scoring path BP through AC lattice using dynamic programming

6. Run wrapper induction algorithm using all AC BP wrapper induction algorithm described in next slides if new local patterns are induced, apply them to:

rescore existing ACs create new ACs

update AC lattice, recompute BP7. Terminate here if no instances are to be generated

output all AC BP (n-best paths supported)



8. Generate Instance Candidates (ICs) bottom-up

– triangular trellis used to store partial ICs– when scoring new ICs, only consider axioms and patterns that

already can be applied to the IC. Validity is not required.– pruning parameters: abs and relative beam size at trellis node,

maximum number of ACs that can be skipped, min IC probability



8. IC generation: continued When new IC is created, its P(IC) is computed from 2 components:

where |IC| is member attribute count, ACskip is an non-member AC that is fully or partially inside the IC,

PAC skip is the probability of AC being a “false positive”.

where C is the set of evidence known for the class C, computed using the same probabilistic model as for ACs.

Scores are combined using the Prospector pseudo-bayesian method:



9. Insert valid ICs into AC lattice Valid ICs were assembled during IC generation phase Score of a valid IC reflects all extraction evidence of its class All unpruned valid ICs are inserted into the AC lattice, scored by

10. The best path BP is calculated through the IC+AC lattice (n-best supported) the search algorithm allows constraints to be defined over the

extracted path(s) e.g. min/max count of extracted instances

output all ACs and ICs on BP

||))(log( ICICscore

IC1


Extraction evidence based on formatting

A simple wrapper induction algorithm – identify formatting regularities– turn them into “local” context patterns to boost contained ACs

1. Assemble distinct formatting subtrees rooted at block elements containing ACs from the best path BP currently determined by the system

2. For each subtree S, calculate

3. If both C(S,Att) and prec(Att|S) reach defined thresholds, a new local context pattern is created with its precision set to C(S,Att) and its recall close to 0 (in order not to harm potential singleton ACs.

TD

A_hrefB

John Doe [email protected]

TD

A_hrefB

Argentina Agosto [email protected]

)(

),()|(

SC

AttSCSAttprec

a formatting tree learned using known names like “John Doe” and applied to unknown names


Agenda



Experimental results: Contact information

109 English contact pages, 200 Spanish, 108 Czech Named entity counts: 7000, 5000, 11000, respectively, instances not labeled Only domain expert’s evidence and formatting pattern induction were used Domain expert saw 30 randomly chosen documents, the rest was test data Instance extraction done but not evaluated


Future work

Confirm that improved results can be achieved when combining expert knowledge and formatting pattern induction with classifiers

Attempt to improve a seed extraction ontology by bootstrapping using relevant pages retrieved from the Internet

Adapt the structure of extraction ontology according to data– e.g. add new attributes to represent product features


Conclusions

Presented an extraction ontology approach to– allow for fast prototyping of IE applications– accomodate extraction schema changes easily– utilize all available forms of extraction knowledge

domain expert’s knowledgetraining dataformatting regularities found in web pages

Results– indicate that extraction ontologies can serve as a

quick prototyping tool– it seems possible to improve performance of the

prototyped ontology when training data become available


Acknowledgements

The research was partially supported by the EC under contract FP6-027026, Knowledge Space of Semantic Inference for Automatic Annotation and Retrieval of Multimedia Content: K-Space.

The medical website application is carried out in the context of the EC-funded (DG-SANCO) project MedIEQ.


Backup slides

IET and co.


Information extraction toolkit - architecture

INFORMATION EXTRACTION TOOLKIT

user components

admin components

IE Engines

Labelling schemas

Classified documents from WCC

DataModel

Manager

Pre-processor

UI

Expert’sdomain

and extractionknowledg

e,annotated

corpora

Ex extraction ontology engine

TaskManage

rUI Documen

t IO

Annotated documents

Extracted attributes, instances

Annotationtool

UI

AQUA

Evaluator

CRF extraction engine

Rule-based integrator (TBD)


extract attributes

extract attributesand instances

refines extracted values,e.g. based on document classification

Information extraction toolkit – document flow

Rule-based

integrator

Extraction

ontology engine

Pre-processor

CRF NE engine

classified document

select extraction model (s) based on document class

extracted attributesand instances


Czech contact data set: results

counts strict mode loose mode

gold auto prec recall F prec recall F

title 0.87 0.88 0.88 0.89 0.91 0.90

name 0.74 0.82 0.78 0.76 0.83 0.80

street 0.78 0.66 0.71 0.83 0.69 0.75

city 0.67 0.69 0.68 0.75 0.79 0.77

region

zip 0.91 0.97 0.94 0.91 0.97 0.94

country 0.64 0.87 0.74 0.66 0.96 0.78

phone 0.92 0.85 0.88 0.93 0.85 0.89

email 0.99 0.98 0.98 0.99 0.98 0.98

organization

department

overall 0.81 0.84 0.82 0.84 0.87 0.84


Czech dataset: per-attribute F-measures

F (strict)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

title

name

street

city zip

coun

try

phon

eem

ail

over

all

F (loose)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

title

name

street

city zip

coun

try

phon

eem

ail

over

all

IET purpose: to support the user by providing suggestions not to work standalone without supervision


Customization to new criteria

Precisely define the criterion or criteria group– define and give positive and negative examples

If gazetteers required:– search or construct appropriate gazetteers

If training required:– annotate training corpus of at least 100 documents with at

least 300 occurrences of the criterion– train one of the trainable extractors:

CRF engine Ex with Weka integration

If some extraction evidence can be given by human:– write new or extend an existing extraction ontology

Evaluate performance


Localization to a new language

Reuse language independent parts of extraction ontology:– class structure (attributes in a class)– cardinalities, constraints, axioms– some criteria can be reused almost completely (phone,

email) If a criterion requires training:

– annotate corpus and train classifier as when adding a new criterion

Provide language specific extraction evidence that can be encoded by a human (if any):– add to extraction ontology


Demo + tutorial

IET + Ex– free text criteria– (shows internal IET user interface)

Tutorial– http://eso.vse.cz/~labsky/ex/ex_tutorial.pdf


New features in Ex IE engine

Significant speed-up Memory footprint reduction Multiple class extraction Extended axiom support Instance parsing and reference resolution

improvements Extraction ontology authoring made easier

University of Economics Prague Ontology-based information extraction: progresses and perspectives of...

Documents

Transcript of University of Economics Prague Ontology-based information extraction: progresses and perspectives of...