human language technology (hlt) and knowledge acquisition for the semantic web: a tutorial

Human Language Technology (HLT) and Knowledge Acquisition for the Semantic

Web: a Tutorial

Diana Maynard (University of Sheffield)Julien Nioche (University of Sheffield)

Marta Sabou (Vrije Universiteit Amsterdam)Johanna Völker (AIFB)

Atanas Kiryakov (Ontotext Lab, Sirma AI)

EKAW 2006

[This work has been supported by SEKT (http://sekt.semanticweb.org/) and

KnowledgeWeb (http://knowledgeweb.semanticweb.org/ ]

http://sekt.semanticweb.org/

http://knowledgeweb.semanticweb.org/

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1. Motivation, background

2. GATE overview

3. Information Extraction

4. GATE’s HLT components

5. IE and the Semantic Web

6. Ontology learning with Text2Onto

7. Focused ontology learning

8. Massive Semantic Annotation

Structure of the Tutorial

3

Aims of this tutorial

• Investigates some technical aspects of HLT for the SW and brings this methodology closer to non-HLT experts

• Provides an introduction to an HLT toolkit (GATE)

• Demonstrates using HLT for automating SW-specific knowledge acquisition tasks such as:– Semantic annotation– Ontology learning– Ontology population

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Some Terminology

• Semantic annotation – annotate in the texts all mentions of instances relating to concepts in the ontology

• Ontology learning – automatically derive an ontology from texts

• Ontology population – given an ontology, populate the concepts with instances derived automatically from a text

5

Semantic Annotation: Motivation

• Semantic metadata extraction and annotation is the glue that ties ontologies into document spaces

• Metadata is the link between knowledge and its management

• Manual metadata production cost is too high

• State-of-the-art in automatic annotation needs extending to target ontologies and scale to industrial document stores and the web

6

Challenge of the Semantic Web

• The Semantic Web requires machine processable, repurposable data to complement hypertext

• Once metadata is attached to documents, they become much more useful and more easily processable, e.g. for categorising, finding relevant information, and monitoring

• Such metadata can be divided into two types of information: explicit and implicit.

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Metadata extraction

• Explicit metadata extraction involves information describing the document, such as that contained in the header information of HTML documents (titles, abstracts, authors, creation date, etc.)

• Implicit metadata extraction involves semantic information deduced from the text, i.e. endogenous information such as names of entities and relations contained in the text. This essentially involves Information Extraction techniques, often with the help of an ontology.

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Ontology Learning and Population: Motivation

• Creating and populating ontologies manually is a very time-consuming and labour-intensive task

• It requires both domain and ontology experts• Manually created ontologies are generally not

compatible with other ontologies, so reduce interoperability and reuse

• Manual methods are impossible with very large amounts of data

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Semantic Annotation vs Ontology Population

• Semantic Annotation– Mentions of instances in the text are annotated wrt

concepts (classes) in the ontology.– Requires that instances are disambiguated.– It is the text which is modified.

• Ontology Population– Generates new instances in an ontology from a text. – Links unique mentions of instances in the text to

instances of concepts in the ontology.– Instances must be not only disambiguated but also

co-reference between them must be established.– It is the ontology which is modified.

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2. GATE overview







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GATE : an open source framework for HLT

• GATE (General Architecture for Text Engineering) is a framework for language processing (http://gate.ac.uk)

• Open Source (LGPL licence)• Hosted on SourceForge

http://sourceforge.net/projects/gate

• Ten years old (!), with 1000s of users at 100s of sites

• Current version 3.1

http://gate.ac.uk/

http://gate.ac.uk/

http://sourceforge.net/projects/gate

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4 sides to the story

• An architecture: A macro-level organisational picture for HLT software systems.

• A framework: For programmers, GATE is an object-oriented class library that implements the architecture.

• A development environment: For language engineers, computational linguists et al, a graphical development environment.

• A community of users and contributors

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Architectural principles

• Non-prescriptive, theory neutral (strength and weakness)

• Re-use, interoperation, not reimplementation (e.g. diverse XML support, integration of Protégé, Jena, Yale...)

• (Almost) everything is a component, and component sets are user-extendable

• (Almost) all operations are available both from API and GUI

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All the world’s a Java Bean....

CREOLE: a Collection of REusable Objects for Language Engineering:

• GATE components: modified Java Beans with XML configuration

• The minimal component = 10 lines of Java, 10 lines of XML, 1 URL

Why bother? • Allows the system to load arbitrary language

processing components

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NOTES•everything is a replaceable bean•all communication via fixed APIs •low coupling, high modularity, high extensibility

…

HTMLdocs

RTFdocs

XMLdocs

PDFdocs

email

XMLDocument

Format

HTMLDocument

Format

PDFDocument

Format

…Document

FormatLayer (LRs)

XML OraclePostgreSql .ser

DataStore Layer

Corpus Document

DocumentContent

AnnotationSet

Annotation FeatureMap

Corpus Layer (LRs)

GATE APIs

Processing Layer (PRs)

NE Co-ref TEs TRs POS …

Onto-logy

ProtégéOnto-logy

Word-net

Gaz-etteers

Language Resource Layer (LRs)

...

Application Layer

ANNIE OBIE …IDE GUI Layer (VRs)

ADiff OntolVR DocVR ...

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GATE Users

• American National Corpus project • Perseus Digital Library project, Tufts University, US• Longman Pearson publishing, UK• Merck KgAa, Germany• Canon Europe, UK• Knight Ridder, US• BBN (leading HLT research lab), US• SMEs: Melandra, SG-MediaStyle, ...• a large number of other UK, US and EU Universities• UK and EU projects inc. SEKT, PrestoSpace,

KnowledgeWeb, MyGrid, CLEF, Dot.Kom, AMITIES, CubReporter, …

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Past Projects using GATE

• MUMIS: conceptual indexing: automatic semantic indices for sports video

• MUSE: multi-genre multilingual IE• HSL: IE in domain of health and safety• Old Bailey: IE on 17th century court reports• Multiflora: plant taxonomy text analysis for biodiversity

research in e-science• EMILLE: creation of S. Asian language corpus• ACE / TIDES: IE competitions and collaborations in

English, Chinese, Arabic, Hindi• h-TechSight: ontology-based IE and text mining

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Current projects using GATE

• ETCSL: Language tools for Sumerian digital library• SEKT: Semantic Knowledge Technologies• PrestoSpace: Preservation of audiovisual data• KnowledgeWeb: Semantic Web network of excellence• MEDIACAMPAIGN: Discovering, inter-relating and

navigating cross-media campaign knowledge • TAO : Transitioning Applications to Ontologies• MUSING : SW-based business intelligence tools• NEON : Networked Ontologies

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GATE

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2. GATE overview







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IE is not IR

IE pulls facts and structured information from the content of large text collections. You analyse the facts.

IR pulls documents from large text collections (usually the Web) in response to specific keywords or queries. You analyse the documents.

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IE for Document Access

• With traditional query engines, getting the facts can be hard and slow

• Where has the Queen visited in the last year?• Which places on the East Coast of the US have

had cases of West Nile Virus? • Which search terms would you use to get this

kind of information?• How can you specify you want someone’s

home page?• IE returns information in a structured way• IR returns documents containing the relevant

information somewhere (if you’re lucky)

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HaSIE: an example application

• Application developed by University of Sheffield, which aims to find out how companies report about health and safety information

• Answers questions such as:“How many members of staff died or had accidents

in the last year?”“Is there anyone responsible for health and

safety?”“What measures have been put in place to

improve health and safety in the workplace?”

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HaSIE

• Identification of such information is too time-consuming and arduous to be done manually.

• Each company report may be hundreds of pages long.

• IR systems can’t help because they return whole documents

• System identifies relevant sections of each document, pulls out sentences about health and safety issues, and populates a database with relevant information

• This can then be analysed by an expert

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HASIE

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Named Entity Recognition: the cornerstone of IE

• Identification of proper names in texts, and their classification into a set of predefined categories of interest

• Persons• Organisations (companies, government

organisations, committees, etc)• Locations (cities, countries, rivers, etc)• Date and time expressions• Various other types as appropriate

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Why is NE important?

• NE provides a foundation from which to build more complex IE systems

• Relations between NEs can provide tracking, ontological information and scenario building

• Tracking (co-reference) “Dr Smith”, “John Smith”, “John”, he”

• Ontologies “Athens, Georgia” vs “Athens, Greece”

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Two kinds of approaches

Knowledge Engineering• rule based • developed by experienced

language engineers • make use of human

intuition • require only small amount

of training data• development can be very

time consuming • some changes may be

hard to accommodate

Learning Systems• use statistics or other

machine learning • developers do not need

LE expertise • require large amounts of

annotated training data • some changes may

require re-annotation of the entire training corpus

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Typical NE pipeline

• Pre-processing (tokenisation, sentence splitting, morphological analysis, POS tagging)

• Entity finding (gazeteer lookup, NE grammars)• Coreference (alias finding, orthographic

coreference etc.)• Export to database / XML

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An ExampleRyanair announced yesterday that it will make Shannon its next European

base, expanding its route network to 14 in an investment worth around

€180m. The airline says it will deliver 1.3 million passengers in the first year

of the agreement, rising to two million by the fifth year.

• Entities: Ryanair, Shannon

• Descriptions: European base

• Relations: Shannon base_of Ryanair

• Events: investment(€180m)

• Mentions: it=Ryanair, The airline=Ryanair, it=the airline

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System development cycle

1. Collect corpus of texts2. Manually annotate gold standard3. Develop system4. Evaluate performance against gold

standard5. Return to step 3, until desired

performance is reached

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Performance Evaluation

2 main requirements:• Evaluation metric: mathematically defines how

to measure the system’s performance against human-annotated gold standard

• Scoring program: implements the metric and provides performance measures – For each document and over the entire

corpus– For each type of NE

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Evaluation Metrics

• Most common are Precision and Recall• Precision = correct answers/answers produced • Recall = correct answers/total possible correct

answers• Trade-off between precision and recall • F1 (balanced) Measure = 2PR / 2(R + P) • Some tasks sometimes use other metrics, e.g. cost-

based (good for application-specific adjustment)• Ontology-based IE requires measures sensitive to

the ontology

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GATE AnnotationDiff Tool

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Corpus-level Regression Testing

• Need to track system’s performance over time• When a change is made we want to know

implications over whole corpus• Why: because an improvement in one case can

lead to problems in others• GATE offers corpus benchmark tool, which

can compare different versions of the same system against a gold standard

• This operates on a whole corpus rather than a single document

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Corpus Benchmark Tool

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2. GATE overview







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GATE’s Rule-based System - ANNIE• ANNIE – A Nearly-New IE system• A version distributed as part of GATE• GATE automatically deals with document

formats, saving of results, evaluation, and visualisation of results for debugging

• GATE has a finite-state pattern-action rule language - JAPE, used by ANNIE

• A reusable and easily extendable set of components

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What is ANNIE?

ANNIE is a vanilla information extraction system comprising a set of core PRs:

– Tokeniser– Gazetteers– Sentence Splitter– POS tagger– Semantic tagger (JAPE transducer)– Orthomatcher (orthographic coreference)

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Core ANNIE Components

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Re-using ANNIE

• Typically a new application will use most of the core components from ANNIE

• The tokeniser, sentence splitter and orthomatcher are basically language, domain and application-independent

• The POS tagger is language dependent but domain and application-independent

• The gazetteer lists and JAPE grammars may act as a starting point but will almost certainly need to be modified

• You may also require additional PRs (either existing or new ones)

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DEMO of ANNIE and GATE GUI

• Loading ANNIE

• Creating a corpus

• Loading documents

• Running ANNIE on corpus

• Demo

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Gazetteers

• Gazetteers are plain text files containing lists of names (e.g rivers, cities, people, …)

• Information used by JAPE rules• Each gazetteer set has an index file listing all the

lists, plus features of each list (majorType, minorType and language)

• Lists can be modified either internally using Gaze, or externally in your favourite editor

• Gazetteers can also be mapped to ontologies• Generates Lookup results of the given kind

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JAPE grammars

• JAPE is a pattern-matching language

• The LHS of each rule contains patterns to be matched

• The RHS contains details of annotations (and optionally features) to be created

• The patterns in the corpus are identified using ANNIC

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Input specifications

The head of each grammar phase needs to contain certain information– Phase name– Inputs– Matching style

e.g.

Phase: locationInput: Token Lookup NumberControl: appelt

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Rule: Company1 Priority: 25 ( ( {Token.orthography == upperInitial} )+ //from tokeniser {Lookup.kind == companyDesignator} //from gazetteer lists ):match --> :match.NamedEntity = { kind=company, rule=“Company1” }

=> will match “Digital Pebble Ltd”

NE Rule in JAPE

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LHS of the rule

• LHS is expressed in terms of existing annotations, and optionally features and their values

• Any annotation to be used must be included in the input header

• Any annotation not included in the input header will be ignored (e.g. whitespace)

• Each annotation is enclosed in curly braces• Each pattern to be matched is enclosed in round

brackets and has a label attached

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Macros

• Macros look like the LHS of a rule but have no label

Macro: NUMBER(({Digit})+)

• They are used in rules by enclosing the macro name in round brackets

( (NUMBER)+):match

• Conventional to name macros in uppercase letters• Macros hold across an entire set of grammar phases

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Contextual information

• Contextual information can be specified in the same way, but has no label

• Contextual information will be consumed by the rule

({Annotation1})

({Annotation2}):match

({Annotation3})

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RHS of the rule

• LHS and RHS are separated by • Label matches that on the LHS

• Annotation to be created follows the label

(Annotation1):match

:match.NE = {feature1 = value1, feature2 = value2}

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Example Rule for DatesMacro: ONE_DIGIT({Token.kind == number, Token.length == "1"})

Macro: TWO_DIGIT({Token.kind == number, Token.length == "2"})

Rule: TimeDigital1// 20:14:25( (ONE_DIGIT|TWO_DIGIT){Token.string == ":"} TWO_DIGIT ({Token.string == ":"} TWO_DIGIT)?(TIME_AMPM)?(TIME_DIFF)?(TIME_ZONE)? ):time-->:time.TempTime = {kind = "positive", rule =

"TimeDigital1"}

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Identifying patterns in corpora

• ANNIC – ANNotations In Context• Provides a keyword-in-context-like interface for

identifying annotation patterns in corpora• Uses JAPE LHS syntax, except that + and *

need to be quantified• e.g. {Person}{Token}*3{Organisation} – find all

Person and Organisation annotations within up to 3 tokens of each other

• To use, pre-process the corpus with ANNIE or your own components, then query it via the GUI

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ANNIC Demo

• Formulating queries

• Finding matches in the corpus

• Analysing the contexts

• Refining the queries

• Demo

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Using phases

• Grammars usually consist of several phases, run sequentially

• A definition phase (conventionally called main.jape) lists the phases to be used, in order

• Only the definition phase needs to be loaded• Temporary annotations may be created in early

phases and used as input for later phases• Annotations from earlier phases may need to be

combined or modified

60

Matching algorithms and Rule Priority

• Rules compete within a single phase!• 3 styles of matching:

– Brill (fire every rule that applies)– First (shortest rule fires)– Appelt (use of priorities)

• Appelt priority is applied in the following order– Starting point of a pattern– Longest pattern– Explicit priority (default = -1)

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Nam

ed E

ntiti

es in

GA

TE

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Using co-reference

• Orthographic co-reference module matches proper names in a document

• Improves results by assigning entity type to previously unclassified names, based on relations with classified entities

• May not reclassify already classified entities• Classification of unknown entities very useful for

surnames which match a full name, or abbreviations, e.g. [Bonfield] will match [Sir Peter Bonfield]; [International Business Machines Ltd.] will match [IBM]

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Named Entity Coreference

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GATE 4.0

• Before end 06• Faster and leaner!• Nicer GUI• ANNIC included• Improved Machine Learning API

(based on YALE)• and more…

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2. GATE overview








66

Information Extraction for the Semantic Web

• Traditional IE is based on a flat structure, e.g. recognising Person, Location, Organisation, Date, Time etc.

• For the Semantic Web, we need information in a hierarchical structure

• Idea is that we attach semantic metadata to the documents, pointing to concepts in an ontology

• Information can be exported as an ontology annotated with instances, or as text annotated with links to the ontology

67

Richer NE Tagging

• Attachment of instances in the text to concepts in the domain ontology

• Disambiguation of instances, e.g. Cambridge, MA vs Cambridge, UK

68

Magpie: an example

• Developed by the Open University• Plugin for standard web browser• Automatically associates an ontology-based

semantic layer to web resources, allowing relevant services to be linked

• Provides means for a structured and informed exploration of the web resources

• e.g. looking at a list of publications, we can find information about an author such as projects they work on, other people they work with, etc.

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MAGPIE in action

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MAGPIE in action

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GATE and the Semantic Web

• Supports ontologies as part of IE applications - Ontology-Based IE (OBIE)

• Supports semantic annotation and ontology population

• Can combine learning and rule-based methods• Allows combination of IE and IR • Enables use of large-scale linguistic resources

for IE, such as WordNet

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Ontology Management in GATE

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Linking the Text to the Ontology

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Exported Database

75

Evaluation for OBIE• Traditional IE is evaluated in terms of Precision,

Recall and F-measure.

• But these are not sufficient for ontology-based IE, because the distinction between right and wrong is less obvious

• Recognising a Person as a Location is clearly wrong, but recognising a Research Assistant as a Lecturer is not so wrong

• Similarity metrics need to be integrated so that items closer together in the hierarchy are given a higher score, if wrong

76

Augmented Precision and Recall

• Development of a new BDM (Balanced Distance Metric) which compares key and response concepts wrt a given ontology

• In the case of ontological mismatch, provides an indication of how serious the error is, and weights it accordingly

• BDM provides a score between 0 and 1 for each key/response match instead of a binary measure

77

Augmented Precision and Recall

Spurious+BDM=AP

BDMMissing+BDM

=ARBDM

BDM is integrated with traditional Precision and Recall in the following way to produce a score at the corpus level:

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Examples of misclassificationEntity Response Key BDM

Sochi Location City 0.724

FBI Org GovOrg 0.959

Al-Jazeera Org TVCompany 0.783

Islamic Jihad Company ReligiousOrg 0.816

Brazil Object Country 0.587

Senate Company Political Entity

0.826

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2. GATE overview







Ontology Learning with Text2Onto

http://ontoware.org/projects/text2onto/

Johanna Vö[email protected]

Institute AIFBUniversity of Karlsruhe

http://ontoware.org/projects/text2onto/

mailto:[email protected]

81

Agenda

• Ontology Learning– Tasks– Problems

• Text2Onto– Overview– Architecture– Linguistic preprocessing– Ontology learning approaches– Summary

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Ontology Learning

• Extraction of (domain) ontologies from natural language text– Machine learning– Natural language processing

• Tools: OntoLearn, OntoLT, ASIUM, Mo’K Workbench, JATKE, TextToOnto, …

83

Ontology Learning – Tasks

Concept extraction car, vehicle, person

Concept classification subclass-of( car, vehicle )

Instance extraction Peter, his-car

Instance classification instance-of( Peter, person )

Relation extraction drive( person, car )

Relation instance extraction drive( Peter, his-car )

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instance-of( Hewlett Packard, organization )

subclass-of( research, activity )

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reach( information, people )

address_in( issue, article )

subclass-of( resource, knowledge )

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Ontology Learning – ProblemsText Understanding

• Words are ambiguous– ‘A bank is a financial institution. A bank is a piece of furniture.’ subclass-of( bank, financial institution ) ?

• Natural Language is informal– ‘The sea is water.’ subclass-of( sea, water ) ?

• Sentences may be underspecified– ‘Mary started the book.’ read( Mary, book_1 ) ?

• Anaphores– ‘Peter lives in Munich. This is a city in Bavaria.’instance-of( Munich, city ) ?

• Metaphores, …

87

• What is an instance / concept?– ‘The koala is an animal living in Australia.’instance-of( koala, animal ) subclass-of( koala, animal ) ?

• How to deal with opinions and quoted speech?– ‘Tom thinks that Peter loves Mary.’love( Peter, Mary ) ?

• Knowledge is changing– instance-of( Pluto, planet ) ?

Conclusion: • Ontology learning is difficult. • What we can learn is fuzzy and uncertain. • Ontology maintenance is important.

Ontology Learning – Problems Knowledge Modeling

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Text2Onto

• Support for (semi-)automatic ontology extraction from natural language text

• Support for ontology maintenance and data-driven ontology evolution by incremental ontology learning

• Model of Possible Ontologies (POM) Confidence / relevance values attached to all

concepts, instances and relations• Enhanced user interaction• Maintenance of multiple modeling alternatives in parallel• Independence of certain ontology language

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subclass-of( user, human ) / confidence 1.0

subclass-of( document, communication ) / confidence 0.75

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• Explicit modeling of evidences– Algorithms provide different types of evidences – Explanation component

• References for annotation and change detection

• Explicit modeling of changes– Corpus, evidence, reference and ontology changes– Future work: ontology change strategies

Text2Onto – Evidence, Reference and Change Management

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Text2Onto – Workflow

Workflow composition

• Complex algorithms– Different types of

algorithms for each ontology learning task

– Flexible combination of results

• Combination strategies– minimum, maximum,

average, linear, classifier, …

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POM Visualization

WorkflowManager

API

GATE

Corpus

Algorithm Controller

OWLWriter

RDFSWriter

F-LogicWriter

POM

Evidence Store

Reference Store

Text2Onto

Ontology

93

Linguistic PreprocessingGATE

• Standard ANNIE components for– Tokenization– Sentence splitting– POS tagging– Stemming / lemmatizing

• Self-defined JAPE patterns and processing resources for– Stop word detection– Shallow parsing

• GATE applications for English, German and Spanish

94

Ontology Learning Approaches Concept Classification

• Heuristics– ‘image processing software’subclass-of( image processing software, software )

• Patterns– ‘animals such as dogs’– ‘dogs and other animals’– ‘a dog is an animal’ subclass-of( dog, animal )

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JAPE Patterns for Ontology Learning

rule: Hearst_1(

(NounPhrase):superconcept{SpaceToken.kind == space}{Token.string=="such"}{SpaceToken.kind == space}{Token.string=="as"}{SpaceToken.kind == space}(NounPhrasesAlternatives):subconcept

):hearst1-->:hearst1.SubclassOfRelation = { rule = "Hearst1" },:subconcept.Domain = { rule = "Hearst1" },:superconcept.Range = { rule = "Hearst1" }

96

Ontology Learning Approaches Instance Classification

• Context similarity‘Columbus is the capital of the state of Ohio.Columbus has a population of about 700.000inhabitants.’

• Columbus ( capital (1), state (1), Ohio (1), population (1), inhabitant (1) )

• city ( country (2), state (1), inhabitant (2), mayor (1), attraction (1) )

• explorer( ship (1), sailor (2), discovery (1) )

instance-of( Columbus, city )

97

Ontology Learning Approaches Relation Extraction

• Subcategorization frames– ‘Tina drives a Ford.’

•instance-of( Tina, person )•instance-of( Ford, vehicle )

– ‘Her father drives a bus.’•subclass-of( father, person )•subclass-of( bus, vehicle )

subcat: drive( subj: person, obj: vehicle )drive( person, vehicle )

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incluyen( ontologiás, definiciones ) / confidence 1.0

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Other Ontology Learning Approaches

• WordNet– Hyponym( ‘bank’, ‘institution’ ) subclass-of( bank, institution ) ?

• Google– ‘cities such as London’, ‘persons such as London’ …– ‘such as London’ instance-of( London, city ) ?

• Instance clustering– Hierarchical clustering of context vectors

• Formal Concept Analysis (FCA)– breathe( animal )– breathe( human ), speak( human ) subclass-of( human, animal ) ?

100

Summary

• Ontology Learning is difficult, because– Language is fuzzy– Knowledge is changing

• Text2Onto targets these Problems– Model of Possible Ontologies– Heterogeneous sources of evidence– Incremental ontology learning

Thanks!

http://www.aifb.de/WBS/jvo/ontology-learning

http://www.ontoware.org/projects/text2onto

http://www.aifb.de/WBS/jvo/ontology-learning

http://www.ontoware.org/projects/text2onto

102



2. GATE overview







Focused Ontology Learning with GATE

Marta Sabou

A Practical Report on Learning Web Service Ontologies

Goal of the Talk

The goal of this talk is:

•To describe a Semantic Web relevant task: Focused

Ontology Learning.•To exemplify this task in the context of Web Services.•To show how focused ontology learning can be

implemented in GATE.

The focus of the talk is NOT ontology learning but the elements of GATE that helped to perform this task.

Outline

1) Generic Problem:* Focused Ontology Learning(definition and characteristics)

2) Specific Problem:* Learning Web Service Ontologies(Context, Problem Scenario)

3) GATE support for:* writing extraction patterns* evaluating term extraction performance

106

Ontology Learning in Restricted Domains

Focused Ontology Learning:• is Ontology Learning in a restricted domain, for a well-defined task• therefore, simpler than Ontology Learning in general• more and more frequent with the growth of the Semantic Web

Previous Talk’s conclusion:Generic Ontology Learning is important but difficult because:

•Language is fuzzy•Knowledge is changing

However... The Semantic Web is increasingly used in specialized domains, where:

• Language exhibits (strong) domain characteristics• e.g., mathematics, medicine

• The Knowledge to be extracted is defined by the task for which the ontology will be used

• e.g., searching patient records, accessing drug related articles

107

Focused Ontology Learning

Focused Ontology Learning characteristics:1. (Small) corpus with special (domain/context) characteristics;2. Well defined ontological knowledge to be extracted;3. An easy to detect correspondence between text characteristics

and ontology elements;

4. Usually an easy solution (adaptation of OL techniques);

5. Implemented/adapted by a non NLP-expert.

What is needed to support domain experts?• libraries of basic NLP tools/data structures;• tools to easily adapt/combine these NLP elements;• intuitive way to create and debug own applications;• usability plays an important role;• generic methodologies of ontology learning rather than hard-coded

algorithms.

Outline



3) GATE support for:* writing extraction patterns (given)* evaluating term extraction performance (given)

109

Context - Semantic Web Services* Semantic WS - semantically annotated WS

* to automate discovery, composition, execution

< rdf:ID=”WS1"> <owls:hasInput rdf:resource=” ”/> <owls:hasInput rdf:resource=” ”/> <owls:hasOutput rdf:resource=” ”/></ >

do:HotelBooking

do:HotelReservationdo:HotelBooking

do:Hoteldo:ReservationDates

=>broad domain coverageBut…increasing nr. of web services

110

A real life story…•Semantic Grid middleware to support in silico experiments in biology•Bioinformatics programs are exposed as semantic web services

150(Services)

4 months!!

Domain Expert

550 ConceptsBut only 125 (23%) usedfor SWS tasks

600(Services)

Our GOAL: Support Expert to learn:1) From more services2) In less time3) A “Better” ontology (for SWS descriptions)

111

FOL Characteristics - 1

* Data Source: * short descriptions of service functionalities* characteristics:

* small corpora (100/200 documents)* employ specific style (sublanguage)

•Replace or delete sequence sections.

•Find antigenic sites in proteins.

•Cai codon usage statistic.

1. (Small) corpus with special (domain/context) characteristics

112

•Web Service Ontologies contain: •A Data Structure hierarchy•A Functionality hierarchy

2. Well defined ontology structure to be extracted


113

3. An easy to detect correspondence between text characteristics and ontology elements

Replace or delete sequence sections.

NP VB_NP


114

Generic Solution: Implementation:

Linguistic Analysis English Tokenizer

Sentence Splitter

POS Tagger

|Replace| |or| |delete| |sequence| ….

Replace or delete sequence sections.(VB) (Prep) (VB) (NN) (NNS)

FOL Characteristics - 44. Usually an easy solution (adaptation of OL techniques).E.g. Pos Tagging

JAPE Rules Replace or delete sequence sections.(VB) (Prep) (VB) (NN) (NNS)

r1 => (NP)

r2 => (Funct)

Extraction Patterns

Ontology Building

Ontology Pruning

OntologyBuilding&Pruning

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Ontology Building

Ontology Pruning

OntologyBuilding&Pruning

Linguistic Analysis Miniparword : 1 : replace : replace : V : * : i : word : 2 : or : or : U : 1 : lex-mod : word : 3 : delete : delete : V : 1 : lex-dep : word : 4 : sequence : sequence : N : 5 : nn : word : 5 : sections : section : N : 1 : obj :

Extraction Patterns JAPE Rules Replace or delete sequence sections.(VB) (Prep) (VB) (NN) (NNS)

r1 => (NP)

r2 => (Funct)

r2 => (Funct)

FOL Characteristics - 44. Usually an easy solution (adaptation of OL techniques).E.g. Dependency Parsing

Outline



3) GATE support for:* writing extraction patterns * evaluating term extraction performance

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* Easy to follow extraction (step by step) * Easy to adapt for domain engineers

GATE Implementation

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Pattern based rules – Example ( (DET)*:det ( (ADJ)|(NOUN))*:mods (NOUN):hn):np:np.NP={}

A noun phrase consists of:• zero or more determiners;• zero or more modifiers which can be adjectives or nouns;• One noun which is the head-noun.

Macro: ADJ( {Token.category == JJ, Token.kind == word}| {Token.category == JJR, Token.kind == word}| {Token.category == JJS, Token.kind == word} )

The ADJ macro identifies any Token tagged as JJ, JJR or JJS.

DET, ADJ, NOUN are macros – make rules more readable.

Extract NP(data) from NP(aaindex).

Displays NP(a non-overlapping wordmatch dotplot) of two NP(sequences)

Outline



3) GATE support for:* writing extraction patterns (given)* evaluating term extraction performance (given)

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Performance EvaluationLinguistic Analysis

Extraction Patterns

Ontology Building

Ontology Pruning

A set of important terms are extracted. Terms are indicated by annotations of type: NP, Funct.

* The correctness of these terms has a direct influence on the correctness of the OB step => evaluating them is important.

•The Corpus Benchmark Tool of GATE compares annotation types in 2 corpora, usually:

• the manually annotated Gold Standard corpus and• the automatically annotated corpus.

• It identifies correct, missed and spurious annotations of a certain type and computes Precision and Recall per each document and the whole corpus.

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Gold Standard Annotations:: Automatic Annotation:

105_profit.xml; Keys : 2Resp : 3

Annotation Type

Precision Recall

Funct 0.666666 1.0

Scan a sequence or database with a matrix or profile.

Funct(scan_sequence)Funct(scan_database)

Funct(scan_sequence)Funct(scan_database)Funct(scan_profile)

Correct = correctly identified annotations (true positives)Spurious = incorrect annotations (false positives)

Example 1:


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Gold Standard Annotations:: Automatic Annotations:

104_printsextract.xml; Keys : 1Resp : 0

AnnotationType

Precision Recall

Funct NaN 0.0

Preprocess the prints database for use with the program pscan.

Funct(preprocess_prints database)

Missed = unidentified annotations (false negative)

Example 2:


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Annotation Type

CorrectPartially Correct

Missing Spurious Precision Recall F-Measure

Funct 70 0 78 3 0.958904 0.47297 0.63348416

Statistics

GoldStandard_Terms

Extracted_Terms

correct

missed

spurious


Precision= correct/(All_Extr)

Recall= correct/(All_GS)

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PROS:•It is very important when developing term extraction.•It allows evaluating:

•1) the performance of the linguistic analyses•2) the coverage of the patterns

•Allows comparing the performance of different tools:•E.g. two different POS taggers

•Easy to use (both from GUI and command line)

Possible improvement:* The current textual output does not allow to directly access all spurious or all missing annotations (these are important when fine-tuning the extraction).* We try to improve this usability issue through visualisation.


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Summary

• Focused Ontology Learning = OL in a restricted domain.

• GATE supports the development of FOL in many ways: • allows easy reuse and combination of basic NLP modules;• offers software libraries for fundamental NLP data structures (Documents, Corpora, Annotations);• incorporates evaluation mechanisms;• easy to debug and use for non-NLP experts.

• Example FOL = OL for Web Services.

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2. GATE overview







KIM Platform An Overview

Atanas KiryakovOntotext Lab, Sirma AI

[email protected]

http://www.ontotext.com/kim/

mailto:[email protected]

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Semantic Annotation: An exampleXYZ was established on 03 November 1978 in London. It opened a plant in Bulgaria in …

Ontology & KB

Company

type

HQ

establOn

City Country

Location

partOf

type

type type

“03/11/1978”

XYZ

London

UK Bulgaria

HQpartOf

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Semantic Annotation of NEsA Semantic Annotation of the named entities (NEs) in a text includes:

- a recognition of the type of the entities in the text

-out of a rich taxonomy of classes (not a flat set of 10 types);

- an identification of the entities, which is also a reference to their semantic description.

The traditional (IE-style) NE recognition approach results in:

<Person>Lama Ole Nydahl</Person>

The Semantic Annotation of NEs results in:

<ReligiousPerson ID=“http://..kim/Person111111”>Lama Ole Nydahl

</ReligiousPerson>

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Platforms for Large-Scale Semantic Annotation

• Allow use of corpus-wide statistics to improve metadata quality, e.g., disambiguation

• Automated alias discovery • Generate SemWeb output (RDF, OWL)• Stand-off storage and indexing of metadata• Use large instance bases to disambiguate to• Ontology servers for reasoning and access• Architecture elements:

– Crawler, onto storage, doc indexing, query, annotators– Apps: sem browsers, authoring tools, etc.

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The KIM Platform• A platform offering services and infrastructure for:

– (semi-) automatic semantic annotation and

– ontology population

– semantic indexing and retrieval of content

– query and navigation over the formal knowledge

• Based on an Information Extraction technology

• Aim: to arm Semantic Web applications

- by providing a metadata generation technology

- in a standard, consistent, and scalable framework

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KIM Architecture

SemanticRepository API

Semantic Annotation

API

Query API

Index API

Document Persistence

API

KIM Web UI

Annotation Server

News Collector

Any WebBrowser

BrowserPlug-in

CustomApplications

CustomBack-end

Custom IE

Entity Ranking

KIM Server RMI

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PROTON Ontology- a light-weight upper-level

ontology;

- 250 NE classes;

- 100 relations and attributes;

- 200.000 entity descriptions;

- covers mostly NE classes, and ignores general concepts;

- includes classes representing lexical resources.

proton.semanticweb.org

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KIM Scaling on Data

• The Semantic Repository is based on Sesame.

• Our practical tests demonstrate a good performance on top of:

– 1.2M entity descriptions:

– about 15M explicit statements;

– above 30M statements after forward chaining.

• Document and annotation storage and indexing with Lucene:

– .5M docs, processed on a $1000-worth machine;

– retrieval in milliseconds.

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Simple Usage: Highlight, Hyperlink, and …

136

Simple Usage: … Explore and Navigate

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How KIM Searches BetterKIM can match a Query:

Documents about a telecom company in Europe, John Smith, and a date in the first half of 2002.

With a document containing:

“At its meeting on the 10th of May, the board of Vodafone appointed John G. Smith as CTO"

The classical IR could not match:

- Vodafone with a "telecom in Europe“, because:- Vodafone is a mobile operator, which is a sort of a telecom;

- Vodafone is in the UK, which is a part of Europe.

- 5th of May with a "date in first half of 2002“;

- “John G. Smith” with “John Smith”.

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Entity Pattern Search

139

Pattern Search: Entity Results

140

Entity Pattern Search: KIM Explorer

141

Pattern Search, Referring Documents

142

Document Details

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Summary

KIM is a platform for: - semantic annotation and ontology population,- semantic indexing and retrieval,- providing an API for remote access and integration,- based on Information Extraction (IE) using GATE.

KIM is: - Robust- Scalable- General-purpose, off the shelf platform!

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THANK YOU!(for not snoring)

The slides: http://www.gate.ac.uk/sale/talks/ekaw2006/ekaw2006-tutorial.ppt

[This work has been supported by SEKT (http://sekt.semanticweb.org/)

andKnowledgeWeb (http://knowledgeweb.semanticweb.org/ )]

http://www.gate.ac.uk/sale/talks/ekaw2006/ekaw2006-tutorial.ppt

http://www.gate.ac.uk/sale/talks/ekaw2006/ekaw2006-tutorial.ppt

http://sekt.semanticweb.org/

http://knowledgeweb.semanticweb.org/

human language technology (hlt) and knowledge acquisition for the semantic web: a tutorial

Documents

semantic information

ontology expertsmanually

instances of concepts

implicit metadata extraction

semantic webthe semantic

hypertextonce metadata

monitoringsuch metadata

endogenous information