Semantic web Bootstrapping & Annotation

Hassan Sayyadi

sayyadi@ce.sharif.edu

Semantic web research laboratory

Computer department

Sharif university of technology

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Outline• What is annotation?

• Why use annotation?

• Crawler

• Annotation model

• Annotation methods

• Our Implementation

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Outline• What is annotation?

• Why use annotation?

• Crawler

• Annotation model

• Annotation methods

• Our Implementation

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What is annotation?• People make notes to themselves in order to

preserve ideas that arise during a variety of activities

• The purpose of these notes is often to summarize, criticize, or emphasize specific phrases or events

• Semantic annotations are to tag ontology class instance data and map it into ontology classes.

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Outline• What is annotation?

• Why use annotation?

• Crawler

• Annotation model

• Annotation methods

• Our Implementation

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Why use annotation?

• To have the world knowledge at one's finger tips seems possible.

• The Internet is the platform for information.

• Unfortunately most of the information is provided in an unstructured and non-standardized form.

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Why use annotation? (continue)

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Outline• What is annotation?

• Why use annotation?

• Crawler

• Annotation model

• Annotation methods

• Our Implementation

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Crawler

• A crawler is a program which traverses the Internet following these links from one page to the next.

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Focused crawler• Not all the Internet knowledge is required for

every query.• This assumption seems reasonable because

most people work on a restricted domain and do not need the knowledge of the whole Internet

• Searching the whole Internet in this case is very inefficient and expensive.

• Free texts in the Internet contain various information in diverse domains.

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Focused crawler (continue)

• The focus can be achieved by examining keywords

• Problems: – “Understanding“ the semantic of document– Extremely focusing on one topic

• Another way to focus is the Internet connectivity structure

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Outline• What is annotation?

• Why use annotation?

• Crawler

• Annotation model

• Annotation methods

• Our Implementation

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Annotation models

• Mark in web page

• Example:– SUT is one of the largest engineering

schools in the Islamic Republic of Iran– <university>SUT</university> is one of the

largest universities in the <country>Islamic Republic of Iran</country>

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Annotation models (continue)• Generate RDF• Example:

– SUT is one of the largest engineering schools in the Islamic Republic of Iran

– <rdf:Description rdf:about="http://sharif.edu/#SUT"> <rdf:type>university</rdf:type>

<SHARIF:be_in rdf:resource="http://sharif.edu/#Islamic+Republic+of+Iran"/>

</rdf:Description> <rdf:Description rdf:about="http://sharif.edu/#Islamic+Republic+of+Iran”> <rdf:type>Country</rdf:type> </rdf:Description>

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Outline• What is annotation?

• Why use annotation?

• Crawler

• Annotation model

• Annotation methods

• Our Implementation

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Annotation methods

• Manually

• Semi-automatically

• Automatically

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Automatic Annotation

• The fully automatic creation of semantic annotations is an unsolved problem.

• Automatic semantic annotation for the natural language sentences in these pages is a daunting task and we are often forced to do it manually or semi-automatically using handwritten rules

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Manual Annotation• Manual annotation is more easily accomplished

today, using authoring tools, which provide an integrated environment for simultaneously authoring and annotating text.

• However, the use of human annotators is often fraught with errors due to factors such as annotator familiarity with the domain, amount of training, personal motivation and complex schemas

• Manual annotation is also an expensive process

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Semi-automatic Annotation

• To overcome the annotation acquisition bottleneck, semiautomatic annotation of documents has been proposed.

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Semi-automatic annotation

• assumptions:– vocabulary set is limited– word usage has patterns– semantic ambiguities are rare– terms and jargon of the domain appear

frequently

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Semantic Annotation Platform (SAP)

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Multistrategy SAPs• Multistrategy SAPs are able to combine

methods from both pattern-based and machine learning-based systems.

• No SAP currently implements the multistrategy approach for semantic annotation, although it has been implemented in systems for ontology extraction (such as On-To-Knowledge)

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Semi-automatic annotation (continue)• Example

– I go to Shanghai

• Link structure is

more like a RDF

graph

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The accuracy of concepts and relations about different algorithm

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Automatic annotation

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Source preprocessing

• Document Object Model (DOM)

• Text Model

• Layout Model

• NLP Model

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Information Identification• Operators

– perform extraction actions on document access models

– Retrieval, Check, Execute• Strategies

– build operator sequences according to user time and quality requirements

• Source Description– build operator sequences according to user time

and quality requirements

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Ontology population• The final stage of the overall process is to

decide which hypothesis represents the extracted information to insert into the ontology

• The module simulates insertions and calculates the cost according to the number of new instance creations, instance modifications or inconsistencies found

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Outline• What is annotation?

• Why use annotation?

• Crawler

• Annotation model

• Annotation methods

• Our Implementation

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Our implementation

• Crawler:– Crawl all link that contains:

• sharif.ir• sharif.edu• sharif.ac.ir

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Our implementation• Source pre-processing

– Html to text• text = text.replaceAll("\n", "*_newline_*");• text = text.replaceAll("\\<script.*?\\</script\\>", "");• text = text.replaceAll("\\<style.*?</style.*\\>", "");• text = text.replaceAll("<\\!--.*?--\\>", "");• text = text.replaceAll("\\<.*?\\>", "");• text = text.replaceAll("&nbsp;", " ");• text = text.replaceAll("&lt;", "<");• …• text = text.replaceAll("\\*_newline_\\*", "\n");

– Additional• text = text.replaceAll("\n(\n|| )*\n",".");• text = text.replaceAll(",", " and ");

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Our implementation

• Information extraction:– JMontyLingua

• SUT is one of the largest engineering schools in the Islamic Republic of Iran

• ("be" "SUT" "one" "of largest engineering school" "in Islamic Republic" "of Iran")

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Our implementation

• JMontyLingua problem:– SUT has computer, mechanic and electric

engineering departments – ("have" "SUT" "computer mechanic and

electric engineering departments")– ("have" "SUT" "computer and mechanic

and electric engineering departments")

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Our inplementation• ("be" "SUT" “university" "in Islamic Republic" "of Iran")

• => ("be" "SUT" “university" "in Islamic Republic of Iran")

• =>SUT,be,university & SUT,be_in,Islamic Republic of Iran

• <rdf:Description rdf:about="http://sharif.edu/#SUT"> <rdf:type>university</rdf:type>

<SHARIF:be_in rdf:resource="http://sharif.edu/#Islamic+Republic+of+Iran"/>

</rdf:Description>

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Any question?