Metadata first, ontologies second

Towards a solution to extract knowledge from the social web (“metadata first, ontologies second”) Project Collaborative Ontology Building System (CollOnBus) INTEK Nets 2005-2007 Aitor Almeida, Borja Sotomayor, Joseba Abaitua , Diego Lopez de Ipiña

Social web: source of knowledge

Crowds share and tag resources of different types:

pictures, music, posts, videoclips, slides, books, bookmarks, etc.

Social tagging (or crowd- tagging ) is a very effective and economic way of generating knowledge

Crowdsourcing “the trend of leveraging the mass collaboration enabled by Web2.0 technologies to achieve business goals. ”

<http://en.wikipedia.org/wiki/Crowdsourcing>

Related work (since 2006)

mapping tags to ontologies

Schmitz 2006. Inducing Ontology from Flickr tags. WWW’2006: Collaborative Web Tagging workshop

Abbasi et. al. 2007. Organizing Resources on Tagging Systems using T-ORG. ESWC2007 SemNet workshop

identifying semantic relations

Specia, Motta. 2007. Integrating Folksonomies with the Semantic Web. ESWC2007

transforming folksonomies into formal representations

Marlow et al. 2006. Tagging, Taxonomy, Flickr, Article, ToRead. WWW’2006: Collaborative Web Tagging workshop

Hotho et al. 2006. Trend Detection in Folksonomies . Semantics And Digital Media Technology SAMT2006

Maala et. Al. A Conversion Process From Flickr Tags to RDF Descriptions. BIS2007 workshop

Which knowledge representation model?

Extracting knowledge from data sharing Web 2.0 sites, but into which formal representation?

Semantic Networks

Lexical networks (WordNet)

Taxonomines

eg. categories from Wikipedia, Thesauri

Metadata

“ mapping to Dublin Core is a weak choice”

Ontologies

“ metadata first, ontologies second”

Crowds tagging pictures

Crowds tagging pictures Aitor Almeida Borja Sotomayor Diego López de Ipiña

Crowds tagging pictures

Crowds tagging posts

Crowds tagging slides

Crowds tagging books

Crowds tagging URL

Crowd-sharing of tags

Flickr, del.icio.us... group tags by social sharing (or “co-usage”)

but the semantic information that socially shared tags acquire is poorly exploited

Mapping folksonomies into tag clusters

RawSugar <http://rawsugar.com/>

allows users to assign hierarchies to their tags, improving the navigation and searching of folksonomies

non-expert users will find it easier to tag resources without any restrictions

Tag clustering

TAG clustering is the main technique used to improve the wealth of social tagging

but semantic relations are not detected

Beyond tag clusters?

Should we map them into ontologies?

Better mapping 1st into metadata

Metadata vs ontologies

Why are metadata structures better than ontologies (for resource classification and categorisation)?

Let’s reflect on different knowledge representations and about who use them:

Folksonomies (crowds)

Taxonomies, ontologies (knowledge engineers, AI/SW practitioners)

Metadata structures (librarians, archivists, documentalists)

What are metadata?

TAG vs metadata ?

Metadata vs ontologies

Why are metadata structures better ?

Because metadata provide wide and complete range of facets for representing knowledge about an entity or resource

Each facet (or data type) could be part of one or several ontological structures

Facet “any of the definable aspects that make up a subject (as of contemplation) or an object (as of consideration)”

“ A faceted classification system allows the assignment of multiple classifications to an object, enabling the classifications to be ordered in multiple ways, rather than in a single, pre-determined, taxonomic order” (Wikipedia).

Better mapping 1st folksonomies into metadata structures

Dublin Core Metadata Initiative http://jodi.tamu.edu/Articles/v02/i02/Greenberg/metadataform.gif

Dublin Core Metadata Initiative

Dublin Core Metadata Inicitive

Our mapping tool: folk2onto (? folk2meta) designed by Borja Sotomayor

folk2onto: Tag Distiller

Tag Distiller :

Downloads tags from Web 2.0 sites

Matches each tag against WordNet (taking into account the tag’s context/cloud)

Filters out synonyms

Keeps the list of remaining tags

Generates an XML file

Implemented by Aitor Almeida

TAG clouds from Technorati

<?xml version="1.0" encoding="utf-8"?>

<!DOCTYPE tapi PUBLIC "-//Technorati, Inc.//DTD TAPI 0.02//EN" "http://api.technorati.com/dtd/tapi-002.xml">

<tapi version="1.0">

<document>

<result>

<querycount>13</querycount>

</result>

<item>

<tag>christmas cookie recipes</tag>

<posts>274</posts>

</item>

… .

Tagged URL at Technorati

All <tag> elements are downloaded

To get the “title” http://api.technorati.com/bloginfo?key= [apikey] &url= [blog url]

And<name> is recovered

semantic relations in WordNet

WordNet relations for tag ‘Spanish’:

TAG filtering algorithm

Tags are filtered out by means of WordNet

If a TAG has only one meaning (synset) that meaning is assigned

If it has more than one, then

T: resources tag set

Related(a,b): gives 1 if a and b have some type of relation (hypernym, hyponym, holonym, meronym)

w: weights

Several iterations are made until a meaning is found (10 iterations max.)

TAG filtering algorithm

Once senses have been discarded, synonyms are also filtered out

Words then are grouped in senses using WordNet’s relation network

The output is exported to a:

XML file with senses

XML file with tags that were discarded

RDF containing WordNet’s relation network

TAG XML file

<?xml version="1.0" encoding="UTF-8"?>

<resource>

<tittle>PostgreSQL: Perguntas Frequentes (FAQ) sobre PostgreSQL</tittle>

<type>Text</type>

<format>text/html</format>

<identifier>www.postgresql.org/docs/faqs.FAQ_brazilian.html</identifier>

<tags>

<tag>

<lemma>tune</lemma>

< idlex>236726</idlex>

</tag>

<tag>

<lemma>bd</lemma>

<idlex>5604473</idlex>

</tag>

TAG file without senses

<resource>

<tittle>Wired News: The Virus That Ate DHS</tittle>

<type>Text</type>

<format>text/html</format>

<identifier>www.wired.com/news/technology/0,72051-0.html?tw=rss.index</identifier>

<tags>

<tag>bit200f06</tag>

<tag>group141</tag>

<tag>dhs</tag>

<tag>group35</tag>

<tag>malware</tag><tag>group91</tag><tag>group17</tag>

<tag>group53</tag>

<tag>computer_security</tag>

</tags>

</resource>

WordNet’s sense sets

Words are grouped in sense sets

If related(a,b) is = 1, then words are grouped in the same set

The relations depth has to be equal or smaller than 3

folk2onto: Tag Trainer

folk2onto: Map Trainer

folk2onto: Tag Mapper

The Mapper makes tag-element associations

These associations are made according to the senses asigned by the Distiller

Mapping targets into Dublin Core metadata records

folk2onto: Dublin Core

The Distiller gets 4 elements from the tag source (del.icio.us, Technorati, etc.):

Title : URL’s title -> from the <title> XML tag

Type : content type -> depending on the source (here both are “Text”)

Format : MIME class -> depending on the source (here we have 2 text/html)

Identifier : we take the resource’s URL

folk2onto: Dublin Core

The Tag-Mapper deals with:

Subject : the “topic”.

Language : en, es, fr, de, ru...

Coverage : when, where (about the topic)

Rights : type of licence

folk2onto: mapping formulae

When a TAG has one mapping, that TAG is used

If it has more than one:

If it has no mapping, then:

folk2onto: file mapping

<rdf:RDF

xmlns:j.0="http://purl.org/dc/elements/1.1"

xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" >

<rdf:Description rdf:nodeID="A0">

<rdf:type rdf:resource="http://purl.org/dc/elements/1.1identifier"/>

<j.0:identifier>www.postgresql.org/docs/faqs.FAQ_brazilian.html</j.0:identifier>

<j.0:type>Text</j.0:type>

<j.0:format>text/html</j.0:format>

<j.0:tittle>PostgreSQL: Perguntas Frequentes (FAQ) sobre PostgreSQL</j.0:tittle>

<j.0:subject>database</j.0:subject>

<j.0:subject>performance</j.0:subject>

<j.0:subject>bd</j.0:subject>

</rdf:Description>

</rdf:RDF>

Mapping trainer

folk2onto: 6 tests (A-F)

Experiment A : Selecting random synsets for the tags.

Experiment B : Without any limit in the semantic relation depth. Only taking into account the trained synsets (frec=0, wordnet=0, trained=1).

Experiment C : Without any limit in the semantic relation depth. Only taking into account the context (frec=0, wordnet=1, trained=0).

Experiment D : Without any limit in the semantic relation depth. Taking the context and the trained synsets into account (frec=0,=wordnet0.4, trained=0.6).

Experiment E : Without any limit in the semantic relation depth. Taking al three components of the equation (familiarity, context and trained synsets) into account (frec=0.1, wordnet=0.3, trained=0.6).

Experiment F : Limiting the semantic relation depth to 3 and taking the context and the trained synsets into account. (frec=0, wordnet=0.4, trained=0.6).

folk2onto: tests output 278 (%12.8) 1894 (%87.2) F 823 (%37.9) 1349 (%62.1) E 680 (%31.3) 1492 (%68.7) D 973 (%44.8) 1199 (%55.2) C 578 (%26.6) 1594 (%73.4) B 1466 (%67.5) 706 (%32.5) A Erroneous synsets Correct synsets Experiment

folk2onto: tests output

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