citius.usc.es

Centro Singular de Investigación en Tecnoloxías da Información

UNIVERSIDADE DE SANTIAGO DE COMPOSTELA

Graph based semantic annotation for enriching documents with linked data

Supervisors: Manuel Lama, Juan C. Vidal

Estefanía Otero García

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Description of the problem

Learning Fruits

Digital books with clear and consisttext. They have a friendly interface and interactive audiovisual resources:

• The content is identical to the textbooks, but with links to other content to access to relevant and complementary information.

• They are based on an XML format that describes the structure of the LF. It facilitates their use by the e-learning applications.

Description of the problem

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Open problems

PROB1Get additional and complementary information about terms that:

• there are no links to complementary web pages or other content

• not provide explanations .

PROB2Search information inside the content of the LF:

• It is necessary to explore through links to external content and web pages

1oc

1oc

ADEGA 1.0

Solution

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Semantic annotation of the Learning Fruit content

Automatically associate the set of relevant terms of the LF to instances of a densely populated ontology

• Each relevant term is associated with a graph of instances that contains information about the term in the LF context

• Each instance has a link to an external web page

Required ontology characteristics

• Represents general purpose encyclopedic knowledge

• It is populated with a huge number of instances that have a link to a document or web page

Solution: Ontology

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DBpedia content is modeled using an shallow, corss-domain ontology based on standard vocabularies.• 529 classes and 2,333 properties

The population of the DBpedia ontology takes place extracting data from Wikipedia structured information• Infobox templates• Information from categories

Each DBpedia instance is linked to the Wikipedia page that the information has been extracted

Solution: DBpedia

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Statistics (English version)

Ontology size2.46 billion RDF triples

4 million entities

Entity number

Persons 832,000 Films 78,000Places 639,000 Music Albums 116,000Organizations 209,000 Video games 18,500Species 226,000 Diseases 5,600

External links

Images 24,600,000External web pages 27,600,000RDF repositories 45,000,000Wikipedia Categories 67,000,000YAGO Categories 41,200,000

Solution: DBpedia

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Annotation based on instance GENERATION

Description These proposals identify the terms of the document to annotate and automatically create instances in the ontology. This requires:

• Identify the concept of ontology which the instance belongs.

• Assign values to the attributes of the instance.

Advantage V1 It is no necessary ontologies with huge number of instances.

Limitations L1 They require complex language processing techniques, combined with machine learning techniques to detect the concepts that instances and attribute belongs .

L2 Apply to a very restricted number of general concepts (people, organizations and places).

State of the art

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Annotation based on instance SEARCH

Description These proposals identify the terms of the document to annotate and then they search instances that best represents the semantics of a given term in a densely populated ontology. It is need:

• Apply disambiguation techniques between instances.

• Use context in searche.

Advantages V1 It is not required natural language processing techniques as complex as proposal of the annotation based on instances generation

• Named Entity Recognition• Syntactic and semantic similarity between terms

Limitations L1 It is necessary to have an populated ontology with a huge number of instances

State of the art

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It does not exist any context based proposal that properly annotates a term with a semantic graph

• DBpedia Ranker discard relevant taxonomic relations

State of the art

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It identifies relevant terms that characterize the LF: context

Each term is associated with a DBpedia instances

Semantic graph is obtained by filtering instances using the context

ADEGA 1.0

Framework

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All your Documents Enriched with Graph Annotations

POS Stanford

Morphology Analysis

Set of nouns, propernouns and compoundnouns extracted from the LF content

Pharaoh Egypt

Ancient Egypt Cleopatra VII

Ramesses II tomb

temple Piest

Horus Nile

God …

ADEGA 1.0

Framework: Context

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Hybridization metricsTF-IDF + Jaro-Winkler

SoftTFIDF

Similarity Analysis

Cluster of terms that are composed by words

that share a similar meaning or arise from

the same root.

Pharaoh Egypt

Ancient Egypt Cleopatra VII

Ramesses II tomb

temple priest

Horus Nile

god …

{Egypt, egyptian, egyptologist}

{Cleopatra, Cleopatra VII}

{god, gods}

…

ADEGA 1.0

Framework: Context

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It is calculated using thefrequency weighted by therelevance of each LF field(pα)

Final relevance

Frequency Analysis

Number of times the term appears in theLF fields

{Egypt, egyptian, egyptologist}

{Cleopatra, Cleopatra VII}

{god, gods}

…

LF Context

ADEGA 1.0

Framework: Context

14∑ , ∙ ∙

ANOTATION

CONTEXT EXTRACTION

FrequencySimilarity…

URI

IDENTIFICATION

LEVEL 1 2 3 4

Luxor forma parte de la antigua ciudad llamada Uaset (en egipcio antiguo), o también conocida como Tebas (en griego), denominada por Homero "La ciudad de las cien puertas", por las numerosas puertasEs la ciudad de los grandes templos del antiguo Egipto (Luxor y Karnak), y de las célebres necrópolis de la ribera ...

TebasNecropolisFaraones...

0.710.650.640.61

Paleolithic node

#level #nodes

1 5372 12,8253 156,7704 2,950,620

There are graph nodes that are not relevant to semantically describe the terms of the document

• Context is used to discriminate the relevant instances for the semantic description of the LF

ADEGA 1.0

Framework: Filter graph

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Depth First Search algorithm (depth limited)

• The exploration determines the relevance of each node, which depends on the relevance of the children nodes that are connected

ADEGA 1.0

Framework: Filter graph

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Text nodes

n4

n2 n7

n1

n3

wr23wr24

n5 n6

wr45 wr46

wr12 wr17

URI nodes

DBpedia relation weight

The node isrelevant if itexceeds a thresholdFrequency Diversity

Ancient Egypt Assyrianempyre Babylonia Canary

IslandCantabrianmountain Caspian sea Caucasus Cleopatra

VII

Desert Earth Egypt Enlil Euphrates Pharaoh Fossil Giza

God Guadalquivir Gudea Hammurabi Homo

ErectusHomo Habilis Horus Human

Ishtar Neanderthal Nile Oceanicclimate Osiris Paleolithic Prehistory ...

Learning Fruits #terms

The landscape of the earth 7

The river civilizations of Mesopotamia 13

The landscape of Spain and Europe 10

The Paleolithic and our remote ancestors 10

Ancient Egypt 10

ADEGA 1.0

Validation

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ADEGA 1.0

Validation: Results

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Comparative: ADEGA vs RelFinder

• RelFinder is set with a exploration depth of 2 levels and the context terms are introduced as the input.

• F1-score is used as a comparisonparameter between ADEGA andRelFinder, using the same numberof instances in both algorithms.

ADEGA 1.0

Validation: Comparative

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ADEGA 1.0Computational Issues

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Each additional level of exploration increments exponentially the number of visited nodes

Jump from level to 1 implies visiting nodes

Exploration results for 1 term

Variable Value

Averaged nodes visited 248,035.02

Average nodes discarded 199,461.73

Average nodes processed 48,573.29

Average literals processed 44,165.53

Average URL processed 4,407.76

Average number of SPARQL queries 22,882.64

Mean time per query in ms 9.91

Mean time of ADEGA (depht = 3) in ms 376,414.17

80% discarded nodes

91% text nodesThe most costly nodes

67% of computational time was used to query DBpedia

x10 terms (avg) = 50 min to obtain a solution

Journal Publications

"Semantic Linking of Learning Object Repositories to Dbpedia”. Manuel Lama, Juan Carlos Vidal, Estefanía Otero-García, Alberto Bugarín, and Senén Barro. Educational Technology & Society 15, no. 4 (2012): 47-61.JCR = 1.171

“Graph-based semantic annotation for enriching documents with linked data”. Juan C. Vidal, Manuel Lama, Estefanía Otero-García, Alberto Bugarín.Knowledge-Based Systems (2013)JCR = 4.104

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Publications

Demo user interfaceADEGA

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All your Documents Enriched with Graph Annotationshttp://tec.citius.usc.es/adega

UNIVERSIA ANNOTATIONADEGA applications

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Clasification of Universia resourceshttp://tec.citius.usc.es/universia/lookup/

MENTOR EMPRENDEADEGA applications

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MENTOR EMPRENDEhttp://www.redemprendia.org/mentor/mentoremprende

Questions?