Graph based semantic annotation for enriching documents with linked data

Transcription

1 Graph based semantic annotation for enriching documents with linked data Estefanía Otero García Supervisors: Manuel Lama, Juan C. Vidal Centro Singular de Investigación en Tecnoloxías da Información UNIVERSIDADE DE SANTIAGO DE COMPOSTELA citius.usc.es

2 Description of the problem Learning Fruits Digital books with clear and consist text. 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. 2

3 Description of the problem Open problems PROB1 Get additional and complementary information about terms that: there are no links to complementary web pages or other content not provide explanations. 1oc 1oc PROB2 Search information inside the content of the LF: It is necessary to explore through links to external content and web pages 3

4 Solution ADEGA 1.0 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 4

5 Solution: Ontology 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 5

6 Solution: DBpedia 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 6

7 Solution: DBpedia Statistics (English version) Ontology size Entity number External links 2.46 billion RDF triples 4 million entities Persons 832,000 Films 78,000 Places 639,000 Music Albums 116,000 Organizations 209,000 Video games 18,500 Species 226,000 Diseases 5,600 Images 24,600,000 External web pages 27,600,000 RDF repositories 45,000,000 Wikipedia Categories 67,000,000 YAGO Categories 41,200,000 7

8 State of the art 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). 8

9 State of the art 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 9

10 State of the art It does not exist any context based proposal that properly annotates a term with a semantic graph DBpedia Ranker discard relevant taxonomic relations 10

11 Framework ADEGA 1.0 All your Documents Enriched with Graph Annotations 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 11

12 Framework: Context ADEGA 1.0 Morphology Analysis Set of nouns, proper nouns and compound nouns extracted from the LF content POS Stanford Pharaoh Ancient Egypt Ramesses II temple Horus God Egypt Cleopatra VII tomb Piest Nile 12

13 Framework: Context ADEGA 1.0 Similarity Analysis Pharaoh Ancient Egypt Ramesses II temple Horus god Egypt Cleopatra VII tomb priest Nile Cluster of terms that are composed by words that share a similar meaning or arise from the same root. SoftTFIDF Hybridization metrics TF-IDF + Jaro-Winkler {Egypt, egyptian, egyptologist} {Cleopatra, Cleopatra VII} {god, gods} 13

14 Framework: Context ADEGA 1.0 {Egypt, egyptian, egyptologist} {Cleopatra, Cleopatra VII} {god, gods} Frequency Analysis Number of times the term appears in the LF fields LF Context Final relevance It is calculated using the frequency weighted by the relevance of each LF field (p α ), 14

15 Framework: Filter graph ADEGA 1.0 CONTEXT EXTRACTION LEVEL Paleolithic node Tebas Necropolis Faraones... ANOTATION Frequency Similarity 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 puertas Es la ciudad de los grandes templos del antiguo Egipto (Luxor y Karnak), y de las célebres necrópolis de la ribera... U R I I D E N T I F I C A T I O N #level #nodes , , ,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 15

16 Framework: Filter graph ADEGA 1.0 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 n 1 DBpedia relation weight wr 12 wr 17 URI n 2 n 7 nodes wr wr 24 The node is 23 relevant if it n 3 n 4 exceeds a threshold Frequency Diversity wr 45 wr 46 Text n 5 n 6 nodes 16

17 Validation ADEGA 1.0 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 Ancient Egypt Assyrian empyre Babylonia Canary Island Cantabrian mountain Caspian sea Caucasus Cleopatra VII Desert Earth Egypt Enlil Euphrates Pharaoh Fossil Giza God Guadalquivi r Gudea Hammurabi Homo Erectus Homo Habilis Horus Human Ishtar Neanderthal Nile Oceanic climate Osiris Paleolithic Prehistory... 17

18 Validation: Results ADEGA

19 Validation: Comparative ADEGA 1.0 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 comparison parameter between ADEGA and RelFinder, using the same number of instances in both algorithms. 19

20 Computational Issues ADEGA 1.0 Each additional level of exploration increments exponentially the number of visited nodes Jump from level to 1implies visiting nodes Exploration results for 1 term Variable Value Averaged nodes visited 248, Average nodes discarded 199, Average nodes processed 48, Average literalsprocessed 44, Average URL processed 4, Average number of SPARQL queries 22, Mean time per query in ms 9.91 Mean time of ADEGA (depht = 3) in ms 376, % discarded nodes 91% text nodes The most costly nodes 67% of computational time was used to query DBpedia x10 terms (avg) = 50 min to obtain a solution 20

21 Publications 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): JCR = 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 =

22 ADEGA Demo user interface All your Documents Enriched with Graph Annotations 27

23 ADEGA applications UNIVERSIA ANNOTATION Clasification of Universia resources 28

24 ADEGA applications MENTOR EMPRENDE MENTOR EMPRENDE 29

25 Questions?