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YAGO 2 : Exploring and Querying World Knowledge in Time, Space, Context, and Many Languages

YAGO 2 : Exploring and Querying World Knowledge in Time, Space, Context, and Many Languages. Published by : Johannes Hoffart, Fabian M. Suchanek, Klaus Berberich, Edwin Lewis- Kelham , Gerard de Melo , Gerhard Weikum - Max Planck Institute for Informatics, Germany

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YAGO 2 : Exploring and Querying World Knowledge in Time, Space, Context, and Many Languages

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  1. YAGO2 : Exploring and Querying World Knowledge inTime, Space, Context, and Many Languages Published by : Johannes Hoffart, Fabian M. Suchanek, Klaus Berberich, Edwin Lewis-Kelham, Gerard de Melo, Gerhard Weikum -Max Planck Institute for Informatics, Germany Presented By : Sumana Venkatesh

  2. ABSTRACT Yago2 - extension of Yago knowledge base with a focus on temporal and spatial knowledge. Automatically built from Wikipedia(e.g., categories, redirects, infoboxes), GeoNames, and WordNet (e.g., synsets). Contains nearly 10 million entities and events, as well as 80 million facts representing general world knowledge. The wealth of spatio-temporal information in YAGO can be explored either graphically or through a special time- and space-aware query language.

  3. OVERVIEW Introduction Extraction Architecture Temporal Dimension Geo-Spatial Dimension Textual Dimension Demo Critique Conclusion

  4. INTRODUCTION Success of Wikipedia and algorithmic advances in information extraction have revived interest in large-scale knowledge bases. KB’s includeDBpedia , KnowItAll , WikiTaxonomy and YAGO. Commercial services include freebase.com, trueknowledge.com, and wolframalpha.com. Describe millions of individual entities, their mappings into semantic classes, and relationships between entities.

  5. Why YAGO2? • RDF model  can store additional fields, however current KB’s are blind to temporal dimension. • Store birth date, death date but does not give an overview of timespan associated with the person and the events that the person can participate in. • Problems at spatial level Store location and locatedIn relations but no geographical dimension to events and entities. • We require a KB which is a comprehensive anchoring of current ontologies along with both geo-spatial and temporal dimension. • Ex: “the era of Elizebeth1”

  6. EXTRACTION ARCHITECTURE The new YAGO2 architecture is based on declarative rules that are stored in text files. The rules take the form of subject-predicate-object triples. The rules themselves are a part of the YAGO2 knowledge base.

  7. TYPES OF RULES • Factual rules - Declarative translations of all the manually defined exceptions and facts from YAGO Knowledge Base. • definitions of all relations, their domains and ranges, and the definition of the classes that make up the YAGO hierarchy of literal types. • Implication rules - if certain facts appear in the knowledge base, then another fact shall be added. • Whenever the YAGO2 extractor detects that it can match facts to the templates of the subject, it generates the fact that corresponds to the object and adds it to the knowledge base.

  8. TYPES OF RULES • Replacement rules - if a part of the source text matches a specified regular expression, it should be replaced by a given string. • Cleaning HTML tags, normalizing numbers, eliminating administrative Wikipedia categories & articles. • “\{\{GA\}\}” replace “[[Georgia]]” • Extraction rules - a part of the source text matches a specified regular expression, a sequence of facts shall be generated. • Apply to patterns found in Wikipedia infoboxes, categories, article titles, headings, links or references.

  9. TEMPORAL DIMENSION • For YAGO2, we can derive the temporal properties of objects from the data in the knowledge base. • All entities that has a meaningful time of existence is taken into consideration. • Ex: People born and pass away, Countries Created and dissolved • Four major entity types with the relations that indicate their time span: • People (with wasBornOn-Dateand diedOnDate), • Groups (Ex: music bands, football clubs, universities, or companies; with wasCreatedOnDateand wasDestroyedOnDate), • Artifacts (Ex: buildings, songs or cities; with wasCreatedOnDate and wasDestroyedOnDate) and • Events (Ex: sports competitions like olympics; with the relations startedOnDate and endedOnDate).

  10. Generic Entity-Time Relations • Two generic entity-time relations: startsExistingOnDate- temporal start point endsExistingOnDate- temporal end point of an entity • The type-specific relations wasBornOnDate, diedOnDate etc. -> sub properties of the two generic relations • Facts can have temporal dimension. • Ex: “ BarackObamaholdsPoliticalPositionPresidentOfTheUnitedStates” • The YAGO2 extractors can find occurrence times of facts from the Wikipedia infoboxes. Ex: Birth, Death, Marriage and Divorce. • Ex: “ BobDylanwasBornIn Duluth ” • “ ElvisPresleydiedIn Memphis ”

  11. GEO-SPATIAL DIMENSION • Entities possessing a permanent spatial extent on Earth Ex: countries, cities, mountains, and rivers. • ClassyagoGeoEntity-> Groups together all entities with a permanent physical location on earth. • Position of geo-entity -> Described by geographical coordinates including latitudes and longitudes. • yagoGeoCoordinates -> data type to store geographical coordinates • hasGeoCoordinates -> Relation that links each geo-entity to its coordinates

  12. Geo-Entities • Geo-entities  Wikipedia + geonames.org • GeoNames contain locatedIn hierarchy. • “Berlin is locatedIn Germany” • Assigns a class to each location. • Berlin : “Capital of a political entity” • Matching Algorithms take care of duplication of classes that exist while we integrate data from Wikipedia and GeoNames (Textual similarity + Geographical coordinates).

  13. Entities, Facts and Location • Location assigned to  Entities + facts • Ex: Location of WoodStock is White Lake, NY which is an instance of yagoGeoEntity. • Events -> Ex: Sports Meet(happenedIn relation) • Groups/Organizations  Ex: company headquarters or university campus locations • Artifacts Ex: Mona Lisa in the Louvre (isLocatedIn relation)

  14. TEXTUAL DIMENSION • For each entity, YAGO2 contains contextual information. • It includes relation hasContext and its sub-properties like • hasWikipediaAnchorText (linking an entity to a string that occurs as anchor text in the entity's article), • hasWikipediaCategory(holds the name of a category in which Wikipedia places the article), • hasCitationTitle(holds the title of a reference on the Wikipedia) • For individual entities, multilingual translations are extracted from inter-language links in Wikipedia, allowing us to query individuals using non-English names.

  15. DEMO - SPOTLX • To facilitate querying Triple + 3 additional Dimensions • 6 Tuple presentation  SPOTLX (SPARQL like Query form) • Subject(s), Predicate(P), Object(O), Time(T), Location(L) and Context(X)

  16. CONCLUSION Methodology for enriching large knowledge bases of entity-relationship-oriented facts along the dimensions of time and space. This has been demonstrated by presenting YAGO2 which has 80 million facts of near human quality. Spatio-temporal knowledge is a crucial asset for many applications including entity linkage across independent sources (e.g., in the Linked Data cloud) and Semantic Search.

  17. REFERENCES(1) • [1] S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, • R. Cyganiak, and Z. Ives. DBpedia: A Nucleus for a • Web of Open Data. In ISWC + ASWC, 2007. • [2] M. Banko, M. J. Cafarella, S. Soderland, • M. Broadhead, and O. Etzioni. Open Information • Extraction from the Web. In IJCAI, 2007. • [3] C. Bizer, T. Heath, and T. Berners-Lee. Linked Data - • the story so far. International Journal on Semantic • Web and Information Systems, 5(3):1{22, 2009. • [4] G. de Melo and G. Weikum. Towards a Universal • Wordnet by Learning from Combined Evidence. In • CIKM, 2009. • [5] F. Giunchiglia, V. Maltese, F. Farazi, and B. Dutta. • GeoWordNet: A Resource for Geo-spatial • Applications. In ESWC, 2010. • [6] C. Gutierrez, C. A. Hurtado, and A. Vaisman. • Introducing Time into RDF. IEEE TKDE, • 19:207{218, 2007.

  18. REFERENCES(2) • [7] J. Hoart, F. M. Suchanek, K. Berberich, and • G. Weikum. YAGO2: A Spatially and Temporally • Enhanced Knowledge Base from Wikipedia. Research • report, Max-Planck-InstitutfurInformatik, 2010. • [8] M. Koubarakis and K. Kyzirakos. Modeling and • Querying Metadata in the Semantic Sensor Web: The • Model stRDF and the Query Language stSPARQL. In • The Semantic Web: Research and Applications, • volume 6088 of LNCS, pages 425{439. 2010. • [9] S. P. Ponzetto and M. Strube. Deriving a Large-Scale • Taxonomy from Wikipedia. In AAAI, 2007. • [10] F. M. Suchanek, G. Kasneci, and G. Weikum. YAGO: • A Core of Semantic Knowledge. In WWW, 2007. • [11] O. Udrea, D. Reforgiato, and V. Subrahmanian. • Annotated RDF. ACM Tr. Comp. Log., 11(2), 2010.

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