knowledge graph analysis - software engineering...dr. hamed shariat yazdi, prof. jens lehmann...

Knowledge Graph Analysis

2018-10-01 Dr. Hamed Shariat Yazdi, Prof. Jens Lehmann

Introduction of Lecturers

Dr. Hamed Shariat Yazdi, Prof. Jens Lehmann Knowledge Graph Analysis 2

Prof. Dr. Jens Lehmann

B Since 2015:Full Professor for Data Engineering at University of Bonn

B 2013-2015:Leader of AKSW Research Group at University of Leipzig

B Since 2010:Leader of Machine Learning and Ontology Engineering Groupin Leipzig

B 2010:PhD University of Leipzig

B (Co-)Founder of DBpedia, DL-Learner and LinkedGeoDataProjects

B Research interests: Machine Learning, Semantic Web, Logic


Dr. Hamed Shariat Yazdi

B 2016 - Now:Leader of the Knowledge Graphs and Artificial IntelligenceTeam and Senior Researcher at University of Bonn

B 2016:Prize of the University of Siegen for the Promotion of YoungScientists

B 2015:PhD of Computer Science at University of Siegen(Grade: Summa Cum Laude mit Auszeichnung)

B Research interests:Machine Learning, Knowledge Graphs, Artificial Intelligence


Organisational Matters


Organisational Matters: Overview

B Knowledge Graph Analysis consists of two modules:Lecture/Exercises + Seminar

B Mailing list: sign-up sheet

B Lecture notes will be provided athttps://sewiki.iai.uni-bonn.de/teaching/lectures/kga/2018/start

B Changes will be announced at the website and via mailing list


https://sewiki.iai.uni-bonn.de/teaching/lectures/kga/2018/start

Organisational Matters: Exercises and Seminar

Exercises

B Start date: Will be announced soon!

B Time: Wednesdays 14:00-16:00, Thursdays 12:00-14:00 & 14:00-16:00 & 16:00-18:00,Fridays 10:00-12:00 & 12:00-14:00

B Location: Seminar Room 1.047, Informatik III

B Address: Endenicher Allee 19a, 53115 Bonn

Seminar

B Optional in addition to lecture and exercises

B Deepens understanding of topics discussed in the lecture

B Based on recently published articles

B 5 or 6 slots: initial distribution of topics, sprint presentations, final presentations

B Start date: 9 October 2018

B Time: Tuesdays 10:00-12:00

B Location: Seminar Room 1.047, Informatik III

B Address: Endenicher Allee 19a, 53115 Bonn


Organisational Matters: Exams

B Written exam (90 minutes)

B Date: Not Fixed Yet!


Organisational Matters: Overview

Tuesday 12:00 - 14:00B October 09: Motivation (Prof. Jens Lehmann, Dr. Hamed Shariat Yazdi)

B October 16: RDF Databases

B October 23: Property Graph Databases

B October 30: Statistical Relational Learning (SRL) for KGs

B November 06: Tensor-Factorization Methods

B November 13: - No Lecture -

B November 20: Alternating Least Squares and Stochastic Gradient Descent

B November 27: Introducing Neural Networks

B December 04: Neural Networks for KG analysis

B December 11: Latent Distance and Graph Feature Models

B January 15: Training SRL Models on KG

B January 22: Markov Logic Networks

B January 29: Summary


Organisational Matters: Literature

B A Review of Relational Machine Learning for Knowledge Graphs byMaximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovichhttps://arxiv.org/pdf/1503.00759.pdf

B O’Reilly’s Graph Databases by Ian Robinson, Jim Webber and Emil Eifremhttps://neo4j.com/book-graph-databases/


Background


Role of Knowledge

Douglas B. Lenat

B “Knowledge is Power” Hypothesis (the KnowledgePrinciple): “If a program is to perform a complextask well, it must know a great deal about theworld in which it operates.”

B The Breadth Hypothesis: “To behave intelligently inunexpected situations, an agent must be capable offalling back on increasingly general knowledge.”1

Edward A. Feigenbaum

1Taken from: Lenat & Feigenbaum, Artificial Intelligence 47 (1991), “On the Threshold of Knowledge”.


Information vs. Knowledge

B Information is a precursor for knowledge: necessary, but not sufficient

B Knowledge requires cognitive and analytical processing; information does not.

B Knowledge = information processed and organized so as to be useful, e.g., amap of immediate environment

⇒ information alone is not enough!


Why Graphs?

B Data is increasingly connected (graph data)

B Relationships within data are often themselves an integral part of the data

B The world is structured: we are surrounded by entities connected byrelations

B Graphs are a natural abstraction that captures the relationships betweenentities

B Graphs are well-understood mathematical objects


Why Graphs?

A picture is worth a thousand words...

B 5th Ave intersects E 42nd St

B 5th Ave intersects E 41st St

B Madison Ave intersects E 39th St

B Lexington Ave intersects E 38th St

B Tunnel Exit St intersects E 38th St

B Park Ave Tunnel intersects E 39th St

B etc...

Goal: Get from point A to point B.Which description would you use?


Why Graphs?

A picture is worth a thousand words...

B It is natural for human beings to representinformation in graphs

B Information represented in a graph is alsoeasier to understand and process (for hu-mans)

B Example: we can think of a map as a graphstructure:

• Nodes = intersections• Edges = roads connecting the inter-

sections

Goal: Get from point A to point B.Which description would you use?


Knowledge Graph: Intuition

B Models entities and relationships between these entities

B Data stored in a graph

B Nodes correspond to entities

B Directed labeled edges between entities keep track of relationships


Knowledge Graph: Intuition


Knowledge Graph: Things, not Strings


Connections to Related Fields

Draws on a multitude of fields:

B Information retrieval

B Natural language processing

B Databases

B Machine learning

B Artificial intelligence


History

2000s have seen a rapid rise of knowledge graphs to prominence


2007: DBpedia

2007: DBpedia is released by collaborated effort of Leipzig University, Universityof Mannheim, and OpenLink Software

B Structured information is mined from Wikipedia

B Allows semantic queries

B Collected information, as well as code, are freely available


2007: Freebase

2007: A California-based startup Metaweb releases Freebase

B Described by Metaweb as “an open shared database of the world’sknowledge”

B Provided interface to enable users to fill in structured data, to categorize orto connect data

B No programming experience required

B Structured data automatically harvested from the web


2008: YAGO

2008: Max-Planck-Institute in Saarbrucken releases YAGO

B Stands for: Yet Another Great Ontology

B Extracts information automatically from Wikipedia, WordNet, andGeoNames

B Was used by the artificial intelligence system Watson (together withDBpedia and Freebase)


2010: NELL

2010: Carnegie Mellon proposes a design for a language learning system NELL

B Stands for Never-Ending Language Learner

B Builds a knowledge base by reading the web

B Continuously improves its ability to process information and to build theknowledge base


2012: Google

2012: Google announces addition of knowledge graph to their search engine

B Goal: make search easier for the user

B Step toward enabling search engines to understand the world similarly to howhumans understand it

B Facilitates a broader and deeper search


2012: Satori

2012: Microsoft introduces Satori into their Bing search engine

B Goal: improve search by making it the underlying architecture moreintelligent

B Provide most relevant information and key facts automatically

B In 2010 Microsoft releases Graph Engine 1.0: a general purpose distributedgraph system over a memory cloud. The goal of the engine is to enableprocessing of large graphs.


Lastest Additions

2013: Facebook introduces “Entity Graph”

B Each “entity” (e.g. persons, institutions, restaurants, etc.) is treated as annode in a graph

B Relations between entities are modeled by their relationships in real-life (e.g.friendships, alumni, checked in at, etc)

2016: LinkedIn introduces their own knowledge graph

B A large knowledge base based on LinkedIn members, jobs, titles, companies,skills, etc.

B Goal: Create a large economic graph - a map of the world economy


In Summary, Knowledge Graphs...

B are applied both in academia and in-dustry

B are rapidly adopted by the most pop-ular search engines and social net-works

B present a natural way of accessing/s-toring knowledge in a multitude ofdomains

B promote better interface and accessto knowledge: dialog system, per-sonalization, emotion, question an-swering

2

2This Wikipedia and Wikimedia Commons image is from the user Chris 73 and is freelyavailable at //commons.wikimedia.org/wiki/File:WorldWideWebAroundGoogle.png under thecreative commons cc-by-sa 3.0 license.


Detailed Examples


Detailed Example

Things, not Strings!https://www.youtube.com/watch?v=mmQl6VGvX-c


https://www.youtube.com/watch?v=mmQl6VGvX-c

Structured Results

Augmentingpresentationwith relevantfacts


Google’s Knowledge Graph: Surfacing Facts Proactively


Google’s Knowledge Graph: Exploratory Search


Detailed Example: DBpedia

dbpedia.org

github.com/dbpedia/

B Crowd-sourced community effort toextract structured information fromWikipedia and Wikidata

B Currently describes 4.58 millionthings - 4.22 million are classified ina consistent ontology

B Available in 125 languages

B Enriches the extracted informationwith semantic layer

B Allows semantic query of relation-ships and properties + many othertools


DBpedia

B Community effort to extract structured information from Wikipedia and tomake this information available on the Web

B Allows to ask sophisticated queries against Wikipedia, and to link other datasets on the Web to Wikipedia data

B Semi-structured Wiki markup → structured information


Wikipedia Limitations

Simple Questions are hard to answer with Wikipedia:

B What do Innsbruck and Leipzig have in common?

B Who are mayors of central European towns elevated more than 1000m?

B Which movies are starring both Brad Pitt and Angelina Jolie?

B All soccer players, who played as goalkeeper for a club that has a stadiumwith more than 40.000 seats and who are born in a country with more than10 million inhabitants


Structure in Wikipedia

B Title

B Abstract

B Infoboxes

B Geo-coordinates

B Categories

B Images

B Links

• other language versions• other Wikipedia pages• To the Web• Redirects• Disambiguation

B ...


DBpedia Information Extraction Framework

DBpedia Information Extraction Framework (DIEF)

B Started in 2007

B Hosted on Sourceforge and Github

B Initially written in PHP but fully re-written Written in Scala and Java

B Around 40 Contributors

B See https://www.ohloh.net/p/dbpedia for detailed overview

Can potentially be adapted to other MediaWikis

B Currently Wiktionary http://wiktionary.dbpedia.org


https://www.ohloh.net/p/dbpedia

http://wiktionary.dbpedia.org

DIEF - Overview


DIEF - Raw Infobox Extractor

WikiText syntax{{Infobox Korean settlement|title = Busan Metropolitan City...|area km2 = 763.46|pop = 3635389|region = [[Yeongnam]]}}RDF serializationdbp:Busan dbp:title ”Busan Metropolitan City”dbp:Busan dbp:area km2 ”763.46”ˆxsd:floatdbp:Busan dbp:pop ”3635389”ˆxsd:int

dbp:Busan dbp:region dbp:Yeongnam


DIEF - Raw Infobox Extractor/Diversity


DIEF - Mapping-Based Infobox Extractor

Cleaner data:

B Combine what belongs together (birth place, birthplace)

B Separate what is different (bornIn, birthplace)

B Correct handling of datatypes

Mappings Wiki:

B http://mappings.dbpedia.org

B Everybody can contribute to new mappings or improve existing ones

B ≈ 170 editors


http://mappings.dbpedia.org

DIEF - Mapping-Based Infobox Extractor


Linked Data Publication via 303 Redirects

B http://dbpedia.org/resource/Dresden - URI of the city of Dresden

B http://dbpedia.org/page/Dresden - information resource describing thecity of Dresden in HTML format

B http://dbpedia.org/data/Dresden - information resource describing thecity of Dresden in RDF/XML format

B further formats supported, e.g. http://dbpedia.org/data/Dresden.n3for N3


http://dbpedia.org/resource/Dresden

http://dbpedia.org/page/Dresden

http://dbpedia.org/data/Dresden

http://dbpedia.org/data/Dresden.n3

DBpedia Links

Data set Predicate Count ToolAmsterdam Museum owl:sameAs 627 SBBC Wildlife Finder owl:sameAs 444 SBook Mashup rdf:type 9 100

owl:sameAsBricklink dc:publisher 10 100CORDIS owl:sameAs 314 SDailymed owl:sameAs 894 SDBLP Bibliography owl:sameAs 196 SDBTune owl:sameAs 838 SDiseasome owl:sameAs 2 300 SDrugbank owl:sameAs 4 800 SEUNIS owl:sameAs 3 100 SEurostat (Linked Stats) owl:sameAs 253 SEurostat (WBSG) owl:sameAs 137CIA World Factbook owl:sameAs 545 S


DBpedia Links

Data set Predicate Count Toolflickr wrappr dbp:hasPhoto- 3 800 000 C

CollectionFreebase owl:sameAs 3 600 000 CGADM owl:sameAs 1 900GeoNames owl:sameAs 86 500 SGeoSpecies owl:sameAs 16 000 SGHO owl:sameAs 196 LProject Gutenberg owl:sameAs 2 500 SItalian Public Schools owl:sameAs 5 800 SLinkedGeoData owl:sameAs 103 600 SLinkedMDB owl:sameAs 13 800 SMusicBrainz owl:sameAs 23 000New York Times owl:sameAs 9 700OpenCyc owl:sameAs 27 100 COpenEI (Open Energy) owl:sameAs 678 S


DBpedia Links

Data set Predicate Count ToolRevyu owl:sameAs 6Sider owl:sameAs 2 000 STCMGeneDIT owl:sameAs 904UMBEL rdf:type 896 400US Census owl:sameAs 12 600WikiCompany owl:sameAs 8 300WordNet dbp:wordnet type 467 100YAGO2 rdf:type 18 100 000Sum 27 211 732

Abbreviations:S: Silk (A Link Discovery Framework), L: LIMES (A Link Discovery Framework),

C: Custom Script, Missing: no regeneration


DBpedia Links - A Query Example

Getting the list of all musicians who were born before 1900 in Germany orderedby their birth date.

SELECT ?name ?birth ?death ?person ?countryOfBirth

WHERE {

?person rdf:type dbo:MusicalArtist .

?person foaf:name ?name .

?person dbo:birthPlace/dbo:country* ?countryOfBirth .

?person dbo:birthDate ?birth .

?person dbo:deathDate ?death .

FILTER ( ?birth < "1900 -01 -01"^^xsd:date ) .

FILTER ( ?countryOfBirth = :Germany ) .

}

ORDER BY ?birth


Infrastructure

DBpedia has two extraction modes:

B Wikipedia-database-dump-based extraction

B DBpedia Live synchronisation (more later)

DBpedia Dumps:

B The DBpedia Dump archive is located in:http://downloads.dbpedia.org/

B Latest downloads is described in: http://dbpedia.org/Downloads

Official Endpoint (by OpenLink): http://dbpedia.org/sparql


http://downloads.dbpedia.org/

http://dbpedia.org/Downloads

http://dbpedia.org/sparql

Query Answering

Back to our Wikipedia questions:

B What do Innsbruck and Leipzig have in common?

B Who are mayors of central European towns elevated more than 1000m?

B Which movies are starring both Brad Pitt and Angelina Jolie?

B All soccer players, who played as goalkeeper for a club that has a stadiumwith more than 40.000 seats and who are born in a country with more than10 million inhabitants

Using the data extracted from Wikipedia and the public SPARQL endpointDBpedia can answer these questions.


DBpedia Live

B DBpedia dumps are generated on a bi-annual basis

B Wikipedia has around 100,000-150,000 page edits per day

B DBpedia Live pulls page updates in real-time and extraction results updatethe triple store

B In practice, a 5 minute update delay increases performance by 15%

Links

B SPARQL Endpoint: http://live.dbpedia.org/sparql

B Documentation: http://wiki.dbpedia.org/DBpediaLive

B Statistics: http://live.dbpedia.org/LiveStats/


http://live.dbpedia.org/sparql

http://wiki.dbpedia.org/DBpediaLive

http://live.dbpedia.org/LiveStats/

DBpedia Live - Overview


DBpedia Internationalization (I18n)

B DBpedia Internationalization Committee founded:

• http://wiki.dbpedia.org/Internationalization

B Available DBpedia language editions in:

• Korean, Greek, German, Polish, Russian, Dutch, Portuguese, Spanish,Italian, Japanese, French

• Use the corresponding Wikipedia language edition for input

B Mappings available for 23 languages


http://wiki.dbpedia.org/Internationalization

DBpedia I18n - Overview


Applications: Disambiguation

Named entity recognition and disambiguation Tools such as: DBpediaSpotlight, AlchemyAPI, Semantic API, Open Calais, Zemanta and ApacheStanbol


Applications: Question Answering

3 elements of a successful question answering system:

B Questions are asked in natural language

B Allows people to use their own terminology

B Provides a concise answer

Example

B Which books did Dan Brown write?

B Which books have Dan Brown as their author?

B Which novels did Dan Brown write?


Applications: Question Answering

B DBpedia is the primary target for several QA systems in the QuestionAnswering over Linked Data (QALD) workshop series

B IBM Watson relied also on DBpedia


Applications: Faceted Browsing

B Faceted browsing: allows finding items byrestricting the overall set of items alongmultiple criteria (facets)

B It is a common feature on websites thatdeal with structured data e.g. e-commercesites

B Some projects:

• Neofonie Browser• gFacet• OpenLink faceted browser (fct)


Applications: Search and Querying

B Sparklis: A query builder in naturallanguage that allows people to explore andquery SPARQL endpoints with all thepower of SPARQL and without anyknowledge of SPARQL.

B ExConQuer: Works on top of DBpediaand enables users to construct a SPARQLquery without requiring any knowledge ofSPARQL or the datasets’ underlyingschema.Users are then able to download the datathey require in a number of differentformats.


Applications: Digital Libraries & Archives

B Virtual International Authority Files (VIAF) project as Linked Data

• VIAF added a total of 250,000 reciprocal authority links to Wikipedia.

B DBpedia can also provide:

• Context information for bibliographic and archive records (e.g. anauthor’s demographics, a film’s homepage, an image etc.)

• Stable and curated identifiers for linking.• The broad range of Wikipedia topics can form the basis for a thesaurus

for subject indexing.


Applications: DBpedia Mobile

B DBpedia Mobile is a location-centric DBpedia client application for mobiledevices consisting of a map view, the Marbles Linked Data Browser and aGPS-enabled launcher application.

B Its initial view is a browser-based area map that indicates the user’s positionand nearby DBpedia resources with appropriate labels and icons.


Other Applications

See http://wiki.dbpedia.org/projects for a more complete list


http://wiki.dbpedia.org/projects

Statistical Relational Learning


KG Completion: Link Prediction

B Problem: KGs are often automatically extracted from text and thus may beincomplete.


KG Completion: Link Prediction

B Problem: KGs are often automatically extracted from text and thus may beincomplete.

B Goal: use existing graph structure to predict missing edges/links.


KG Correction: Link correction

B Problem: KGs are often automatically extracted from text and thus may bewrong.


KG Correction: Link correction


B Goal: use existing graph structure to estimate probability of correctness ofan edge/link.


KG Correction: Entity resolution



KG Correction: Entity resolution


B Goal: use existing graph structure and other information to mergeduplicated entities.


Link-based clustering

B Goal: Group entities based on their similarity.

B In contrast to feature-based clustering, in link-based clustering entities arenot only grouped by the similarity of their features but also by the similarityof their links.

B E.g. community detection in social networks.

2This Wikipedia and Wikimedia Commons image is from the user Daniel Tenerife and isfreely available at //commons.wikimedia.org/wiki/File:Social Red.jpg under the creativecommons license.


Related problems

B You will learn to solve the problems we just saw in this lecture.

B They also play a role for:

• merging of KGs• similarity estimation of entities• suggestion of friends in social networks• ...


Machine Learning on KGs

Methods that play a role for statistical relational learning techniques covered inthis class :

B tensor factorization

B neural networks

B probabilistic approaches

B optimization techniques (like stochastic gradient descent)

B ...


Further Research Problems

...which can not be addressed by methods learned in the lecture:

B Growth: knowledge graphs are incomplete!

• Ontology matching: connect graphs• Knowledge extraction: extract new entities and relations from web/text

B Validation: knowledge graphs are not always correct!

• Entity resolution: split wrongly merged entities• Error detection: remove false assertions• Fact validation: find evidence for / against facts being true

B Interface: how to make it easier to access knowledge?

• Semantic parsing: interpret the meaning of queries• Question answering: compute answers using the knowledge graph

B Intelligence: can AI emerge from knowledge graphs?

• Automatic reasoning and planning• Generalization and abstraction• Clustering and community detection


Opportunities in the Smart Data Analyics Group

B Master thesis on most topics discussed in this lecture

B Machine Learning on knowledge graphs (and in general)

B Semantic technologies

B Spatiotemporal prediction

B Question answering on knowledge graphs

B We are looking for students and offering PhD opportunities!


SDA Project: SANSA

B SANSA will be a suite of APIs fordistributed reading, querying, infer-encing and analyzing of RDF knowl-edge graphs

SANSA (Semantic Analytics Stack)


Summary

B KGs facilitate mapping and understanding of the world in a way that issimilar to how humans understand it

B KGs utilize not just information, but knowledge, and thus are an integralpart toward knowledge-powered assistance⇒ a better, more interactive user interface⇒ understanding of user queries instead of simple string-matching

B KGs naturally allow for efficient pattern matching: numerous applicationsfrom fraud detection to spam filters to computational molecular biology


The End

Dr. Hamed Shariat [email protected]

University of Bonn

Prof. Dr. Jens Lehmannhttp://jens-lehmann.org

[email protected]

[email protected]

University of BonnFraunhofer IAIS

Thanks for your attention!


[email protected]

http://jens-lehmann.org

[email protected]

[email protected]

References I

S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z. Ives.

Dbpedia: A nucleus for a web of open data.

In The semantic web, pages 722–735. Springer, 2007.

A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E. R. Hruschka Jr, and T. M.Mitchell.

Toward an architecture for never-ending language learning.

In AAAI, volume 5, page 3, 2010.

V. I. Eric Sun.

Under the hood: The entities graph.

https://www.facebook.com/notes/facebook-engineering/

under-the-hood-the-entities-graph/10151490531588920/.

Q. He.

Building the linkedin knowledge graph.

https://en.wikipedia.org/wiki/Knowledge_Graph.


https://www.facebook.com/notes/facebook-engineering/under-the-hood-the-entities-graph/10151490531588920/

https://www.facebook.com/notes/facebook-engineering/under-the-hood-the-entities-graph/10151490531588920/

https://en.wikipedia.org/wiki/Knowledge_Graph

References II

M. Nickel, K. Murphy, V. Tresp, and E. Gabrilovich.

A review of relational machine learning for knowledge graphs.

arXiv preprint arXiv:1503.00759, 2015.

D. R. Qian.

Understand your world with bing.

http:

//blogs.bing.com/search/2013/03/21/understand-your-world-with-bing/.

I. Robinson, J. Webber, and E. Eifrem.

Graph Databases: New Opportunities for Connected Data.

” O’Reilly Media, Inc.”, 2015.

A. Singhal.

Introducing the knowledge graph: things, not strings.

https://googleblog.blogspot.de/2012/05/

introducing-knowledge-graph-things-not.html.


http://blogs.bing.com/search/2013/03/21/understand-your-world-with-bing/

http://blogs.bing.com/search/2013/03/21/understand-your-world-with-bing/

https://googleblog.blogspot.de/2012/05/introducing-knowledge-graph-things-not.html

https://googleblog.blogspot.de/2012/05/introducing-knowledge-graph-things-not.html

knowledge graph analysis - software engineering...dr. hamed shariat yazdi, prof. jens lehmann...

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