A Hitchhiker s Guide to Ontology
|
|
- Warren Mitchell
- 5 years ago
- Views:
Transcription
1 A Hitchhiker s Guide to Ontology Fabian M. Suchanek Télécom ParisTech University, Paris, France
2 2 Fabian M. Suchanek money freedom permanent
3 3 Fabian M. Suchanek 2003: BSc in Cognitive Science University of Osnabrück/DE 2005: MSc in Computer Science Saarland University/DE 2008: PhD in Computer Science Max Planck Institute/DE money freedom permanent
4 4 Fabian M. Suchanek 2003: BSc in Cognitive Science University of Osnabrück/DE 2005: MSc in Computer Science Saarland University/DE 2008: PhD in Computer Science Max Planck Institute/DE money freedom permanent
5 5 Fabian M. Suchanek 2009: PostDoc at Microsoft Research Silicon Valley/US money freedom permanent
6 6 Fabian M. Suchanek 2009: PostDoc at Microsoft Research Silicon Valley/US money freedom permanent
7 7 Fabian M. Suchanek 2009: PostDoc at Microsoft Research Silicon Valley/US 2010: PostDoc INRIA Saclay/FR money freedom permanent
8 8 Fabian M. Suchanek 2009: PostDoc at Microsoft Research Silicon Valley/US 2010: PostDoc INRIA Saclay/FR money freedom permanent
9 9 Fabian M. Suchanek 2009: PostDoc at Microsoft Research Silicon Valley/US 2010: PostDoc INRIA Saclay/FR 2012: Research group leader Max Planck Institute/DE money freedom permanent
10 10 Fabian M. Suchanek 2009: PostDoc at Microsoft Research Silicon Valley/US 2010: PostDoc INRIA Saclay/FR 2012: Research group leader Max Planck Institute/DE money freedom permanent
11 11 Fabian M. Suchanek 2009: PostDoc at Microsoft Research Silicon Valley/US 2010: PostDoc INRIA Saclay/FR 2012: Research group leader Max Planck Institute/DE 2013: Maître de Conférences Télécom ParisTech/FR money freedom permanent
12 12 Fabian M. Suchanek 2009: PostDoc at Microsoft Research Silicon Valley/US 2010: PostDoc INRIA Saclay/FR 2012: Research group leader Max Planck Institute/DE 2013: Maître de Conférences Télécom ParisTech/FR money freedom permanent
13 13 Fabian M. Suchanek I am an Elvis fan! Elvis, when I need you, I can hear you! Will there ever be someone like him?
14 14 Searching with Google Another singer called Elvis, young
15 15 Searching with Google Another singer called Elvis, young Carol Connors talks about her first boyfriend Carol Connors talks about her first boyfriend - a young singer called Elvis Presley.
16 16 Searching with Google Another singer called Elvis, young Younger Elvis, singer
17 17 Rephrasing does not help Another singer called Elvis, young Younger Elvis, singer Google, you don t understand! I want
18 18 We need structured knowledge Singer type type born 1935? born? To answer the question, the computer would need structured knowledge: an ontology
19 19 I work on Ontologies Singer type type born? born?
20 20 I work on Ontologies Constructing ontologies Singer type type born? born?
21 I work on Ontologies Constructing ontologies Singer type type born? born? Mining ontologies A B C 21
22 I work on Ontologies Constructing ontologies Singer type type born? born? Mining ontologies A B C Aligning ontologies 22
23 I work on Ontologies Constructing ontologies Singer Protecting ontologies type type born? born? Mining ontologies A B C Aligning ontologies 23
24 I work on Ontologies Constructing ontologies Singer Applying ontologies Protecting ontologies type type born? born? Mining ontologies A B C Aligning ontologies 24
25 25 Extracting from Wikipedia Elvis Presley Elvis Presley was one of the best blah blah blub blah don t read this, listen to the speaker! blah blah blah blubl blah you are still reading this! blah blah blah blah blabbel blah Born: 1935 In: Tupelo... Categories: Rock&Roll, American Singers, Academy Award winners...
26 26 Extracting from Wikipedia Elvis Presley Elvis Presley was one of the best blah blah blub blah don t read this, listen to the speaker! blah blah blah blubl blah you are still reading this! blah blah blah blah blabbel blah Born: 1935 In: Tupelo... ElvisPresley Categories: Rock&Roll, American Singers, Academy Award winners...
27 27 Extracting from Wikipedia Elvis Presley Elvis Presley was one of the best blah blah blub blah don t read this, listen to the speaker! blah blah blah blubl blah you are still reading this! blah blah blah blah blabbel blah Categories: Rock&Roll, American Singers, Academy Award winners... Born: 1935 In: Tupelo... born 1935 ElvisPresley bornin Tupelo
28 28 Extracting from Wikipedia Elvis Presley Elvis Presley was one of the best blah blah blub blah don t read this, listen to the speaker! blah blah blah blubl blah you are still reading this! blah blah blah blah blabbel blah Categories: Rock&Roll, American Singers, Academy Award winners... Born: 1935 In: Tupelo... AmericanSinger type ElvisPresley born bornin 1935 Tupelo
29 29 Extracting from Wikipedia Elvis Presley Elvis Presley was one of the best blah blah blub blah don t read this, listen to the speaker! blah blah blah blubl blah you are still reading this! blah blah blah blah blabbel blah Categories: Rock&Roll, American Singers, Academy Award winners... Born: 1935 In: Tupelo... AmericanSinger type ElvisPresley born bornin 1935 Tupelo won AcAward
30 30 Extracting from Wikipedia Elvis Presley Elvis Presley was one of the best blah blah blub blah don t read this, listen to the speaker! blah blah blah blubl blah you are still reading this! blah blah blah blah blabbel blah Categories: Rock&Roll, American Singers, Academy Award winners... Born: 1935 In: Tupelo... AmericanSinger type ElvisPresley born bornin 1935 Tupelo won locatedin AcAward USA
31 31 Extracting from Wikipedia Elvis Presley Elvis Presley was one of the best blah blah blub blah don t read this, listen to the speaker! blah blah blah blubl blah you are still reading this! blah blah blah blah blabbel blah Categories: Rock&Roll, American Singers, Academy Award winners... Born: 1935 In: Tupelo... Person? subclass Singer subclass AmericanSinger type ElvisPresley born bornin 1935 Tupelo won locatedin AcAward USA
32 Adding WordNet Person subclass Singer Scientist Person subclass Singer AmericanSinger subclass type born ElvisPresley bornin WordNet a lexicon of the English language developed at Princeton 1935 Tupelo won locatedin AcAward USA 32
33 33 Adding Time and Space Person subclass Singer LasVegas 1967 AmericanSinger subclass type PriscillaPresley married born ElvisPresley bornin We added a temporal and spatial dimension to facts and entities Tupelo won locatedin AcAward USA
34 34 YAGO: a large knowledge base YAGO is a knowledge base that combines WordNet classes and Wikipedia instances has time and space has a manually evaluated accuracy of 95% born Person subclass Singer AmericanSinger type ElvisPresley subclass bornin 1935 Tupelo won locatedin AcAward USA
35 35 YAGO: a large knowledge base Extractor Extractor Extractor GeoNames WordNet Extractor Extractor Extractor Extractor Try it online Try locally [WWW 2007, JWS 2008, WWW 2011 demo, AIJ 2013, WWW 2013 demo]
36 36 Example: YAGO about Elvis Try it online
37 37 YAGO: a large knowledge base 100m facts 10m entities 95% accuracy 100 Web site visitors/day 1000 citations linked to Freebase and DBpedia
38 38 SOFIE extracts by Reasoning Elvis died in 528 died 1955 born 1935
39 39 SOFIE extracts by Reasoning Elvis died in 528 died 1955 born 1935 occurs( Elvis, died in,528)
40 40 SOFIE extracts by Reasoning Elvis died in 528 Einstein died in 1955 died born occurs( Elvis, died in,528) occurs( Einstein, died in,1955)
41 41 SOFIE extracts by Reasoning Elvis died in 528 Einstein died in 1955 died born occurs( Elvis, died in,528) occurs( Einstein, died in,1955) died(einstein,1955), born(elvis, 1935)
42 42 SOFIE extracts by Reasoning Elvis died in 528 Einstein died in 1955 died born occurs( Elvis, died in,528) occurs( Einstein, died in,1955) died(einstein,1955), born(elvis, 1935) occurs(x,p,y) & means(x,x) & R(X,Y) => pattern(p,r) occurs(x,p,y) & means(x,x) & pattern(p,r) => R(X,Y)
43 SOFIE extracts by Reasoning Elvis died in 528 Einstein died in 1955 died born occurs( Elvis, died in,528) occurs( Einstein, died in,1955) died(einstein,1955), born(elvis, 1935) occurs(x,p,y) & means(x,x) & R(X,Y) => pattern(p,r) occurs(x,p,y) & means(x,x) & pattern(p,r) => R(X,Y) born(x,y) & died(x,z) => Z>Y... 43
44 44 SOFIE extracts by Reasoning Elvis died in 528 Solving a Weighted MAX SAT problem at scale occurs( Elvis, died in,528) occurs( Einstein, died in,1955) died(einstein,1955), born(elvis, 1935) occurs(x,p,y) & means(x,x) & R(X,Y) => pattern(p,r) occurs(x,p,y) & means(x,x) & pattern(p,r) => R(X,Y) born(x,y) & died(x,z) => Z>Y...
45 45 SOFIE extracts by Reasoning Elvis died in 528 died 528 [WWW 2009] occurs( Elvis, died in,528) occurs( Einstein, died in,1955) died(einstein,1955), born(elvis, 1935) occurs(x,p,y) & means(x,x) & R(X,Y) => pattern(p,r) occurs(x,p,y) & means(x,x) & pattern(p,r) => R(X,Y) born(x,y) & died(x,z) => Z>Y...
46 Product extraction 46
47 Work on Ontologies Constructing ontologies Singer Applying ontologies Protecting ontologies type type born? born? Mining ontologies A B C Aligning ontologies 47
48 48 Rule Mining finds patterns married haschild haschild married(x, y) haschild(x, z) haschild(y, z)
49 Rule Mining finds patterns married haschild haschild married(x, y) haschild(x, z) haschild(y, z) But: Rule mining needs counter examples and RDF ontologies are positive only 49
50 50 Partial Completeness Assumption haschild Assumption: If we know r(x,y1),..., r(x,yn), then all other r(x,z) are false.
51 51 Partial Completeness Assumption haschild Assumption: If we know r(x,y1),..., r(x,yn), then all other r(x,z) are false.
52 52 Partial Completeness Assumption haschild Assumption: If we know r(x,y1),..., r(x,yn), then all other r(x,z) are false.
53 53 Partial Completeness Assumption haschild Assumption: If we know r(x,y1),..., r(x,yn), then all other r(x,z) are false. + an efficient implementation
54 54 AMIE finds rules in ontologies AMIE (5min) type(x, pope) diedin(x, Rome)
55 55 AMIE finds rules in ontologies AMIE (5min) type(x, pope) diedin(x, Rome) [WWW 2013]
56 Work on Ontologies Constructing ontologies Singer Applying ontologies Protecting ontologies type type born? born? Mining ontologies A B C Aligning ontologies 56
57 Ontologies are complementary plays type Singer RockSinger type married born YAGO birthdate 1935 ElvisPedia 57
58 No Links => No Use plays type Singer Wo is the spouse of the guitar player? RockSinger type married born birthdate 1935 YAGO ElvisPedia 58
59 Match classes, entities, & relations plays type Singer subclassof sameas RockSinger type married born subpropertyof birthdate 1935 YAGO ElvisPedia 59
60 Match classes, entities, & relations Elvis name Elvis label
61 Match classes, entities, & relations Elvis name Elvis label 1. Match literals
62 Match classes, entities, & relations Elvis name Elvis label 1. Match literals
63 Match classes, entities, & relations Elvis name Elvis label 2. Assume small equivalence of all relations
64 Match classes, entities, & relations Elvis name Elvis label 2. Assume small equivalence of all relations
65 Match classes, entities, & relations Elvis name Elvis label 3. If entities share a relation that is highly inverse functional, and object is matched, match them.
66 Match classes, entities, & relations Elvis name Elvis label 3. If entities share a relation that is highly inverse functional, and object is matched, match them.
67 Match classes, entities, & relations Elvis name Elvis label 4. If relations share many pairs, increase their match
68 Match classes, entities, & relations Elvis name Elvis label 4. If relations share many pairs, increase their match
69 Match classes, entities, & relations Elvis name Elvis label 5. Iterate P (e 1 e 2 ) = 1 42 α β... P (r 1 r 2 )... P (r 1 r 2 ) = 42φ... P (e 1 = e 2 )...
70 Match classes, entities, & relations singer type type AmericanSinger 6. Compute class subsumption P (c 1 c 2 ) = arcsin(4.1125) P (e 1 e 2 )..
71 Match classes, entities, & relations singer type type AmericanSinger 6. Compute class subsumption
72 PARIS:match entities,classes,relations PARIS matches DBpedia & YAGO in 2 hours with 90% accuracy [VLDB 2012] 72
73 73 Matching heterogeneous KBs bornincountry USA bornincity Tupelo locatedin USA
74 74 1. Coalesce the KBs bornincountry USA bornincity Tupelo locatedin
75 75 2. Mine rules bornincountry USA bornincity Tupelo locatedin AMIE bornincity(x, y) locatedin(y, z) bornincountry(x, z) ROSA rule
76 76 ROSA rules match ontologies [AKBC 2013] bornincity(x, y) locatedin(y, z) bornincountry(x, z) ROSA rule
77 Work on Ontologies Constructing ontologies Singer Applying ontologies Protecting ontologies type type born? born? Mining ontologies A B C Aligning ontologies 77
78 78 Plagiarism People may steal from other ontologies without giving due credit. Most ontologies have licenses that require attribution. cc stolen YAGO EvilPedia
79 Additive Watermarking By adding a few fake facts to the source ontology, one can prove theft in the target ontology. stolen YAGO EvilPedia [WWW 2012 demo] 79
80 Subtractive Watermarking One can also prove theft by selectively removing facts from the source ontology. stolen YAGO EvilPedia [ISWC 2011] 80
81 Work on Ontologies Constructing ontologies Singer Applying ontologies Protecting ontologies type type born? born? Mining ontologies A B C Aligning ontologies 81
82 82 Mining Le Monde
83 83 Mining Le Monde
84 84 Mining Le Monde
85 85 Mining Le Monde time place entity 1967 USA
86 86 Mining Le Monde Average age of people mentioned
87 Mining Le Monde [AKBC 2013] 87
88 Work on Ontologies Constructing ontologies Singer Applying ontologies Protecting ontologies type type born? born? Mining ontologies A B C Aligning ontologies 88
89 YAGO can answer our question Find x such that x is called Elvis x is a singer x was born after
90 YAGO can answer our question Find x such that x is called Elvis x is a singer x was born after 1970 Elvis Crespo, singer, born
91 Thank you for your attention! Applying ontologies Constructing ontologies Singer Protecting ontologies type type born? born? Aligning Mining ontologies ontologies A B C Slides done with Powerline, my free SVG slide editor with LaTex support 91
Advancing the utility of large knowledge bases
Advancing the utility of large knowledge bases Fabian M. Suchanek Habilitation defense at the Université Pierre et Marie Curie in Paris, France Contributions à l avancement des grandes bases de connaissances
More informationA Hitchhiker s guide to Ontology
A Hitchhiker s guide to Ontology Fabian M. Suchanek Télécom ParisTech University Paris, France >me >intro 2 Fabian M. Suchanek 2003: 2005: 2008: money freedom permanent 3 Fabian M. Suchanek 2003: BSc in
More informationYAGO: a Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames
1/38 YAGO: a Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames Fabian Suchanek 1, Johannes Hoffart 2, Joanna Biega 2, Thomas Rebele 1, Erdal Kuzey 2, Gerhard Weikum 2 1 Télécom ParisTech
More informationYAGO: a Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames
1/24 a from Wikipedia, Wordnet, and Geonames 1, Fabian Suchanek 1, Johannes Hoffart 2, Joanna Biega 2, Erdal Kuzey 2, Gerhard Weikum 2 Who uses 1 Télécom ParisTech 2 Max Planck Institute for Informatics
More informationMining Rules to Align Knowledge Bases
Mining Rules to Align Knowledge Bases Luis Galárraga 1, Nicoleta Preda 2, Fabian M. Suchanek 1 1 Max-Planck Institute for Informatics, Saarbrücken, Germany 2 University of Versailles, PRiSM-CNRS, Versailles,
More informationYago: A Large Ontology from Wikipedia and WordNet. Fabian M. Suchanek, Gjergji Kasneci, Gerhard Weikum
Yago: A Large Ontology from Wikipedia and WordNet Fabian M. Suchanek, Gjergji Kasneci, Gerhard Weikum MPI I 2007 5-003 December 2007 Authors Addresses Fabian M. Suchanek Max-Planck-Institute for Computer
More informationDB Project. Database Systems Spring 2013
DB Project Database Systems Spring 2013 1 Database project YAGO Yet Another Great Ontology 2 About YAGO * A huge semantic knowledge base knowledge base a special kind of DB for knowledge management (e.g.,
More informationYAGO: A Large Ontology from Wikipedia and WordNet
YAGO: A Large Ontology from Wikipedia and WordNet Fabian M. Suchanek, Gjergji Kasneci, Gerhard Weikum Max-Planck-Institute for Computer Science, Saarbruecken, Germany Abstract This article presents YAGO,
More informationInformation Extraction for Ontology Learning
Information Extraction for Ontology Learning Fabian SUCHANEK Max Planck Institute for Informatics, Germany Abstract. In this chapter, we discuss how ontologies can be constructed by extracting information
More informationרשת סמנטית, אונטולוגיות וכו שיעור 17
רשת סמנטית, אונטולוגיות וכו שיעור 17 מדעי הרוח הדיגיטליים למדעי המחשב אונ בן גוריון תזכורת: מקורות מידע רבים האחדה, שיתוף Some slides taken from: https://people.cs.kuleuven.be/~bettina.berendt/teaching/
More informationYAGO - Yet Another Great Ontology
YAGO - Yet Another Great Ontology YAGO: A Large Ontology from Wikipedia and WordNet 1 Presentation by: Besnik Fetahu UdS February 22, 2012 1 Fabian M.Suchanek, Gjergji Kasneci, Gerhard Weikum Presentation
More informationReasoning on Web Data Semantics
Reasoning on Web Data Semantics Oui. Peut-on préciser l'heure et le lieu? Merci Marie-Christine Rousset Université de Grenoble (UJF) et Institut Universitaire de France Amicalement Marie-Christine 1 Evolution
More informationReal-time population of Knowledge Bases: Opportunities and Challenges. Ndapa Nakashole Gerhard Weikum
Real-time population of Knowledge Bases: Opportunities and Challenges Ndapa Nakashole Gerhard Weikum AKBC Workshop at NAACL 2012 Real-time Data Sources In news and social media, the implicit query is:
More informationSemantic Interoperability. Being serious about the Semantic Web
Semantic Interoperability Jérôme Euzenat INRIA & LIG France Natasha Noy Stanford University USA 1 Being serious about the Semantic Web It is not one person s ontology It is not several people s common
More informationArgumentation-based Inconsistencies Detection for Question-Answering over DBpedia
Argumentation-based Inconsistencies Detection for Question-Answering over DBpedia Elena Cabrio, Julien Cojan, Serena Villata, and Fabien Gandon INRIA Sophia Antipolis, France {firstname.lastname}@inria.fr
More information15 רועיש linked data ו תויגולוטנוא םיילטיגידה חורה יעדמ ןוירוג ןב 1
שיעור 15 אונטולוגיות ו linked data מדעי הרוח הדיגיטליים בן גוריון 1 טקסונומיות ותזארוס Taxonomies and thesauri אוצר מילים מבוקר controlled vocabulary מתאר מושגים (והמילים הנרדפות להם) ויחסים ביניהם טקסונומיה
More informationSOFIE: A Self-Organizing Framework for Information Extraction. Fabian M. Suchanek, Mauro Sozio, Gerhard Weikum
SOFIE: A Self-Organizing Framework for Information Extraction Fabian M. Suchanek, Mauro Sozio, Gerhard Weikum MPI I 2008 5-004 November 2008 Authors Addresses Fabian M. Suchanek Max-Planck-Institute for
More informationRobust Discovery of Positive and Negative Rules in Knowledge-Bases
Robust Discovery of Positive and Negative Rules in Knowledge-Bases Paolo Papotti joint work with S. Ortona (Meltwater) and V. Meduri (ASU) http://www.eurecom.fr/en/publication/5321/detail/robust-discovery-of-positive-and-negative-rules-in-knowledge-bases
More informationYAGO: A Core of Semantic Knowledge Unifying WordNet and Wikipedia
YAGO: A Core of Semantic Knowledge Unifying WordNet and Wikipedia Fabian M. Suchanek Max-Planck-Institut Saarbrücken / Germany suchanek@mpii.mpg.de Gjergji Kasneci Max-Planck-Institut Saarbrücken / Germany
More informationEnriching an Academic Knowledge base using Linked Open Data
Enriching an Academic Knowledge base using Linked Open Data Chetana Gavankar 1,2 Ashish Kulkarni 1 Yuan Fang Li 3 Ganesh Ramakrishnan 1 (1) IIT Bombay, Mumbai, India (2) IITB-Monash Research Academy, Mumbai,
More informationJianyong Wang Department of Computer Science and Technology Tsinghua University
Jianyong Wang Department of Computer Science and Technology Tsinghua University jianyong@tsinghua.edu.cn Joint work with Wei Shen (Tsinghua), Ping Luo (HP), and Min Wang (HP) Outline Introduction to entity
More informationOntology Alignment at the Instance and Schema Level
INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE Ontology Alignment at the Instance and Schema Level Fabian M. Suchanek Serge Abiteboul Pierre Senellart N 0408 version 2 initial version
More informationDiscovering Semantic Relations from the Web and Organizing them with PATTY
Discovering Semantic Relations from the Web and Organizing them with PATTY Ndapandula Nakashole, Gerhard Weikum, Fabian Suchanek Max Planck Institute for Informatics, Saarbruecken, Germany {nnakasho,weikum,suchanek}@mpi-inf.mpg.de
More informationScalable Knowledge Harvesting with High Precision and High Recall. Ndapa Nakashole Martin Theobald Gerhard Weikum
Scalable Knowledge Harvesting with High Precision and High Recall Ndapa Nakashole Martin Theobald Gerhard Weikum Web Knowledge Harvesting Goal: To organise text into precise facts Alex Rodriguez A.Rodriguez
More informationWhat you have learned so far. Interoperability. Ontology heterogeneity. Being serious about the semantic web
What you have learned so far Interoperability Introduction to the Semantic Web Tutorial at ISWC 2010 Jérôme Euzenat Data can be expressed in RDF Linked through URIs Modelled with OWL ontologies & Retrieved
More informationarxiv: v1 [cs.ai] 19 Jul 2012
SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases Simon Lacoste-Julien INRIA - SIERRA project-team Ecole Normale Superieure Paris, France Konstantina Palla University of Cambridge Cambridge,
More informationNatasha Noy Stanford University USA
Semantic Interoperability Jérôme Euzenat INRIA & LIG France Natasha Noy Stanford University USA Semantic Interoperability Jérôme Euzenat INRIA & LIG France Natasha Noy Stanford University US Being serious
More informationCSE 701: LARGE-SCALE GRAPH MINING. A. Erdem Sariyuce
CSE 701: LARGE-SCALE GRAPH MINING A. Erdem Sariyuce WHO AM I? My name is Erdem Office: 323 Davis Hall Office hours: Wednesday 2-4 pm Research on graph (network) mining & management Practical algorithms
More informationNAGA: Searching and Ranking Knowledge. Gjergji Kasneci, Fabian M. Suchanek, Georgiana Ifrim, Maya Ramanath, and Gerhard Weikum
NAGA: Searching and Ranking Knowledge Gjergji Kasneci, Fabian M. Suchanek, Georgiana Ifrim, Maya Ramanath, and Gerhard Weikum MPI I 2007 5 001 March 2007 Authors Addresses Gjergji Kasneci Max-Planck-Institut
More informationDBpedia As A Formal Knowledge Base An Evaluation
DBpedia As A Formal Knowledge Base An Evaluation TOMASZ BOIŃSKI Gdańsk University of Technology Faculty of Electronics, Telecommunications and Informatics Narutowicza Street 11/12 80-233 Gdańsk POLAND
More informationSemantic Web Fundamentals
Semantic Web Fundamentals Web Technologies (706.704) 3SSt VU WS 2018/19 with acknowledgements to P. Höfler, V. Pammer, W. Kienreich ISDS, TU Graz January 7 th 2019 Overview What is Semantic Web? Technology
More informationWatermarking for Ontologies
Watermarking for Ontologies Fabian M. Suchanek 1, David Gross-Amblard 1,2, and Serge Abiteboul 1 1 INRIA Saclay, Paris 2 LE2I CNRS, Université de Bourgogne, Dijon Abstract. In this paper, we study watermarking
More informationThe YAGO-NAGA Approach to Knowledge Discovery
The YAGO-NAGA Approach to Knowledge Discovery Gjergji Kasneci, Maya Ramanath, Fabian Suchanek, Gerhard Weikum Max Planck Institute for Informatics D-66123 Saarbruecken, Germany kasneci,ramanath,suchanek,weikum@mpi-inf.mpg.de
More informationTriAD: A Distributed Shared-Nothing RDF Engine based on Asynchronous Message Passing
TriAD: A Distributed Shared-Nothing RDF Engine based on Asynchronous Message Passing Sairam Gurajada, Stephan Seufert, Iris Miliaraki, Martin Theobald Databases & Information Systems Group ADReM Research
More information01 INTRODUCTION TO SEMANTIC WEB
SEMANTIC WEB 01 INTRODUCTION TO SEMANTIC WEB FROM WEB 1.0 TO WEB 3.0 IMRAN IHSAN ASSISTANT PROFESSOR, AIR UNIVERSITY, ISLAMABAD WWW.IMRANIHSAN.COM QUESTIONS What is the Semantic Web? Why do we want it?
More informationA Korean Knowledge Extraction System for Enriching a KBox
A Korean Knowledge Extraction System for Enriching a KBox Sangha Nam, Eun-kyung Kim, Jiho Kim, Yoosung Jung, Kijong Han, Key-Sun Choi KAIST / The Republic of Korea {nam.sangha, kekeeo, hogajiho, wjd1004109,
More informationThe Picture of Dorian Gray
Comprehension Test for ISBN 978-0-19-479126-7 The Picture of Dorian Gray Oscar Wilde 1 Are these sentences true (T) or false (F)? a Dorian Gray was twenty years old at the beginning of the story. b He
More informationSESAME: European Statistics Explored via Semantic Alignment onto Wikipedia
SESAME: European Statistics Explored via Semantic Alignment onto Wikipedia Natalia Boldyrev 1 Marc Spaniol 2 Jannik Strötgen 1 Gerhard Weikum 1 1 Max Planck Institute for Informatics, Saarland Informatics
More informationOn Statistical Characteristics of Real-life Knowledge Graphs
On Statistical Characteristics of Real-life Knowledge Graphs Wenliang Cheng, Chengyu Wang, Bing Xiao, Weining Qian, Aoying Zhou Institute for Data Science and Engineering East China Normal University Shanghai,
More informationPersonalization of Search on Structured Data
Universität des Saarlandes, FR Informatik Max-Planck-Institut für Informatik, AG 5 U N S A R I V E R S A V I E I T A S N I S S Personalization of Search on Structured Data Masterarbeit im Fach Informatik
More informationLinking Entities in Short Texts Based on a Chinese Semantic Knowledge Base
Linking Entities in Short Texts Based on a Chinese Semantic Knowledge Base Yi Zeng, Dongsheng Wang, Tielin Zhang, Hao Wang, and Hongwei Hao Institute of Automation, Chinese Academy of Sciences, Beijing,
More informationDBpedia Extracting structured data from Wikipedia
DBpedia Extracting structured data from Wikipedia Anja Jentzsch, Freie Universität Berlin Köln. 24. November 2009 DBpedia DBpedia is a community effort to extract structured information from Wikipedia
More informationAligning and Integrating Data in Karma. Craig Knoblock University of Southern California
Aligning and Integrating Data in Karma Craig Knoblock University of Southern California Data Integration Approaches 3 Data Integration Approaches Data Warehousing 4 Data Integration Approaches Data Warehousing
More informationLinked Data in Archives
Linked Data in Archives Publish, Enrich, Refine, Reconcile, Relate Presented 2012-08-23 SAA 2012, Linking Data Across Libraries, Archives, and Museums Corey A Harper Semantic Web TBL s original vision
More informationDistant Supervision via Prototype-Based. global representation learning
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) Distant Supervision via Prototype-Based Global Representation Learning State Key Laboratory of Computer Science, Institute
More informationKNOWLEDGE GRAPHS. Lecture 1: Introduction and Motivation. TU Dresden, 16th Oct Markus Krötzsch Knowledge-Based Systems
KNOWLEDGE GRAPHS Lecture 1: Introduction and Motivation Markus Krötzsch Knowledge-Based Systems TU Dresden, 16th Oct 2018 Introduction and Organisation Markus Krötzsch, 16th Oct 2018 Knowledge Graphs slide
More informationWordNets and TEI-LEX. John P. McCrae, Thierry Declerck
WordNets and TEI-LEX John P. McCrae, Thierry Declerck Global WordNet Grid Princeton WordNet 3. 0 EuroWord Net BalkaNet Multi WordNet Indo WordNet Open WordNet PT Asian WordNet Open Multilingual WordNet
More informationLogics for Data and Knowledge Representation: midterm Exam 2013
1. [6 PT] Say (mark with an X) whether the following statements are true (T) or false (F). a) In a lightweight ontology there are is-a and part-of relations T F b) Semantic matching is a technique to compute
More informationOpen Digital Forms. Hiep Le, Thomas Rebele, Fabian Suchanek. HAL Id: hal
Open Digital Forms Hiep Le, Thomas Rebele, Fabian Suchanek To cite this version: Hiep Le, Thomas Rebele, Fabian Suchanek. Open Digital Forms. Research and Advanced Technology for Digital Libraries - 20th
More informationAROMA results for OAEI 2009
AROMA results for OAEI 2009 Jérôme David 1 Université Pierre-Mendès-France, Grenoble Laboratoire d Informatique de Grenoble INRIA Rhône-Alpes, Montbonnot Saint-Martin, France Jerome.David-at-inrialpes.fr
More informationShortest paths on large graphs: Systems, Algorithms, Applications
Shortest paths on large graphs: Systems, Algorithms, Applications Andrey Gubichev TU München January 2012 Andrey Gubichev Shortest paths on large graphs 1 / 53 Outline Introduction Systems Algorithms Applications
More informationTitle. Prolog, Rules, Reasoning and SPARQLing Magic in the real world. Franz Inc
Prolog, Rules, Reasoning and SPARQLing Magic in the real world 1 Contents How do we fit it all together: rules and prolog and reasoning and magic predicates and SPARQL Use case: BigBank Event view of the
More informationKnowledge Graphs: In Theory and Practice
Knowledge Graphs: In Theory and Practice Nitish Aggarwal, IBM Watson, USA, Sumit Bhatia, IBM Research, India Saeedeh Shekarpour, Knoesis Research Centre Ohio, USA Amit Sheth, Knoesis Research Centre Ohio,
More informationKnowledge Discovery over the Deep Web, Semantic Web and XML
Knowledge Discovery over the Deep Web, Semantic Web and XML Aparna Varde 1, Fabian Suchanek 2, Richi Nayak 3, and Pierre Senellart 4 1 Department of Computer Science, Montclair State University, Montclair,
More informationGeoTemporal Reasoning for the Social Semantic Web
GeoTemporal Reasoning for the Social Semantic Web Jans Aasman Franz Inc. 2201 Broadway, Suite 715, Oakland, CA 94612, USA ja@franz.com Abstract: We demonstrate a Semantic Web application that organizes
More informationHow Co-Occurrence can Complement Semantics?
How Co-Occurrence can Complement Semantics? Atanas Kiryakov & Borislav Popov ISWC 2006, Athens, GA Semantic Annotations: 2002 #2 Semantic Annotation: How and Why? Information extraction (text-mining) for
More informationDeriving Intensional Descriptions for Web Services
Deriving Intensional Descriptions for Web Services Maria Koutraki, Dan Vodislav, Nicoleta Preda To cite this version: Maria Koutraki, Dan Vodislav, Nicoleta Preda. Deriving Intensional Descriptions for
More informationKNOWLEDGE GRAPH CONSTRUCTION
KNOWLEDGE GRAPH CONSTRUCTION Jay Pujara Karlsruhe Institute of Technology 7/7/2015 Can Computers Create Knowledge? Internet Massive source of publicly available information Knowledge Computers + Knowledge
More information2. Knowledge Representation Applied Artificial Intelligence
2. Knowledge Representation Applied Artificial Intelligence Prof. Dr. Bernhard Humm Faculty of Computer Science Hochschule Darmstadt University of Applied Sciences 1 Retrospective Introduction to AI What
More informationBryan Smith May 2010
Bryan Smith May 2010 Tool (Onto2SMem) to generate declarative knowledge base in SMem from ontology Sound (if incomplete) inference Proof of concept Baseline implementation Semantic memory (SMem) Store
More informationInteractive Rule Mining in Knowledge Bases
Interactive Rule Mining in Knowledge Bases Luis Galárraga Télécom ParisTech Paris, France galarrag@enst.fr ABSTRACT L extraction de règles d association dans une base de connaissance consiste en la découverte
More informationQuery-driven Data Completeness Assessment
Query-driven Data Completeness Assessment Simon Razniewski Free University of Bozen-Bolzano, Italy Part I joint work with Flip Korn, Werner Nutt, Divesh Srivastava Background 2011-2014: PhD (on reasoning
More informationCS446: Machine Learning Fall Problem Set 4. Handed Out: October 17, 2013 Due: October 31 th, w T x i w
CS446: Machine Learning Fall 2013 Problem Set 4 Handed Out: October 17, 2013 Due: October 31 th, 2013 Feel free to talk to other members of the class in doing the homework. I am more concerned that you
More informationBuilding DBpedia Japanese and Linked Data Cloud in Japanese
Building DBpedia Japanese and Linked Data Cloud in Japanese Fumihiro Kato 1,2, Hideaki Takeda 1,3, Seiji Koide 1,2, and Ikki Ohmukai 1,3 1 National Institute of Informatics, 2-1-2, Chiyoda-ku, Tokyo, Japan
More informationSemantic Web Fundamentals
Semantic Web Fundamentals Web Technologies (706.704) 3SSt VU WS 2017/18 Vedran Sabol with acknowledgements to P. Höfler, V. Pammer, W. Kienreich ISDS, TU Graz December 11 th 2017 Overview What is Semantic
More informationFREQUENTLY ASKED QUESTIONS
FREQUENTLY ASKED QUESTIONS In order to better assist you with the transition to our new home banking service, we wanted to provide you with a list of anticipated questions and things that may need your
More informationOWL-CM : OWL Combining Matcher based on Belief Functions Theory
OWL-CM : OWL Combining Matcher based on Belief Functions Theory Boutheina Ben Yaghlane 1 and Najoua Laamari 2 1 LARODEC, Université de Tunis, IHEC Carthage Présidence 2016 Tunisia boutheina.yaghlane@ihec.rnu.tn
More informationEntity and Knowledge Base-oriented Information Retrieval
Entity and Knowledge Base-oriented Information Retrieval Presenter: Liuqing Li liuqing@vt.edu Digital Library Research Laboratory Virginia Polytechnic Institute and State University Blacksburg, VA 24061
More informationEntity Linking in Web Tables with Multiple Linked Knowledge Bases
Entity Linking in Web Tables with Multiple Linked Knowledge Bases Tianxing Wu, Shengjia Yan, Zhixin Piao, Liang Xu, Ruiming Wang, Guilin Qi School of Computer Science and Engineering, Southeast University,
More informationDBPedia (dbpedia.org)
Matt Harbers Databases and the Web April 22 nd, 2011 DBPedia (dbpedia.org) What is it? DBpedia is a community whose goal is to provide a web based open source data set of RDF triples based on Wikipedia
More informationEnhanced retrieval using semantic technologies:
Enhanced retrieval using semantic technologies: Ontology based retrieval as a new search paradigm? - Considerations based on new projects at the Bavarian State Library Dr. Berthold Gillitzer 28. Mai 2008
More informationA Survey on Information Extraction in Web Searches Using Web Services
A Survey on Information Extraction in Web Searches Using Web Services Maind Neelam R., Sunita Nandgave Department of Computer Engineering, G.H.Raisoni College of Engineering and Management, wagholi, India
More informationDoctorat ParisTech. TELECOM ParisTech. Rule Mining in Knowledge Bases
2013-ENST-0050 EDITE - ED 130 Doctorat ParisTech T H È S E pour obtenir le grade de docteur délivré par TELECOM ParisTech Spécialité «Informatique» présentée et soutenue publiquement par Luis Galárraga
More informationJason Mraz - We Sing. We Dance. We Steal Things. By Jason Mraz
Jason Mraz - We Sing. We Dance. We Steal Things. By Jason Mraz If looking for the book by Jason Mraz Jason Mraz - We Sing. We Dance. We Steal Things. in pdf format, then you've come to the loyal website.
More informationNumber Sense Workbook 7, Part 1: Unit 3
Number Sense Workbook, Part : Unit Worksheet NS- page. a),, : 0,,,,, 0, : 0,, 0,, 0,, 0. a) Yes No Yes; Yes; Yes. a) 0 All whole numbers, since any whole number times zero is zero.. is a factor of. is
More informationMapping WordNet Instances to Wikipedia
Mapping WordNet Instances to Wikipedia John P. McCrae Insight Centre for Data Analytics, National University of Ireland Galway Lexical vs. Encyclopedic Yellow (in a dictionary) Is a verb, noun and adjective
More informationIntuitive and Interactive Query Formulation to Improve the Usability of Query Systems for Heterogeneous Graphs
Intuitive and Interactive Query Formulation to Improve the Usability of Query Systems for Heterogeneous Graphs Nandish Jayaram University of Texas at Arlington PhD Advisors: Dr. Chengkai Li, Dr. Ramez
More informationDatabases & Information Retrieval
Databases & Information Retrieval Maya Ramanath (Further Reading: Combining Database and Information-Retrieval Techniques for Knowledge Discovery. G. Weikum, G. Kasneci, M. Ramanath and F.M. Suchanek,
More informationLinked Data and RDF. COMP60421 Sean Bechhofer
Linked Data and RDF COMP60421 Sean Bechhofer sean.bechhofer@manchester.ac.uk Building a Semantic Web Annotation Associating metadata with resources Integration Integrating information sources Inference
More informationLecture 1: Introduction and Motivation Markus Kr otzsch Knowledge-Based Systems
KNOWLEDGE GRAPHS Introduction and Organisation Lecture 1: Introduction and Motivation Markus Kro tzsch Knowledge-Based Systems TU Dresden, 16th Oct 2018 Markus Krötzsch, 16th Oct 2018 Course Tutors Knowledge
More informationEn##es, Graphs, and Crowdsourcing for be7er Web Search
En##es, Graphs, and Crowdsourcing for be7er Web Search Gianluca Demar#ni exascale Infolab University of Fribourg, Switzerland gianlucademar#ni.net exascale.info Gianluca Demar#ni M.Sc. at University of
More informationQAKiS: an Open Domain QA System based on Relational Patterns
QAKiS: an Open Domain QA System based on Relational Patterns Elena Cabrio, Julien Cojan, Alessio Palmero Aprosio, Bernardo Magnini, Alberto Lavelli, Fabien Gandon To cite this version: Elena Cabrio, Julien
More informationPeter T. Wood. Third Alberto Mendelzon International Workshop on Foundations of Data Management
Languages Languages query School of Computer Science and Information Systems Birkbeck, University of London ptw@dcs.bbk.ac.uk Third Alberto Mendelzon International Workshop on Foundations of Data Management
More informationIntroduction to Spring 2007 Artificial Intelligence Midterm Solutions
NAME: SID#: Login: Sec: CS 88 Introduction to Spring 2007 Artificial Intelligence Midterm Solutions You have 80 minutes. There are five questions with equal points. Look at all the five questions and answer
More informationNews English.com Ready-to-use ESL / EFL Lessons
www.breaking News English.com Ready-to-use ESL / EFL Lessons 1,000 IDEAS & ACTIVITIES FOR LANGUAGE TEACHERS The Breaking News English.com Resource Book http://www.breakingnewsenglish.com/book.html Top
More informationUniversity of Rome Tor Vergata DBpedia Manuel Fiorelli
University of Rome Tor Vergata DBpedia Manuel Fiorelli fiorelli@info.uniroma2.it 07/12/2017 2 Notes The following slides contain some examples and pictures taken from: Lehmann, J., Isele, R., Jakob, M.,
More informationCharacter Encodings. Fabian M. Suchanek
Character Encodings Fabian M. Suchanek 22 Semantic IE Reasoning Fact Extraction You are here Instance Extraction singer Entity Disambiguation singer Elvis Entity Recognition Source Selection and Preparation
More informationA Keyword-based Structured Query Language
Expressive and Flexible Access to Web-Extracted Data : A Keyword-based Structured Query Language Department of Computer Science and Engineering Indian Institute of Technology Delhi 22th September 2011
More informationPRIOR System: Results for OAEI 2006
PRIOR System: Results for OAEI 2006 Ming Mao, Yefei Peng University of Pittsburgh, Pittsburgh, PA, USA {mingmao,ypeng}@mail.sis.pitt.edu Abstract. This paper summarizes the results of PRIOR system, which
More informationKnowledge Based Systems Text Analysis
Knowledge Based Systems Text Analysis Dr. Shubhangi D.C 1, Ravikiran Mitte 2 1 H.O.D, 2 P.G.Student 1,2 Department of Computer Science and Engineering, PG Center Regional Office VTU, Kalaburagi, Karnataka
More informationDeclarative Data Transformations for Linked Data Generation: the case of DBpedia
Declarative Data Transformations for Linked Data Generation: the case of DBpedia Ben De Meester, Wouter Maroy, Anastasia Dimou, Ruben Verborgh, and Erik Mannens Ghent University imec IDLab, Belgium In
More informationWikipedia 101: Bryn Mawr Edit-a-thon. Mary Mark Ockerbloom, Wikipedian in Residence, Chemical Heritage Foundation
Wikipedia 101: Bryn Mawr Edit-a-thon Mary Mark Ockerbloom, Wikipedian in Residence, Chemical Heritage Foundation What is Wikipedia? Wikipedia s Goal: To present all of human knowledge from a neutral point
More informationSerge Abiteboul. INRIA Saclay--Île-de-France & LRI Université Paris Sud. Comité de Visite AERES, February 3-4, 2009
1 Gemo Serge Abiteboul INRIA Saclay--Île-de-France & LRI Université Paris Sud Comité de Visite AERES, February 3-4, 2009 2 Organization The team Scientific contributions (with 2 Zooms) Achievements Evolution
More informationTowards Linked Data and ontology development for the semantic enrichment of volunteered geo-information
AGILE Link-VGI workshop, Helsinki 14 June 2016 Towards Linked Data and ontology development for the semantic enrichment of volunteered geo-information Rob Lemmens University of Twente, Faculty of Geo-Information
More informationSemantics and Ontologies for Geospatial Information. Dr Kristin Stock
Semantics and Ontologies for Geospatial Information Dr Kristin Stock Introduction The study of semantics addresses the issue of what data means, including: 1. The meaning and nature of basic geospatial
More informationOleksandr Kuzomin, Bohdan Tkachenko
International Journal "Information Technologies Knowledge" Volume 9, Number 2, 2015 131 INTELLECTUAL SEARCH ENGINE OF ADEQUATE INFORMATION IN INTERNET FOR CREATING DATABASES AND KNOWLEDGE BASES Oleksandr
More informationKNOWLEDGE GRAPH CONSTRUCTION
KNOWLEDGE GRAPH CONSTRUCTION Jay Pujara University of Maryland, College Park Max Planck Institute 7/9/2015 Can Computers Create Knowledge? Internet Massive source of publicly available information Knowledge
More informationSemantic Web Company. PoolParty - Server. PoolParty - Technical White Paper.
Semantic Web Company PoolParty - Server PoolParty - Technical White Paper http://www.poolparty.biz Table of Contents Introduction... 3 PoolParty Technical Overview... 3 PoolParty Components Overview...
More informationeresearch A Max Planck Perspective Kurt Mehlhorn Max-Planck-Institut für Informatik Interim Head, Max Planck Digital Library
eresearch A Max Planck Perspective Kurt Mehlhorn Max-Planck-Institut für Informatik Interim Head, Max Planck Digital Library 1 Overview The MPG eresearch, a Definition Max Planck Digital Library (MPDL):
More informationAAAI 2018 Tutorial Building Knowledge Graphs. Craig Knoblock University of Southern California
AAAI 2018 Tutorial Building Knowledge Graphs Craig Knoblock University of Southern California Wrappers for Web Data Extraction Extracting Data from Semistructured Sources NAME Casablanca Restaurant STREET
More information