Gerhard Weikum Max Planck Institute for Informatics & Saarland University http://www.mpi-inf.mpg.de/ ~weikum/ Semantic Search: from Names and Phrases to Entities and Relations
Feb 25, 2016
Gerhard Weikum Max Planck Institute for Informatics & Saarland Universityhttp://www.mpi-inf.mpg.de/~weikum/
Semantic Search:from Names and Phrasesto Entities and Relations
Acknowledgements
Big Picture: Opportunities Now !
KB Population
Info Extraction Semantic Authoring
Entity Linkage
Web of D
ataW
eb o
f Use
rs &
Con
tent
s
Very Large Knowledge Bases
Semantic Docs
Disambiguation
Big Picture: Opportunities Now !
KB Population
Info Extraction Semantic Authoring
Entity Linkage
Web of D
ataW
eb o
f Use
rs &
Con
tent
s
Very Large Knowledge Bases
Semantic Docs
Disambiguation
This talk:How Do We Search this World ofKnowledge, Data, and Text(and cope with ambiguity)
for Knowledge Harvestingsee talks at College de Franceand at VLDB School in Kunming
http://richard.cyganiak.de/2007/10/lod/lod-datasets_2011-09-19_colored.png
Web of Data: RDF, Tables, Microdata
YAGO
Cyc
TextRunner/ReVerb
WikiTaxonomy/WikiNet
SUMO
ConceptNet 5
BabelNet
ReadTheWeb
30 Bio. SPO triples (RDF) and growing
http://richard.cyganiak.de/2007/10/lod/lod-datasets_2011-09-19_colored.png
Web of Data: RDF, Tables, Microdata
YAGO
30 Bio. SPO triples (RDF) and growing
• 10M entities in 350K classes• 120M facts for 100 relations• 100 languages• 95% accuracy
• 4M entities in 250 classes• 500M facts for 6000 properties• live updates
• 25M entities in 2000 topics• 100M facts for 4000 properties• powers Google knowledge graph
Ennio_Morricone type composerEnnio_Morricone type GrammyAwardWinnercomposer subclassOf musicianEnnio_Morricone bornIn RomeRome locatedIn ItalyEnnio_Morricone created Ecstasy_of_GoldEnnio_Morricone wroteMusicFor The_Good,_the_Bad_,and_the_UglySergio_Leone directed The_Good,_the_Bad_,and_the_Ugly
owl:s
ameAs
rdf.freebase.com/ns/en.rome
owl:sameAs
owl:sameAs
data.nytimes.com/51688803696189142301
Coord
geonames.org/3169070/roma
N 41° 54' 10'' E 12° 29' 2''
dbpprop:citizenOf
dbpedia.org/resource/Rome
rdf:ty
pe
rdfs:subclassOf
yago/wordnet:Actor109765278
rdf:ty
pe
rdfs:subclassOfyago/wikicategory:ItalianComposer
yago/wordnet: Artist109812338
prop:actedInimdb.com/name/nm0910607/
Linked RDF Triples on the Web
prop: composedMusicFor
imdb.com/title/tt0361748/
dbpedia.org/resource/Ennio_Morricone
500 Mio. links
Embedding (RDF) Microdata in HTML Pages
May 2, 2011
Maestro Morricone will perform on the stage of the Smetana Hall to conduct the Czech National Symphony Orchestra and Choir. The concert will feature both Classical compositions and soundtracks such asthe Ecstasy of Gold.In programme two concerts for July 14th and 15th.
<html … May 2, 2011
<div typeof=event:music>
<span id="Maestro_Morricone">Maestro Morricone<a rel="sameAs"resource="dbpedia/Ennio_Morricone "/></span>…<span property = "event:location" >Smetana Hall </span>…<span property="rdf:type"resource="yago:performance">The concert </span> will feature …<span property="event:date" content="14-07-2011"></span>July 1
</div>
Supported by RDFaand microformats like schema.org
Outline
Opportunities Now
Entity Name Disambiguation
Question Answering
Disambiguation Reloaded
Wrap-Up
Semantic Search Today
Semantic Search Today (1)
Semantic Search Today (1)
Semantic Search Today (1)
Semantic Search Today (1)
Semantic Search Today (1)
Semantic Search Today (2)
Select ?x Where { ?x type composer [western movie] .?x wasBornIn ?y . ?y locatedIn Europe . }
Semantic Search Today (2)
Select ?x Where { ?x type composer .?x participatedIn ?y . ?y type western_film . }
Semantic Search Today (3)
Semantic Search Today (3)
Semantic Search Today (3)
Semantic Search Today (4)
Semantic Search Today (4)Key problem in semantic search: diversity and ambiguity of names and phrases !
Outline
Opportunities Now
Entity Name Disambiguation
Question Answering
Disambiguation Reloaded
Wrap-Up
Semantic Search Today
Three Different NLP Problems
Harry fought with you know who. He defeats the dark lord.
1) named-entity detection: segment & label by HMM or CRF (e.g. Stanford NER tagger)
2) co-reference resolution: link to preceding NP (trained classifier over linguistic features)3) named-entity disambiguation: map each mention (name) to canonical entity (entry in KB)
Three NLP tasks:
HarryPotter
DirtyHarry
LordVoldemort
The Who(band)
Prince Harryof England
3-23
Sergio talked to Ennio aboutEli‘s role in theEcstasy scene. This sequence onthe graveyardwas a highlight inSergio‘s trilogyof western films.
Named Entity Disambiguation
D5 Overview May 30, 2011
Sergio means Sergio_LeoneSergio means Serge_GainsbourgEnnio means Ennio_AntonelliEnnio means Ennio_MorriconeEli means Eli_(bible)Eli means ExtremeLightInfrastructureEli means Eli_WallachEcstasy means Ecstasy_(drug)Ecstasy means Ecstasy_of_Goldtrilogy means Star_Wars_Trilogytrilogy means Lord_of_the_Ringstrilogy means Dollars_Trilogy … … …
KB
Eli (bible)
Eli Wallach
Mentions(surface names)
Entities(meanings)
Dollars Trilogy
Lord of the Rings
Star Wars Trilogy
Benny Andersson
Benny Goodman
Ecstasy of Gold
Ecstasy (drug)
?
3-24
Sergio talked to Ennio aboutEli‘s role in theEcstasy scene. This sequence onthe graveyardwas a highlight inSergio‘s trilogyof western films.
Mention-Entity Graph
Dollars Trilogy
Lord of the Rings
Star Wars
Ecstasy of Gold
Ecstasy (drug)
Eli (bible)
Eli Wallach
KB+Stats
weighted undirected graph with two types of nodes
Popularity(m,e):• freq(e|m)• length(e)• #links(e)
Similarity (m,e):• cos/Dice/KL (context(m), context(e))
bag-of-words orlanguage model:words, bigrams, phrases
3-25
Sergio talked to Ennio aboutEli‘s role in theEcstasy scene. This sequence onthe graveyardwas a highlight inSergio‘s trilogyof western films.
Mention-Entity Graph
Dollars Trilogy
Lord of the Rings
Star Wars
Ecstasy of Gold
Ecstasy (drug)
Eli (bible)
Eli Wallach
KB+Stats
weighted undirected graph with two types of nodes
Popularity(m,e):• freq(e|m)• length(e)• #links(e)
Similarity (m,e):• cos/Dice/KL (context(m), context(e))
jointmapping
3-26
Mention-Entity Graph
27 / 20
Dollars Trilogy
Lord of the Rings
Star Wars
Ecstasy of Gold
Ecstasy(drug)
Eli (bible)
Eli Wallach
KB+Stats
weighted undirected graph with two types of nodes
Popularity(m,e):• freq(m,e|m)• length(e)• #links(e)
Similarity (m,e):• cos/Dice/KL (context(m), context(e))
Coherence (e,e‘):• dist(types)• overlap(links)• overlap (anchor words)
Sergio talked to Ennio aboutEli‘s role in theEcstasy scene. This sequence onthe graveyardwas a highlight inSergio‘s trilogyof western films.
3-27
Mention-Entity Graph
28 / 20
KB+Stats
weighted undirected graph with two types of nodes
Popularity(m,e):• freq(m,e|m)• length(e)• #links(e)
Similarity (m,e):• cos/Dice/KL (context(m), context(e))
Coherence (e,e‘):• dist(types)• overlap(links)• overlap (anchor words)
American Jewsfilm actorsartistsAcademy Award winners
Metallica songsEnnio Morricone songsartifactssoundtrack music
spaghetti westernsfilm trilogiesmoviesartifactsDollars Trilogy
Lord of the Rings
Star Wars
Ecstasy of Gold
Ecstasy (drug)
Eli (bible)
Eli Wallach
Sergio talked to Ennio aboutEli‘s role in theEcstasy scene. This sequence onthe graveyardwas a highlight inSergio‘s trilogyof western films.
3-28
Mention-Entity Graph
29 / 20
KB+Stats
weighted undirected graph with two types of nodes
Popularity(m,e):• freq(m,e|m)• length(e)• #links(e)
Similarity (m,e):• cos/Dice/KL (context(m), context(e))
Coherence (e,e‘):• dist(types)• overlap(links)• overlap (anchor words)
http://.../wiki/Dollars_Trilogyhttp://.../wiki/The_Good,_the_Bad, _the_Uglyhttp://.../wiki/Clint_Eastwoodhttp://.../wiki/Honorary_Academy_Award
http://.../wiki/The_Good,_the_Bad,_the_Uglyhttp://.../wiki/Metallicahttp://.../wiki/Bellagio_(casino)http://.../wiki/Ennio_Morricone
http://.../wiki/Sergio_Leonehttp://.../wiki/The_Good,_the_Bad,_the_Uglyhttp://.../wiki/For_a_Few_Dollars_Morehttp://.../wiki/Ennio_MorriconeDollars Trilogy
Lord of the Rings
Star Wars
Ecstasy of Gold
Ecstasy (drug)
Eli (bible)
Eli Wallach
Sergio talked to Ennio aboutEli‘s role in theEcstasy scene. This sequence onthe graveyardwas a highlight inSergio‘s trilogyof western films.
3-29
Mention-Entity Graph
30 / 20
KB+StatsPopularity(m,e):• freq(m,e|m)• length(e)• #links(e)
Similarity (m,e):• cos/Dice/KL (context(m), context(e))
Coherence (e,e‘):• dist(types)• overlap(links)• overlap (anchor words)
Metallica on Morricone tributeBellagio water fountain showYo-Yo MaEnnio Morricone composition
The Magnificent SevenThe Good, the Bad, and the UglyClint EastwoodUniversity of Texas at Austin
For a Few Dollars MoreThe Good, the Bad, and the UglyMan with No Name trilogysoundtrack by Ennio Morricone
weighted undirected graph with two types of nodes
Dollars Trilogy
Lord of the Rings
Star Wars
Ecstasy of Gold
Ecstasy (drug)
Eli (bible)
Eli Wallach
Sergio talked to Ennio aboutEli‘s role in theEcstasy scene. This sequence onthe graveyardwas a highlight inSergio‘s trilogyof western films.
3-30
Joint Mapping
• Build mention-entity graph or joint-inference factor graph from knowledge and statistics in KB• Compute high-likelihood mapping (ML or MAP) or dense subgraph such that: each m is connected to exactly one e (or at most one e)
9030
5100
100
50 20
50
90
80 90
30
10 10
20
30
30
3-31
Coherence Graph Algorithm
• Compute dense subgraph to maximize min weighted degree among entity nodes such that: each m is connected to exactly one e (or at most one e)• Greedy approximation: iteratively remove weakest entity and its edges• Keep alternative solutions, then use local/randomized search
9030
5100
100
50 50
90
80 90
30
10 20
10
20
30
30
[J. Hoffart et al.: EMNLP‘11]140
180
50
470
145
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3-32
Mention-Entity Popularity Weights
• Collect hyperlink anchor-text / link-target pairs from• Wikipedia redirects• Wikipedia links between articles• Interwiki links between Wikipedia editions• Web links pointing to Wikipedia articles
…• Build statistics to estimate P[entity | name]
• Need dictionary with entities‘ names:• full names: Arnold Alois Schwarzenegger, Los Angeles, Microsoft Corp.• short names: Arnold, Arnie, Mr. Schwarzenegger, New York, Microsoft, …• nicknames & aliases: Terminator, City of Angels, Evil Empire, …• acronyms: LA, UCLA, MS, MSFT• role names: the Austrian action hero, Californian governor, CEO of MS, …
… plus gender info (useful for resolving pronouns in context): Bill and Melinda met at MS. They fell in love and he kissed her.
[Milne/Witten 2008, Spitkovsky/Chang 2012]
3-33
Mention-Entity Similarity Edges
Extent of partial matches Weight of matched words
Precompute characteristic keyphrases q for each entity e:anchor texts or noun phrases in e page with high PMI:
)()(
),()(~)|(
mcontextinekeyphrasesq
mcover(q)distqscoremescore
1
)|(#~)|(qw
cover(q)w
e)|weight(w
ewweight
cover(q)oflengthwordsmatchingeqscore
)()(),(log),(efreqqfreq
eqfreqeqweight
Match keyphrase q of candidate e in context of mention m
Compute overall similarity of context(m) and candidate e
„Metallica tribute to Ennio Morricone“
The Ecstasy piece was covered by Metallica on the Morricone tribute album.
3-34
Entity-Entity Coherence EdgesPrecompute overlap of incoming links for entities e1 and e2
))2(),1(min(log||log))2()1(log())2,1(max(log1
eineinEeineineein~e2)coh(e1,-mw
Alternatively compute overlap of anchor texts for e1 and e2
or overlap of keyphrases, or similarity of bag-of-words, or …
)2()1()2()1(
engramsengramsengramsengrams
~e2)coh(e1,-ngram
Optionally combine with type distance of e1 and e2(e.g., Jaccard index for type instances)
For special types of e1 and e2 (locations, people, etc.)use spatial or temporal distance
3-35
AIDA: Accurate Online Disambiguation
http://www.mpi-inf.mpg.de/yago-naga/aida/3-36
AIDA: Accurate Online Disambiguation
http://www.mpi-inf.mpg.de/yago-naga/aida/3-37
http://www.mpi-inf.mpg.de/yago-naga/aida/
AIDA: Very Difficult Example
3-38
http://www.mpi-inf.mpg.de/yago-naga/aida/
AIDA: Very Difficult Example
3-39
AIDA: Accurate Online Disambiguation
http://www.mpi-inf.mpg.de/yago-naga/aida/3-40
AIDA: Accurate Online Disambiguation
http://www.mpi-inf.mpg.de/yago-naga/aida/3-41
Some NED Online Tools forJ. Hoffart et al.: EMNLP 2011, VLDB 2011https://d5gate.ag5.mpi-sb.mpg.de/webaida/P. Ferragina, U. Scaella: CIKM 2010http://tagme.di.unipi.it/R. Isele, C. Bizer: VLDB 2012http://spotlight.dbpedia.org/demo/index.htmlReuters Open Calaishttp://viewer.opencalais.com/ S. Kulkarni, A. Singh, G. Ramakrishnan, S. Chakrabarti: KDD 2009http://www.cse.iitb.ac.in/soumen/doc/CSAW/D. Milne, I. Witten: CIKM 2008http://wikipedia-miner.cms.waikato.ac.nz/demos/annotate/
perhaps more
some use Stanford NER tagger for detecting mentionshttp://nlp.stanford.edu/software/CRF-NER.shtml
3-42
NED: Experimental EvaluationBenchmark:• Extended CoNLL 2003 dataset: 1400 newswire articles• originally annotated with mention markup (NER), now with NED mappings to Yago and Freebase• difficult texts: … Australia beats India … Australian_Cricket_Team … White House talks to Kreml … President_of_the_USA … EDS made a contract with … HP_Enterprise_Services
Results:Best: AIDA method with prior+sim+coh + robustness test82% precision @100% recall, 87% mean average precisionComparison to other methods, see paper
J. Hoffart et al.: Robust Disambiguation of Named Entities in Text, EMNLP 2011http://www.mpi-inf.mpg.de/yago-naga/aida/
3-43
Ongoing Research & Remaining Challenges• More efficient graph algorithms (multicore, etc.)
• Short and difficult texts: • tweets, headlines, etc.• fictional texts: novels, song lyrics, etc.• incoherent texts
• Disambiguation beyond entity names:• coreferences: pronouns, paraphrases, etc.• common nouns, verbal phrases (general WSD)
• Leverage deep-parsing structures, leverage semantic types Example: Page played Kashmir on his Gibson
subj obj
mod
• Allow mentions of unknown entities, mapped to null
• Structured Web data: tables and lists
3-44
Variants of NED at Web Scale
• How to run this on big batch of 1 Mio. input texts? partition inputs across distributed machines, organize dictionary appropriately, … exploit cross-document contexts
• How to handle Web-scale inputs (100 Mio. pages) restricted to a set of interesting entities? (e.g. tracking politicians and companies)
Tools can map short text onto entities in a few seconds
3-45
Outline
Opportunities Now
Entity Name Disambiguation
Question Answering
Disambiguation Reloaded
Wrap-Up
Semantic Search Today
Deep Question Answering
99 cents got me a 4-pack of Ytterlig coasters from this Swedish chain
This town is known as "Sin City" & its downtown is "Glitter Gulch"
William Wilkinson's "An Account of the Principalities of Wallachia and Moldavia" inspired this author's most famous novel
As of 2010, this is the only former Yugoslav republic in the EU
YAGO
knowledgeback-ends
questionclassification &decomposition
D. Ferrucci et al.: Building Watson. AI Magazine, Fall 2010.IBM Journal of R&D 56(3/4), 2012: This is Watson.
Semantic Keyword Search Need to map (groups of) keywords onto entities & relationshipsbased on name-entity similarities/probabilities
q: composer Rome scores westerns
[Ilyas et al. Sigmod‘10]
Media Composervideo editor
Western Digital
Rome(Italy)
goal infootball
film music
composer(creatorof music)
Rome(NY)
LazioRoma
western movies
western world
Western (airline)ASRoma
Western (NY)
… born in … … plays for … … used in … … recorded at …
Natural Language Questions are Natural
Who composed scores for westerns and is from Rome?
translate question into Sparql query:• dependency parsing to decompose question• mapping of question units onto entities, classes, relations
Who composedscores for westernsand is from Rome?
map resultsinto tabular or visual presentationor speech
From Questions to Queries
NL question:
Who composed scores for westerns and is from Rome?
scores for westerns
is from Rome Who composed scores
Dependency parsing exposes structure of question „triploids“ (sub-cues)
2-50
From Triploids to TriplesWho composed scores for westerns and is from Rome?
Who is from Rome
Who composed scores
scores for westerns
?x composed scores
?x bornIn Rome
scores contributesTo ?y?y type westernMovie
?x type composer?x composed ?s
?s contributesTo ?y
?s type music
2-51
Pattern Dictionary for Relations[N. Nakashole et al.: EMNLP 2012]
WordNet-style dictionary/taxonomy for relational phrases based on SOL patterns (syntactic-lexical-ontological)
• Relational phrases can be synonymous
• One relational phrase can subsume another
• Relational phrases are typed
Problem: cope with language diversity & ambiguityExample: composed …, wrote …, created …, …
“graduated from” “obtained degree in * from”“and $PRP ADJ advisor” “under the supervision of”
“wife of” “ spouse of”
<person> graduated from <university><singer> released <album><singer> covered <song> <book> covered <event>
PATTY: Pattern Taxonomy for Relations[N. Nakashole et al.: EMNLP 2012, demo at VLDB 2012]
350 000 SOL patterns with 4 Mio. instancesDerived from large data (Wikipedia, NYT, ClueWeb)by scalable sequence miningaccessible at: www.mpi-inf.mpg.de/yago-naga/patty
Disambiguation Mapping for TriploidsWho composed scores for westerns and is from Rome?
composed
composedscores
scores for
westerns
is from
Rome
Who
q1
q2
q3
q4
Combinatorial Optimization by ILP (with type constraints etc.)
e: Rome (Italy)e: Lazio Roma
c: personc: musiciane: WHO
r: createdr: wroteCompositionr: wroteSoftware
c:soundtrackr: soundtrackForr: shootsGoalFor
r: bornInr: actedIn
c: western moviee: Western Digital
wei
ghte
d ed
ges
(coh
eren
ce, s
imila
rity,
etc
.)
Relaxing Overconstrained QueriesSelect ?p Where {
?p composed ?s . ?s type music . ?s for ?m . ?m type movie .?p bornIn Rome . }
Select ?p Where {
?p composed ?s . ?s type music . ?s for ?m . ?m type movie [western] .?p bornIn Rome . }
Select ?p Where {
?p ?rel1 ?s [composed] . ?s type music . ?s ?rel2 ?m . ?m type movie [western] .?p bornIn Rome . }
with extended SPARQL-FullText: SPOX quad patterns
(S. Elbassuoni et al.: CIKM‘10, ESWC’11, SIGIR‘12)
Select ?p Where {?p composed ?s . ?s type music . ?s for ?m . ?m type movie [western] .?p bornIn Rome . }
Preliminary Results (M. Yahya et al.: WWW‘12, EMNLP‘12)
http://www.mpi-inf.mpg.de/yago-naga/deanna/
Outline
Opportunities Now
Entity Name Disambiguation
Question Answering
Disambiguation Reloaded
Wrap-Up
Semantic Search Today
Disambiguation MappingWho composed scores for westerns and is from Rome?
composed
composedscores
scores for
westerns
is from
Rome
Who
q1
q2
q3
q4
e:Rome (Italy)e:Lazio Roma
c:personc:musiciane:WHO
r:createdr:wroteCompositionr:wroteSoftware
c:soundtrackr:soundtrackForr:shootsGoalFor
r:bornInr:actedIn
c:western moviee:Western Digital
wei
ghte
d ed
ges
(coh
eren
ce, s
imila
rity,
etc
.)
Selection: Xi Assignment: YijJointMapping: Zkl
[M.Yahya et al.: EMNLP‘12]
Disambig. Mapping: Objective FunctionWho composed scores for westerns and is from Rome?
composed
composedscores
scores for
westerns
is from
Rome
Who
q1
q2
q3
q4
e:Rome (Italy)e:Lazio Roma
c:personc:musiciane:WHO
r:createdr:wroteCompositionr:wroteSoftware
c:soundtrackr:soundtrackForr:shootsGoalFor
r:bornInr:actedIn
c:western moviee:Western Digital
wei
ghte
d ed
ges
(coh
eren
ce, s
imila
rity,
etc
.)
Selection: Xi Assignment: YijJointMapping: Zkl
maximize i,j wij Yij + k,l vkl Zkl +… subject to:1) Yij Xi for all i,j2) j Yij 1 for all i3) Zkl i,j Yik and Zkl j Yil for all k,l4) Xi,Yij,Zkl {0,1}
wijvkl
Disambig. Mapping: ConstraintsWho composed scores for westerns and is from Rome?
composed
composedscores
scores for
westerns
is from
Rome
Who
q1
q2
q3
q4
e:Rome (Italy)e:Lazio Roma
c:personc:musiciane:WHO
r:createdr:wroteCompositionr:wroteSoftware
c:soundtrackr:soundtrackForr:shootsGoalFor
r:bornInr:actedIn
c:western moviee:Western Digital
wei
ghte
d ed
ges
(coh
eren
ce, s
imila
rity,
etc
.)
Selection: Xi Assignment: YijJointMapping: Zkl
maximize i,j wij Yij + k,l vkl Zkl +… subject to:5) Qhi = 1 g Qhg = 3 for all h,i6) Xi + Xg 1 for all mutually exclusive i,g7) Qhi = 1 g,j Qhg Ygj = 1 for relation nodes j
wijvkl
Selection: Qhi
Disambig. Mapping: Type ConstraintsWho composed scores for westerns and is from Rome?
composed
composedscores
scores for
westerns
is from
Rome
Who
q1
q2
q3
q4
e:Rome (Italy)e:Lazio Roma
c:personc:musiciane: WHO
r:createdr:wroteCompositionr:wroteSoftware
c:soundtrackr:soundtrackForr:shootsGoalFor
r:bornInr:actedIn
c:western moviee:Western Digital
wei
ghte
d ed
ges
(coh
eren
ce, s
imila
rity,
etc
.)
Selection: Xi Assignment: YijJointMapping: Zkl
maximize i,j wij Yij + k,l vkl Zkl +… subject to:8) Yij = 1 and j is relation node and Zkj=1 and Zjl=1 domain(j) types(k) and range(j) types(l)
wijvkl
Selection: Qhi
ILP optimizers like Gurobisolve this in 1 or 2 seconds
Outline
Opportunities Now
Entity Name Disambiguation
Question Answering
Disambiguation Reloaded
Wrap-Up
Semantic Search Today
Summary
• Web of Data & Knowledge & Text (RDF + Phrases) Calls for Semantic Search by Entities, Classes & Relations
• Diversity & Ambiguity of Names and Phrases Calls for Disambiguation Mapping
• Strong Story for Entity Name Disambiguation
• Ongoing Work on Relation Phrase Disambiguation
• Cornerstone of Question Answering with Natural Language or Advanced Keywords
Great opportunity towards next-generation searchChallenging problems: robustness, scale, dynamics & transfer
Take-Home Message
Solve „Who composed the Ecstasy and other pieces for westerns?“
can solve semantic search with natural-language disambiguation