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Leveraging Wikipedia-based Features for Entity Relatedness
and Recommendations
Nitish AggarwalSupervised by Dr. Paul Buitelaar
PhD Viva
Brad Pitt
Motivation
2
MotivationSemantic Web
Technologies:1. RDF2. SPARQL3. Ontology4. Linked data5. Turtle (syntax)
Entity Recommendation
Companies:1. Metaweb2. Ontoprise GmbH3. OpenLink Software4. Ontotext5. Powerset (company)
MyosinProteins and cells:1. Actin2. Muscle contraction3. Sarcomere4. Myofibril5. Cytoskeleton
Biologists:1. Hugh Huxley2. James Spudich3. Ronald Vale4. Manuel Morales5. Brunó Ferenc Straub
3
Determine the degree of relatedness between two entities
Brad Pitt Tom Cruise
?
Entity Relatedness
4
Person, location, organization
Time, date, money, percent
Event, movie, disease, symptom, side effect, law, license and more
Background
Entity• Many such types are covered
in Wikipedia
• More than 2K classes in DBpedia
• More than 350k classes in Yago
• Every Wikipedia article is considered about an entity
5
Motor vehicle
Car Motorcycle
Automobile
AutoCar seat
Car windows
s
h h
m m
Background
Relatedness
Synonyms Similar
Related
Subs
titut
abili
ty
6
Outline• Motivation• Entity Relatedness
• Distributional Semantics for Entity Relatedness (DiSER)• Evaluation
• Entity Recommendation• Wikipedia-based Features for Entity Recommendation (WiFER)• Evaluation
• Text Relatedness• Non-Orthogonal Explicit Semantic Analysis (NESA)• Evaluation
• Application and Industry Use Cases• Conclusion
7
Wikipedia Features for Entity Recommendation
(WiFER)Feature
Extraction
Thesis Overview
Distributional Semantic for Entity Relatedness
(DiSER)
DistributionalRepresentatio
n
Non-Orthogonal Explicit Semantic Analysis
(NESA)
Chapter V
Chapter IV
Chapter VI
8
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
Thesis Overview
Wikipedia Features for Entity Recommendation
(WiFER)Feature
Extraction
Distributional Semantic for Entity Relatedness
(DiSER)
DistributionalRepresentatio
n
Non-Orthogonal Explicit Semantic Analysis
(NESA)
Chapter IV
9
Entity Relatedness
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
10
Entity Relatedness: State of the Art• Graph-based methods
• Path distance in Wikipedia graph (Strube and Ponzetto, 2006)• Normalized Google Distance on Wikipedia graph (Witten and Milne,
2008)• Personalized pagerank on Wikipedia graph (Agirre et. al, 2015)• Path-based measures on DBpedia graph (Hulpus et. al, 2015)
• Corpus-based methods• Key-phrase Overlap for Related Entities (KORE): partial overlaps
between key-phrases in corresponding Wikipedia articles (Hoffart et. al, 2012)
• Text relatedness measures: use colocation information in text
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
11
Explicit Semantic Analysis (ESA)Uses explicit (manually defined) concepts like Wikipedia articles where every article is considered describing a single concept (Gabrilovich and
Markovitch, 2007)
Entity Relatedness: State of the ArtDistributional Semantics
word1 W11 W12 W13 W14 …....... W1n
word2 W21 W22 W23 W24 …....... W2n
word3 W31 W32 W33 W34 …........ W3n
wordm Wm1 Wm2 Wm3 Wm4 …... Wmn
...
doc1 doc2 doc3 doc4 ….... docn
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
12
word1 W11 W12 W13 W14 …....... W1n
word2 W21 W22 W23 W24 …....... W2n
word3 W31 W32 W33 W34 …........ W3n
wordm Wm1 Wm2 Wm3 Wm4 …... Wmn
...
Entity Relatedness: State of the ArtDistributional Semantics
doc1 doc2 doc3 doc4 ….... docn
Implicit/Latent Semantic Analysis (LSA)Transforms sparse document space into a dense latent topic space
Latent Dirichlet Allocation (LDA)(Blei et al., 2003)
Latent Semantic Analysis (LSA)(Deerwester et al., 1990)
Neural Embeddings(Word2Vec)(Mikolov et al., 2013)
n ~ 1M
word1 W11 W12 ……..... W1k
word2 W21 W22 ……..... W2k
wordm Wm1 Wm2 ……..... Wmk
...
topic1 topic2 … topick
Dimensionality
Reduction
k < 1000
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
13
Limitation of Text Relatedness Measures
• Compositionality • Most of the entities are multiword expressions• Vector(Brad Pitt) = Vector(Brad) + Vector(Pitt) ?
• Ambiguity • Vector of an entity with ambiguous name like “Nice” (French city)
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
14
Chapter IV
Distributional Semantics for Entity Relatedness (DiSER)
entity1 W11 W12 W13 W14 …....... W1n
entity2 W21 W22 W23 W24 …....... W2n
entity3 W31 W32 W33 W34 …........ W3n
entityn Wn1 Wn2 Wn3 Wn4 …... Wnn
...
doc1 doc2 doc3 doc4 ….... docn
Wikipedia-based Distributional Semantics for Entity Relatedness In: AAAI-FSS-2014
[Steve Jobs] co-founded Apple in 1976 to sell Wozniak’s [Apple I] [Personal Computer]. [Steve Jobs | Jobs] was CEO of [Apple Inc. | Apple] and largest shareholder of [Pixar]. Jobs is widely recognized as a pioneer of the [Microcomputer Revolution], along [Steve Wozniak | Wozniak].
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
Annotated Wikipedia with entities
One sense per document
Wikipedia entities[Steve Jobs] [Apple Inc.| Apple] [Steve Wozniak | Wozniak]’ [Apple I] [Personal Computer]. [Steve Jobs | Jobs] was CEO of [Apple Inc. | Apple] and largest shareholdef [Pixar]. [Steve Jobs | Jobs] is widely recognizpioneer of the [Microcomputer Revolution], along [Steve Wozniak | Wozniak].
15
The Tree of Life (film)Falmouth, CornwallWorld War Z (film)What Just HappenedA Mighty Heart (film)Plan B EntertainmentJamaican PatoisRichard: A NovelSobriquetI Want a Famous Face
Brad Pitt (DiSER)Damiani (jewelry
company)University of Pittsburgh
BandBrad PittMake It Right FoundationPittsburgh men’s basketballBrangelinaPittsburgh Panthers baseballPitt (Comics)Pitt RiverBrad Pitt filmography
Brad Pitt (ESA)
Wikipedia-based Distributional Semantics for Entity Relatedness In: AAAI-FSS-2014
ESA vs DiSER Vector
Chapter IV
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
16
Entity Relatedness: Evaluation
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
17
• Absolute relatedness score• Relatedness between “Apple Inc.” and “Steve Jobs”• Very low inter-annotator agreement
• Relative relatedness score• Is “Steve Jobs” more related with “Apple Inc.” than “Bill Gates”• High inter-annotator agreement
• KORE (Hoffart et al., 2012)• 21 seed entities• Every entity has list of 20 entities with their relatedness score• 420 entity pairs in total
Entity Relatedness: DatasetMotivation Entity
RelatednessEntity
RecommendationText Relatedness Application Conclusion
18
ApproachesSpearman
Rank Correlation
Graph-based measures
Path-DBpedia (Hulpus et al., 2015) 0.610WLM (Witten and Milne, 2008) 0.659PPR (Agirre et al., 2015) 0.662
Corpus-based measures
Word2Vec (Mikolov et al., 2013) 0.181GloVe (Pennington et al., 2014) 0.194LSA (Landauer et al., 1998) 0.375KORE (Hoffart et al., 2012) 0.679ESA (Gabrilovich and Markovitch, 2007)
0.691
DiSER 0.781
Wikipedia-based Distributional Semantics for Entity Relatedness In: AAAI-FSS-2014
Results: KORE DatasetMotivation Entity
RelatednessEntity
RecommendationText Relatedness Application Conclusion
19
DiSER Vector for non-Wikipedia Entities
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
20
BBC: http://www.bbc.com/news/world-europe-22204377
Article about Savita
Context-DiSER
Noun phrase extraction: StanfordNLP
Entity linking: Prior probability
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
21
AbortionAbortion-
rights movement
The Irish Times
United States pro-life
movement
Vincent Browne
Michael D.
Higgins
Context-DiSER
Irish abortion lawDeath of SavitaGalway University HospitalMiscarriageCatholic Country…….
Savita Halappanavar
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
22
Approaches Spearman Rank Correlation
KORE (state of the art) 0.679Context-ESA 0.684Context-DiSER (Manual linking)
0.769
Context-DiSER (Automatic linking)
0.719
Wikipedia-based Distributional Semantics for Entity Relatedness In: AAAI-FSS-2014
Context-DiSER: Results on KORE Dataset
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
23
Thesis Overview
Wikipedia Features for Entity Recommendation
(WiFER)Feature
Extraction
Distributional Semantic for Entity Relatedness
(DiSER)
DistributionalRepresentatio
n
Non-Orthogonal Explicit Semantic Analysis
(NESA)
Chapter V
Chapter IV
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
24
Entity Recommendation
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
25
• Classical Recommendation Systems• Focus on personalized recommendation• Require user-item preferences
• Entity Recommendation in Web Search (Blanco et al., 2013)• Co-occurrence features: query logs, query session, Flickr tags,
tweets• Graph-based features: shared connections in Yahoo knowledge
graph and others domain specific knowledge bases• Entity and Relation type in Knowledge graph• More than 100 features• Combines features using learning to rank
Entity Recommendation: State of the Art
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
26
Features:
Prior Probability of Entity1
Prior Probability of Entity2
Joint ProbabilityConditional ProbabilityReverse Conditional ProbabilityCosine SimilarityPointwise Mutual InformationDistributional Semantic Model
Learning to Rank
Leveraging Wikipedia Knowledge for Entity Recommendations In: ISWC 2015
Wikipedia-based Features for Entity Recommendation (WiFER)
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
27
Prior Probability of Entity1Prior Probability of Entity2Joint ProbabilityConditional ProbabilityCosine Similarity Pointwise Mutual InformationReverse Conditional ProbabilityDistributional Semantic Model (ESA)
Wikipedia Text Wikipedia EntitiesPrior Probability of Entity1Prior Probability of Entity2Joint ProbabilityConditional ProbabilityCosine Similarity Pointwise Mutual InformationReverse Conditional ProbabilityDistributional Semantic Model (DiSER)
Wikipedia-based Features for Entity Recommendation (WiFER)
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
28
• Learning to Rank• Gradient Boosted Decision Trees (GBDT) (Li Hang, 2011)• It builds the model in a stage-wise fashion
• Dataset: Entity recommendation in web search• 4,797 web search queries (entities)• Every entity query has a list of entity candidates (47,623 entity-
pairs)• All candidates are tagged on 5 label scales: Excellent, Prefer, Good,
Fair, and Bad
Combining Features
Type Total instances Percentage
Location 22,062 46.32People 21,626 45.41Movies 3,031 6.36
TV Shows 280 0.58Album 563 1.18Total 47,623 100
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
29
• Evaluation• Normalized discounted cumulative gain (NDCG@10) • 10 fold cross validation
Features All Person LocationSpark
(Blanco et al., 2013)
0.9276 0.9479 0.8882
WiFER 0.9173 0.9431 0.8795Spark+WiFE
R0.9325 0.9505 0.8987
Insights into Entity Recommendation in Web SearchIn: IESD at ISWC, 2015
Entity Recommendation: Results Motivation Entity
RelatednessEntity
RecommendationText Relatedness Application Conclusion
30
Insights into Entity Recommendation in Web SearchIn: IESD at ISWC, 2015
Entity Recommendation: Feature Analysis in Spark+WiFER
Relation type
Cosine similarity over Flickr tagsProbability of target entity over Wikipedia text corpusCF7 over Flickr tagsDSM over Wikipedia entities corpus (DiSER)Conditional user probability over query termsDSM over Wikipedia text corpus (ESA)Probability of source entity over Wikipedia entities corpusProbability of target entity over Flickr tagsProbability of target entity over Wikipedia entities corpus
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
31
Thesis OverviewMotivation Entity
RelatednessEntity
RecommendationText Relatedness Application Conclusion
Wikipedia Features for Entity Recommendation
(WiFER)Feature
Extraction
Distributional Semantic for Entity Relatedness
(DiSER)
DistributionalRepresentatio
n
Non-Orthogonal Explicit Semantic Analysis
(NESA)
Chapter V
Chapter IV
Chapter VI
32
Text Relatedness:Non-Orthogonal Explicit Semantic Analysis (NESA)
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
33
ESA assumes that related words share highly weighted concepts in their distributional vector
Chapter VIImproving ESA with Document SimilarityIn: ECIR-2013
“soccer”History of Soccer in the United
StatesSoccer in the United States
United States Soccer Federation
North American Soccer League
United Soccer Leagues
“football”
FIFA
FootballHistory of association
footballFootball in England
Association football
ESA(football, soccer) = 0.0
Orthogonality in ESAMotivation Entity
RelatednessEntity
RecommendationText Relatedness Application Conclusion
34
Chapter VIImproving ESA with Document SimilarityIn: ECIR-2013
“soccer”History of Soccer in the United
StatesSoccer in the United States
United States Soccer Federation
North American Soccer League
United Soccer Leagues
“football”
FIFA
FootballHistory of association
footballFootball in England
Association football
NESA(football, soccer) = (FIFA x Soccer in the United States + FIFA x United Soccer Leagues ….) = 0.38
Non-Orthogonal Explicit Semantic Analysis (NESA)
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
35
• ESA: v1 and v2 are the n-dimensional vectors for words w1 and w2
• relESA (w1, w2) = v1T . v2
• NESA: Correlation between vector dimensions
• relNESA (w1,w2) = v1T . C . v2
• C(n,n) = ET . E
• Dimension correlation methods• DiSER scores between corresponding Wikipedia article
Non-Orthogonal Explicit Semantic Analysis (NESA)
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
36
• WN353• 353 word pairs annotated by 13-15 experts on a scale of 1-10.
• RG65• 65 word pairs annotated by 51 experts on scale of 0-4
• MC30• 30 word pairs annotated by 38 experts on scale of 0-1
• MT287• 287 word pairs annotated by 10-12 experts on scale of 0-1
Word Relatedness DatasetsMotivation Entity
RelatednessEntity
RecommendationText Relatedness Application Conclusion
37
Non-Orthogonal Explicit Semantic AnalysisIn: *SEM-2015 Chapter VI
WN353 MC30 RG65 MT287
LSA 0.579 0.667 0.616 0.555
LSA (Wiki) 0.538 0.744 0.697 0.353
Word2Vec 0.663 0.824 0.751 0.560
ESA 0.66 0.765 0.826 0.507
NESA 0.696 0.784 0.839 0.572
Spearman rank correlation with word similarity gold standard datasets
NESA: ResultsMotivation Entity
RelatednessEntity
RecommendationText Relatedness Application Conclusion
38
Non-Orthogonal Explicit Semantic AnalysisIn: *SEM-2015 Chapter VI
NESA: Results
• Word similarity vs relatedness (Agirre et al., 2009)• WN353Rel: 202 word pairs from WN353• WN353Sim: 252 word pairs from WN353Spearman rank correlation with word similarity vs relatedness
datasets WN353Rel
WN353Sim
LSA 0.521 0.662
LSA (Wiki) 0.506 0.559
Word2Vec 0.601 0.741
ESA 0.643 0.663
NESA 0.663 0.719
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
39
Outline• Motivation• Entity Relatedness
• Distributional Semantics for Entity Relatedness (DiSER)• Evaluation
• Entity Recommendation• Wikipedia-based Features for Entity Recommendation (WiFER)• Evaluation
• Text Relatedness• Non-Orthogonal Explicit Semantic Analysis (NESA)• Evaluation
• Application and Industry Use Cases• Conclusion
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
40
Chapter VIIhttp://enrg.insight-centre.org/
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
41
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
42
EnRG SPARQL Endpoint
National University of Ireland, Galway
Industrial Use Cases
Medical entity linking for question-answering and relationship explanation in Knowledge Graph
Entity Recommendation in Web Search
Company name disambiguation for social profiling
Motivation Entity Relatedness
Entity Recommendation
Text Relatedness Application Conclusion
43
• Entity Relatedness• Distributional Semantics for Entity Relatedness (DiSER)• Outperformed state of the art entity relatedness measures
• Entity Recommendation• Wikipedia-based Features for Entity Recommendation (WiFER)• Effective features for entity recommendation in web search
• Text Relatedness• Non-Orthogonal Explicit Semantic Analysis (NESA)• Outperformed other existing word relatedness measures
• Entity Relatedness Graph (EnRG)• Contains all Wikipedia entities and their pre-computed relatedness
scores• Contains distributional vectors for all Wikipedia entities
ConclusionMotivation Entity
RelatednessEntity
RecommendationText Relatedness Application Conclusion
45
• Relationship explanation for recommended entities• Best path in knowledge graph• Best natural language description
• Knowledge discovery• Analogy querying over knowledge graph
e.g. Google to Motorola => Microsoft to ?• Example based querying
e.g. Google to Motorola => ? to ?
Future Research Directions
46
Related Queries?