The document discusses using knowledge base semantics in context-aware entity linking. It proposes a collective entity linking approach that identifies all entities mentioned in a text at once by leveraging an RDF knowledge base. It introduces a weighted semantic relatedness measure to compute the collective coherence score between candidate entities. Experimental results show the approach outperforms state-of-the-art methods on several benchmark datasets.

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Introduction Collective Entity Linking Conclusion and perspectives Bibliographie
Using Knowledge Base Semantics in
Context-Aware Entity Linking
Cheikh Brahim El Vaigh, François Goasdoué, Guillaume Gravier
and Pascale Sébillot
DocEng ’19, September 23–26, 2019, Berlin, Germany

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Context
Exploring large archive of a regional newspaper efficiently
iCODA1 :
Building an unified graph (RDF KB) with all data sources
Providing human friendly visualization for journalists
Bridging content and data : Linking content to the RDF
Knowledge Base
1
https://project.inria.fr/icoda/

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RDF Knowledge Bases (KB)?
Specification of RDF graphs with triples :
(s, p, o) ∈ (U ∪ B)xUx(U ∪ L ∪ B) s
p
−→ o
RDF triples for facts and knowledge
RDF fact Triple notation
Class assertion (s, τ, o)
Property assertion (s, p, o) with p ∈ {τ, subC,
subP, d, r}
RDF knowledge Triple notation
Subclass (s, subC, o)
Subproperty (s, subP, o)
Domain typing (s, d, o)
Range typing (s, r, o)

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KB example
Facts
(Q19837, τ, Person)
(Q19837, name, ”Steve Jobs”)
∗(Q19837, emplBy, Q312)
Knowledge
(Employee, subC, Person)
(emplBy, d, Employee)
(emplBy, r, Organization)
(emplBy, subP, workFor)

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Deriving the implicit triples from KB
Facts
(Q19837, τ, Person)
(Q19837, name, ”Steve Jobs”)
∗(Q19837, emplBy, Q312)
(Q19837, τ, Employee)
(Q312, τ, Organization)
∗(Q19837, workFor, Q312)
Knowledge
(Employee, subC, Person)
(emplBy, d, Employee)
(emplBy, r, Organization)
(emplBy, subP, workFor)
(workFor, r, Organization)
(workFor, d, Employee)
(workFor, d, Person)
(p, d, o), (s1, p, o1) → (s1, τ, o)
(p, r, o), (s1, p, o1) → (o1, τ, o)
(s, p1, o), (p1, subP, p) → (s, p, o)
(s, subC, o), (s1, τ, s) → (s1, τ, o)

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Standard Entity Linking pipeline
Definition (Entity Linking)
Identifying the entities of a reference knowledge base (KB) that are
mentioned in textual documents

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Introduction Collective Entity Linking Conclusion and perspectives Bibliographie
Standard Entity Linking pipeline
Definition (Entity Linking)
Identifying the entities of a reference knowledge base (KB) that are
mentioned in textual documents
Standard pipeline :
Named Entity Recognition (NER)

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Introduction Collective Entity Linking Conclusion and perspectives Bibliographie
Standard Entity Linking pipeline
Definition (Entity Linking)
Identifying the entities of a reference knowledge base (KB) that are
mentioned in textual documents
Standard pipeline :
Named Entity Recognition (NER)
Candidate Entity Generation

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Introduction Collective Entity Linking Conclusion and perspectives Bibliographie
Standard Entity Linking pipeline
Definition (Entity Linking)
Identifying the entities of a reference knowledge base (KB) that are
mentioned in textual documents
Standard pipeline :
Named Entity Recognition (NER)
Candidate Entity Generation
Candidate Entity Ranking

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Introduction Collective Entity Linking Conclusion and perspectives Bibliographie
Challenges
Mentions ambiguity :
Names variants : Jobs; Steve Jobs; Steven Paul Jobs
Indirect mentions : the CEO of Apple, Steve...
Less popular entities : Ploulec’h(Brittany)

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Limits of Entity Linking Techniques
Disregarding mentions context : entity-by-entity linking
Leveraging mostly Wikipedia : hyperlink graphs
Limited use of RDF KBs : binary indicator

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Introduction Collective Entity Linking Conclusion and perspectives Bibliographie
Collective Entity Linking
Definition (Collective Entity Linking)
Identifying all the entities of a reference knowledge base (KB) that
are mentioned in textual documents at once.
n : number of mention
mi : ith mention in the text
ei : a candidate entity for mi
(ˆe1, ..., ˆen)= arg max
e1,...,n
(
n
i=1
φ(mi |ei )
mention/entity similarity
+
n
i=1
n
j=1;j=i
ψ(ei |ej )
collective coherence score
)
CEL = φ() + ψ() + Optimisation
Optimisation = Graph search or Learning to rank

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Candidate Entities Generation + Local score
Candidate Entities Generation :
Dictionary : Cross-Wiki
cross-wiki["stevejobs"] => [[’7412236’, 0.99],[’5042765’,0.01]]
Search engine : Wikipedia search
Local score (φ) :
Cosine similarity : cosine(Vmention, Vcandidate entity)
Wikipedia popularity(mention, candidate entity)
pop(m, e) =
n(m, e)
e ∈W
n(m, e )
(1)
n(m, e) = number of time m occurs as anchor of e

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Word2vec computes φ()
Learning word representation based on its context.
Two models
Continuous Bag-of-Words (CBOW) : predicting word from
its context
Skip-Gram : predicting for a given word, its context
Example
dataset : the cat sits on the mat.
half-window : 1
CBOW :
([the,sits],cat),([cat,on],sits),([sits,the],on),([on,mat],the)
Skip-Gram :
(cat,[the,sits]),(sits,[cat,on]),(on,[sits,the]),(the,[on,mat])
Reflects semantic proximity
Computes cosine similarity

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WSRM : Weighted Semantic Relatedness Measure
Contribution : WSRM
Generic definition over RDF KBs
Take advantages of RDF KBs semantics
More relations = strong similarity
WSRM(ei , ej ) =
n(ei , ej )
e ∈E
n(ei , e )
(2)
ψ(ei , ej ) =
1
2
(WSRM(ei , ej ) + WSRM(ej , ei )) . (3)
Pre-computed from the RDF KB
Similar to Wikipedia popularity Eq.1

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WSRM computes ψ() : Example
Facts
(Q19837, τ, Person)
(Q19837, name, ”Steve Jobs”)
(Q19837, τ, Employee)
(Q312, τ, Organization)
∗(Q19837, emplBy, Q312)
∗(Q19837, workFor, Q312)
∗(Q19837, workWith, Q483382)
∗(Q483382, workFor, Q312)
Knowledges
(Employee, subC, Person)
(emplBy, d, Employee)
(emplBy, r, Organization)
(emplBy, subP, workFor)
(workFor, r, Organization)
(workFor, d, Employee)
WSRM(Q312, Q19837) = 0
WSRM(Q19837, Q312) = 2/3
WSRM(Q312, Q483382) = 0
WSRM(Q483382, Q312) = 1
ψ(Q312, Q19837) = 1/3
ψ(Q312, Q483382) = 1/2

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Local classifications : problem solved
Learning a matching mention/candidate entity (0 or 1)
Training logistic regression with 6 features : cos, pop, sum,
max@1, max@2, max@3
sum(eij ; m1, .., mn) =
n
l=1,k=i e∈C(ml )
ψ(eij , e) , (4)
maxk(eij ; m1, .., mn) =
n
max @k
l=1,j=i
max
e ∈C(ml )
ψ(ei , e) (5)
Global optimization (argmax) ⇔ local classifications
Rank with Posterior probability

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Introduction Collective Entity Linking Conclusion and perspectives Bibliographie
Datasets
AIDA [4] : entity annotated corpus of Reuters news
documents
Reuters128 [9] : Economic news articles
RSS500 [9] : RSS feeds including all major worldwide
newspapers
TAC-KBP 2016-2017 datasets [5, 6] : Newswire and
forum-discussion documents
Dataset Nb. docs Nb. mentions Avg nb. mentions/doc
TAC-KBP 2016 eval 169 9231 54.6
TAC-KBP 2017 eval 167 6915 41.4
AIDA-train 846 18519 21.9
AIDA-valid 216 4784 22.1
AIDA-test 231 4479 19.4
Reuters128 128 881 6.9
RSS-500 500 1000 2
Table: Statistics on the used datasets.

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Results (1/2) : Features Study
Features F1-score
popularity 72.3
popularity + cosine 72.9
popularity + cosine + sum 73.2
popularity + cosine + max1,2,3 75.7
popularity + cosine + sum + max1,2,3 75.9
Table: Linking accuracy (F1 score) on the TAC KBP-2017 dataset
Collective coherence improves Entity-by-Entity linking
SUM and MAX complementary
Local classification successfully aggregate local and coherence
scores

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Results (2/2) : Comparison to State-of-the-Art Approaches
Approach AIDA-A AIDA-B Reuters128 RSS-500
CNN [2] (Entity-by-Entity) - 85.5 - -
End-to-End [7] 89.4 82.4 54.6 42.2
NCEL [1] 79.0 80.0 - -
AGDISTIS [10] 57.5 57.8 68.9 54.2
Babelfy [8] 71.9 75.5 54.8 64.1
AIDA [4] 74.3 76.5 56.6 65.5
PBoH [3] 79.4 80.0 68.3 55.3
WSRM 90.6 87.7 79.9 79.3
Table: Micro-averaged F1 score for different methods on the four
datasets
WSRM stable for different datasets

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Conclusion and perspectives
CEL without Wikipedia
Improving CEL with WSRM = Binary indicator
Opening the door to semantic reasoning : paths of size
m, m > 1

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Introduction Collective Entity Linking Conclusion and perspectives Bibliographie
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Introduction Collective Entity Linking Conclusion and perspectives Bibliographie
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Introduction Collective Entity Linking Conclusion and perspectives Bibliographie
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