1. Enriching Structured Knowledge with Open Information

2. Outline Introduction Related Work Overview Clustering & Mapping
Experiments

3. Introduction State-0f-the-art IE systems Differences Focus
NELL, REVERB, OLLIE, … (OIE)
YAGO, DBPEDIA, FREEBASE, … (KB)
Differences
OIE: no fixed schemata, unstructured
KB: assertions, URI, ontology
Focus
Mapping(OIE -> ontology)
Results: precise & unambiguous assertions

4. Introduction Scenario Example Domain unlimited
Applicable to NL: rel(s, o)
Example
REVERB fact input: is a town in (Croydon, London)
Target KB: DBPEDIA
Mappings
is a town in - > dbo:country
Croydon -> db:Croydon, London -> db:London
Assertion output: dbo:county(db:Croydon, db:London)

5. Introduction Proplems Strategy Contributions Polysemous, Ambiguity
Multi-references
Strategy
Relation phrases clustering
Contributions
Modularised mapping workflow: OIE -> KB
Markov clustering
Feedback: improve overall results’ quality

6. Related Work Matching Instances
Knowledge Base Constructions & Debugging
Distant Supervision based Approaches
Semantifying Open Information

7. Overview
Framework

8. Overview Modules instance matching (IM) look up (LU) clustering (CL)
property mapping (PM)

9. Module Description IM Input: OIE facts
Output: mapping[s & o terms -> Dbpedia entities]
Working mechanism(Disambiguation)
OIE Instances
Possible referred entities(in KB Dbpedia)
Candidate matching: probabilistic ranking based on KB relation pattern (domain/range)
Filtering(MAP state)

10. Module Description LU Process Function: search for facts in target KB
Input: set of instance mappings from IM
Process
OIE fact f, subject x, object y
Search for KB assertions relating x & y
Judge
f+: facts with KB assertions(as PM mapping evidences)
f-: facts without KB assertions(translated to Dbpedia vocabulary, KB extension)

11. Module Description CL Input: OIE facts 3 different clusters Output
wf1: Trivial, a relational phrase=one element cluster
wf2: Non-trivial, without Dbpedia seeds(properties)
wf3: Non-trivial, with Dbpedia seeds
Output
wf1: clusters of similar relational phrases
wf2,wf3: clusters(forward to IM)

12. Module Description PM
Aim: map[relation phrase (OIE properties)-> object property(KB properties)]
Mechanism
Association rule(frequent rule pattern) mining
rel -> (domain, range)
Input: f+
Evidences for association rules formation
Evidences for possible mapping
Output: set of property mappings

13. Clustering & Mapping Similarity Metrics jac(), jaccard similarity
wn(), WordNet similarity
β, weighing factor, β∈[0, 1]

14. Clustering & Mapping Markov Clustering Node: rel-phrase
Edge: affinity score
Transition probability
Mechanism
Random walk(markov)
Iterate to steady state
probability distribution
Strong link stronger
Weak link weaker

15. Clustering & Mapping Markov Clustering Inflation Choice of I
Parameter inflation factor, I
Choice of I
Set too small, cluster coarse, vice fersa
Reasonable I, some final cluster had sub-clusters

16. Clustering & Mapping PM Strategies Pairwise similarity
wf1-REVERB rel-phrases
one element cluster, map
to Dbpedia property
wf2-extension of wf1,
cluster REVERB rel-ph
wf3-add Dbpedia properties
as seeds clustered with
REVERB rel-phrases, use
markov clustering
Pairwise similarity

17. Experiments Dataset REVERB ClubWeb Extractions
confidence score >= 95%, remove facts with numeric expressions: 3.5 million triples with rel-phs
500 most frequent REVERB properties
100 most frequent Dbpedia properties

18. Experiments-Evaluation
Metric
S: cluster score
comp(ci) : intra-cluster sparseness
iso(C): inter-cluster sparseness
The comp(ci) higher, iso(C) lower, the better.

19. Experiments-Evaluation
Analysis
Control
parameter

20. Experiments-Evaluation

21. THANKS!