1. An Empirical Study of Property Collocation on Large Scale of Knowledge Base
龚赛赛

2. Contents
Introduction
Survey
Measure

3. Introduction
In the semantic web, properties are used to describe various entities
Like word collocation in natural language, collocation also exists among these properties
Property collocation: a combination of properties that happens very often and more frequently than would happen by chance

4. Example Wikidata browser Freebase browser
Property collocation is natural and common in real usage !

5. Introduction Various applications
Entity browsing
Vocabulary search and recommendation
Ontology analysis and assessment …
Previous work lacks comprehensive and dedicated investigation in property collocation
Equivalent properties
General relatedness of concepts or vocabularies
In this paper, we present an empirical study of property collocation in large scale knowledge base

6. Survey
Q1: Whether property collocation in the ontology exists and how common it is.
Q2: How people agree on property collocation

7. Survey
31 users
Setup: select 10 popular classes for Dbpedia, Wikidata and Freebase resp. Each user randomly browse 2 entities of each class and identify collocated properties

8. Survey Basic statistics Dbpedia Wikidata Freebase Entity Num 321 316
315
Covered property Num
18.2%, 513 in 2819
21.7%, 461 in 2128
12.1%, 810 in 6679
Covered property num with direction
832
694
1541
Group num
713
440
644

9. Cumulative percentage of properties for Q1
Dbpedia
Wikidata
Freebase

10. Cumulative number of groups for Q2
Dbpedia 11.7% for 3+
Wikidata 8% for 3+
Dbpedia: 11.7%, wikidata 8% , Freebase : 10%
Freebase 10% for 3+

11. Cumulative number of collocated pairs for Q2
Wikidata 4758 pairs
Dbpeida 6265 pairs
Freebase 5907 pairs

12. Measure 1 Statistical association
P(pi): the probability that a resource is described by pi in some context
Phi Coefficient
Symmetrical Uncertainty Coefficient
Jaccard Coefficient

13. Measure 2 Semantic collocation Domain, range, property hierarchy
dmin/rmin : minimal classes in domain/range
SetSimd, SetSimr: similarity of minimal class set
HRel: relatedness based on property hierarchy , shortest path

14. Measure 3 Lexical Similarity I-sub JaroWinkler Levenshtein similarity
Wordnet

15. Measure evaluation
Sort collocated property pairs based on the number of users
Sort these property pairs based on different measure values
Compute Spearman's rank correlation coefficient