• Ranking patterns
– size – random walk measure (relatedness) – count (frequency) – rarity (specificity)
Espresso [Seufert 2016]
System
Input
Relationshi p
• the properties; other objects with same properties; relationships contain the selected node
– more info
RECAP [Pirro 2015]
• Object mapping
– auto-completion
• simple paths
– RelFinder, RECAP, Explass, connect-dots
• subgraphs
– minimal subgraph patterns
• REX
– relatedness cores (cliques)
• Espresso
Key Challenges
• relatedness?
– coherence
• Visualization & Exploration
– – – – aggregated vs. detailed view temporal analysis topic distribution facet-values
• Evaluation
知识图谱的可视化交互分析和 关系发现
王秋月 中国人民大学信息学院 2017-1-5
杜治娟
王硕
郝泽慧
李进
张祎
郭豫龙
Ongoing Work
• Visualization and interactive analysis of large knowledge graphs • Relationship discovery in KB • Question answering over KB
– Data mining – Web search – Semantic Web
What is the relationship between Lucy and Arthur in Bram Stoker’s Dracula?
More Examples
• Which European politicians are related to politicians in the United States, and how? • What are the relationships between China and countries from the Middle East over the last few years?
RECAP Explass
2 entity 2 entity
simple paths simple paths
informativeness; diversity length; frequency; informativeness; overlap specificity; connectivity; cohesiveness size; random walk; count; rarity path pattern
– product of all the above three scores
REX [Fang 2012]
• minimal subgraph patterns
– essential & non-decomposable – enumerate path patterns; combine path patterns
• Finding paths
– SPARQL
• Ranking paths
– path informativeness – pattern informativeness – path divemerging paths
– all paths; top-m paths; top-m patterns; + diversity …
• relationship extraction • relationship prediction
• context aware ranking
– domain specific degree-of-interest
Demo
• ScholarExplorer
/scholarexplorer/
• Interaction
– more info about selected nodes and edges
Ranking Criteria
• Path informativeness (predicate frequency inverse triple frequency)
•
Pattern informativeness
• Connectivity
– edge strength = relatedness of two entities
• Cohesiveness
– strength of linkages between adjacent edges in the path
• Overall score of a path
Relationship Discovery
• Find relationships between two entities
– What is the relationship between PKU and RUC?
• Application areas such as National Security, Intelligence Services, Bioinformatics • Study from different communities
• Large volume of indirect relationships • How to efficiently find paths? • How to help users explore the large set of paths?
– Filtering
• path lengths; predicates; classes
Y
Summary
• Ranking
– importance
• specificity? (specificity of predicate, entity, class, pattern) • popularity? (entity popularity, pattern frequency)
– relevance
Ranking Criteria
• • frequency informativeness
•
overlap
– ontological overlap – contextual overlap
Connecting the Dots [Aggarwal 2016]
• Specificity
– entity specificity = inverse of total count of its neighbors
• Relationship search
– SPARQL
• Relationship ranking
– path length
• Visulization & Interaction
– overview: aggregated on path length, connectivity levels, predicates, classes – detailed view: force-directed graph – highlight
• BioExplorer
http://202.112.113.252:8080/bioweb2016/paper.html
谢谢！
•
Path diversity
Explass [Cheng 2014]
• Object mapping
– auto-completion
• Find paths • Mine path patterns
– frequent itemset mining
• Rank path patterns
– – – – length frequency Informativeness overlap
Input
• two entities
– REX, Explass, RECAP, connect-dots
• n entities
– RelFinder
• two entity sets
– Espresso
• one entity, one entity set
Definition of Relationship
Y
connect dots REX
2 entity
simple paths
2 entity
minimal subgraph patterns relatedness cores classes; time; location;
Espresso
2 entity sets
relatedness; prior probability
simple paths
Filtering
path length; connectivity; predicates; classes predicates predicates; classes
Ranking
path length
Clustering Graph
Y
RelFinder n entity