SlideShare a Scribd company logo

Summarizing Semantic Data

Download as PPTX, PDF
Gong Cheng, profile picture
Gong Cheng

This document discusses summarizing semantic data, including entity descriptions, entity associations, and semantic datasets. It describes extractive and abstractive summarization methods. For entity descriptions, intrinsic metrics like frequency, centrality, informativeness, and diversity are used to rank property-value pairs for the summary. Extrinsic metrics also utilize external knowledge and context. Similar methods are applied to summarizing entity associations by ranking paths between entities. Summarizing semantic datasets involves selecting a representative subset of the data.

Science
Related topics:
Semantic Web
1 of 84
Downloaded 84 times
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
Summarizing Semantic Data Gong Cheng National Key Laboratory for Novel Software Technology Nanjing University, China Websoft
What is semantic data? • Entity • Class • Property • Attribute • Relation
What is semantic data? • Entity • Class • Property • Attribute • Relation Datasets
Semantic datasets on the Web
What is semantic data summarization? Why? 1. Summarizing entity descriptions (a.k.a. entity summarization)
What is semantic data summarization? Why? 2. Summarizing entity associations Alice Bob article-A paper-A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor
What is semantic data summarization? Why? 3. Summarizing semantic datasets
Two types of summaries • Extractive methods • summary = a subset of data • summarization = ranking and selection • Abstractive methods (a.k.a. non-extractive methods) • summary = a high-level abstraction of data • summarization = a more complex process
Summarizing Semantic Data
Outline of this talk • Summarizing entity descriptions • Summarizing entity associations • Summarizing semantic datasets • Summarizing ontologies (if time permits)
Outline of this talk • Summarizing entity descriptions • Summarizing entity associations • Summarizing semantic datasets
Summarizing entity descriptions • Extractive methods (summary = a subset of property-value pairs) • Metrics for ranking property-value pairs • Intrinsic metrics • Extrinsic metrics • Structures for combining metrics • Abstractive methods • Not known yet Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman …
Intrinsic metrics 1. Frequency 2. Centrality 3. Informativeness 4. Diversity
Intrinsic metrics (1): frequency • Frequency of property • Frequency in the dataset • Frequency among entities of the same type • Frequency in this entity description • Frequency in the ontology (i.e., richness of definition) Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman … Property Value … influenced … … Property Value … type Artist creates … …
Intrinsic metrics (1): frequency • Frequency of property value • Frequency in the dataset (note: entities in text) • Frequency in this entity description (note: indirect relations) Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman … Property Value … … Mona Lisa … … Lady with an Ermine … Property Value … … …Mona Lisa… … Indirect relations may also be counted.
Intrinsic metrics (1): frequency • Frequency of property-value pair • Frequency among similar entities • Frequency in the dataset (why not?) Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman … Property Value … type Artist … influenced Richard Feynman … (a similar entity)
Intrinsic metrics (2): centrality • Centrality of property value • Within the dataset: (weighted) PageRank • On the Web: authority of datasets referencing it Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman …
Intrinsic metrics (2): centrality • Centrality of property-value pair • PageRank, weighted by inverse Google distance[Cheng et al., ISWC’11] Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman … name: Leonardo da Vinci type: Person creates: Mona Lisa …
Intrinsic metrics (3): informativeness • Informativeness of property-value pair • Self-information of property-value pair[Cheng et al., ISWC’11] • Depth of class Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman … Property Value … type Person type Scientist … Person Artist Scientist
Intrinsic metrics (4): diversity • Diversity of properties • To avoid common properties • To avoid properties having similar values Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman …
Intrinsic metrics (4): diversity • Diversity of property-value pairs[Cheng et al., JoWS’15, WWW’15] • Similarity between text: string-based, word-based • Similarity between numbers • Semantic similarity: reasoning-based Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman … Person Artist Scientist type:Artist ⇒ type:Person
Extrinsic metrics 1. Using external knowledge 2. Context-based
Extrinsic metrics (1): using external knowledge • Using domain knowledge • Certain properties are known to be important. • Using indicators on the Web • Search engine hits • Bidirectional links in Wikipedia • Using user feedback • User clicks Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman …
Extrinsic metrics (2): context-based • Entity search results • context = query • solution: query relevance [Cheng et al., IJSWIS’09]
Extrinsic metrics (2): context-based • Entities in a document • context = contents of the document • solution: Class Vector Model[Cheng et al., WWW’15] Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman … vector = {Painting} … The Starry Night, from MoMA’s collection, reminds us of some work painted by Leonardo da Vinci. ... Property Value … type Painting … vector(context) = {Painting} vector = {Artist}
Extrinsic metrics (2): context-based • Co-summarization • context = other entities • solution: • difference from other entities[Cheng et al., WWW’15] (for entity linking) • similarity with other entities[Cheng et al., JoWS’15] (for entity coreference resolution)
Structures for combining metrics 1. Result combination 5 1 3 2 4 5 2 4 1 3 5 1 2 4 3 Ranked by Metric A Ranked by Metric B Ranked by Metric C Summary
Structures for combining metrics 1. Result combination (cont.) Ranked by Metric A Ties broken by Metric B
Structures for combining metrics 2. Arithmetic combination ɑ*MetricA + β*MetricB
Structures for combining metrics • e.g., combinatorial optimization • Quadratic Knapsack Problem[Cheng et al., JoWS’15] • Quadratic Multidimensional Knapsack Problem[Cheng et al., WWW’15] Length constraint Similarity with and difference from other entities Inverse similarity Diagonal: informativeness One entity The other entity Inverse similarity
Structures for combining metrics • e.g., weighted PageRank[Cheng et al., ISWC’11] Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman … name: Leonardo da Vinci type: Person creates: Mona Lisa … Probability of jumpingProbability of following edges Inverse Google distance Informativeness
Structures for combining metrics 3. Machine Learning • Decision trees • Linear regression
Structures for combining metrics 4. Complex combinations • Result combination + arithmetic combination • Machine learning + arithmetic combination
Summarizing Semantic Data
Outline of this talk • Summarizing entity descriptions • Summarizing entity associations • Summarizing semantic datasets
Summarizing entity associations • Extractive methods • Finding and ranking associations between two entities (summary = a subset of paths) • Path finding and filtering • Intrinsic and extrinsic metrics for ranking paths • Structures for combining metrics • Finding and ranking associations between multiple entities (summary = a subset of subgraphs) • Abstractive methods • Ranking association patterns • Hierarchically organizing association patterns Alice Bob article-A paper-A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor
Finding associations between two entities • Path finding • Dijkstra or A* • Bidirectional breadth-first search (bi-BFS) • Schema-based performance optimization Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor Paper Person Conference inProcOf cites, extends O(Δd)  O(Δd/2)
Finding associations between two entities • Path filtering • By length • By entities, classes, relations • By keywords Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor
Ranking associations between two entities • Intrinsic metrics • Frequency • Centrality • Informativeness • Diversity • Length • Conformity • Extrinsic metrics • Using external knowledge • Context-based • Structures for combining metrics
Intrinsic metrics: frequency, centrality, diversity, length • Property frequency • Degree centrality • Diverse relations • Length Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor
Intrinsic metrics: informativeness • Informativeness • Data-based informativeness: inverse relation frequency • Schema-based informativeness: depth of class/relation Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor
Intrinsic metrics: conformity • Conformity to schema Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor Paper Person Conference inProcOf cites, extends
Extrinsic metrics • Using external knowledge • Explicit: user-defined weights • Implicit: user’s Web browsing history • Context-based • Query relevance Alice Bob article-A paper-A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor
Finding and ranking associations between multiple entities • association = a size-constrained connected subgraph (size = number of other entities) 3 associations via 2 other entities
Finding and ranking associations between multiple entities • association = a size-constrained connected subgraph (size = diameter)[Cheng et al., ISWC’16] 3 associations having a diameter of 3
Finding and ranking associations between multiple entities • Subgraph finding • n-directional breadth-first search • Distance-based performance optimization[Cheng et al., ISWC’16]
Finding and ranking associations between multiple entities • Subgraph ranking (based on entity ranking) • PageRank • Query relevance • Number of short paths • Random walk with restart
Finding and ranking associations between multiple entities • association = a Steiner tree (size-unconstrained, weight-minimized)
Abstractive methods • Association pattern[Cheng et al., ISWC’14] paper-A conf-A inProcOfsecondAuthor reviewer paper-B conf-B inProcOffirstAuthor chair Paper Conference inProcOfauthor role Patterns Associations
Abstractive methods • Association pattern[Cheng et al., ISWC’16] Patterns Associations
Ranking association patterns • Metrics • Frequency • Informativeness • Diversity • Structures for combining metrics Paper Conference inProcOfauthor role
Metrics: frequency • frequency = occurrences of canonical code[Cheng et al., ISWC’16] = isomorphic? eq 1r1C1r2C2r3eq 2$r4eq 3$$$$ (when T=e)
Metrics: frequency • frequency = occurrences of canonical code[Cheng et al., ISWC’16] ? Solution: using query entities as proxies for classes to be ordered
Hierarchically organizing association patterns • subClassOf/subPropertyOf  subPatternOf[Zhang et al., JIST’13] Paper Conference inProcOfauthor role Demo Conference inProcOfauthor reviewer Poster Conference inProcOfauthor chair
Summarizing Semantic Data
Outline of this talk • Summarizing entity descriptions • Summarizing entity associations • Summarizing semantic datasets
Summarizing semantic datasets • Extractive methods (summary = a subset of triples) • Centrality • Abstractive methods 1. Inferred schema 2. Flat partitioning 3. Hierarchical grouping
Extractive methods • Triple ranking (based on entity ranking) • Centrality: degree, PageRank Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor
Abstractive methods (1): inferred schema • summary = a graph-structured (sub-)schema inferred from data (grouping entities by classes) Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor Paper Person Conference inProcOf cites, extends
Abstractive methods (1): inferred schema • Metrics for ranking classes and properties • Frequency • Centrality Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor Paper Person Conference inProcOf cites, extends
Abstractive methods (2): flat partitioning • summary = entity partitions connected by relations • partitioning by shared classes (= inferred schema) • partitioning by shared attributes • partitioning by shared paths (a.k.a. bisimulation) Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor Paper Person Conference inProcOf cites, extends
Abstractive methods (3): hierarchical grouping[Cheng et al., IJCAI’16] • summary = a hierarchical grouping of entities • identified by property-value pairs • connected by relations A hierarchical grouping of entities Relations connecting sibling groups
• Metrics for choosing groups (i.e., property-value pairs) • Coverage of data  large subgroups • Height of hierarchy  moderate-sized subgroups • Cohesion within groups  informative property-value pairs • Overlap between groups  controllable overlap • Homogeneity of groups  different values of the same property A hierarchical grouping of entities Relations connecting sibling groups Abstractive methods (3): hierarchical grouping[Cheng et al., IJCAI’16]
• Combining metrics by combinatorial optimization (formulated as a multidimensional knapsack problem) maximizing moderateness of each subgroup maximizing cohesion within each subgroup disallowing large overlap between subgroups selecting ≤k subgroups (optionally) disallowing different properties Abstractive methods (3): hierarchical grouping[Cheng et al., IJCAI’16]
Concluding remarks • Research • More application scenarios are to be identified. • New applications may promote new metrics. • More benchmarks are needed for evaluation. • Practice • Handy tools for semantic data summarization are missing. The 2016 ENtity Summarization Evaluation Campaign (ENSEC 2016) http://km.aifb.kit.edu/ws/sumpre2016/challenge.html
Papers on summarizing entity descriptions • Gong Cheng, Danyun Xu, Yuzhong Qu. Summarizing Entity Descriptions for Effective and Efficient Human-centered Entity Linking. (WWW'15) • Gong Cheng, Danyun Xu, Yuzhong Qu. C3D+P: A Summarization Method for Interactive Entity Resolution. (JoWS’15) • Gong Cheng, Thanh Tran, Yuzhong Qu. RELIN: Relatedness and Informativeness-based Centrality for Entity Summarization. (ISWC’11) • Gong Cheng, Yuzhong Qu. Searching Linked Objects with Falcons: Approach, Implementation and Evaluation. (IJSWIS’09)
Papers on summarizing entity associations • Gong Cheng, Daxin Liu, Yuzhong Qu. Efficient Algorithms for Association Finding and Frequent Association Pattern Mining. (ISWC'16) • Gong Cheng, Yanan Zhang, Yuzhong Qu. Explass: Exploring Associations between Entities via Top-K Ontological Patterns and Facets. (ISWC’14) • Yanan Zhang, Gong Cheng, Yuzhong Qu. Towards Exploratory Relationship Search: A Clustering-based Approach (JIST’13)
Papers on summarizing semantic datasets • Gong Cheng, Cheng Jin, Yuzhong Qu. HIEDS: A Generic and Efficient Approach to Hierarchical Dataset Summarization. (IJCAI’16)
Summarizing Semantic Data
Ontology • Terms • Publication • Paper • Conference • title • inProc • Term descriptions • SubClassOf(Paper, Publication) • SubClassOf(Paper, DataExactCardinality(1, title)) • ObjectPropertyDomain(inProc, Paper) • ObjectPropertyRange(inProc, Conference)
Summarizing ontologies: an application
Summarizing ontologies • Extractive methods 1. Ranking terms (summary = a subset of terms) 2. Ranking term descriptions (summary = a subset of term descriptions) 3. Ranking subgraphs (summary = a subgraph) • Abstractive methods • Not known yet
Extractive methods (1): ranking terms • Intrinsic metrics 1. Frequency 2. Centrality 3. Diversity 4. Simplicity • Extrinsic metrics 1. Using external knowledge 2. Context-based
Intrinsic metrics (1): frequency • Schema-based frequency • Data-based frequency
Intrinsic metrics (2): centrality • Middleness in the hierarchy • Degree • Betweenness • PageRank Paper Publication title inProc Conference Publication Paper Book Article Poster
Intrinsic metrics (3): diversity • Coverage of hierarchy Publication Paper Book Article Poster
Intrinsic metrics (4): simplicity • Number of words in the name of a term Paper vs. PaperPublishedAtCCKS2016
Extrinsic metrics • Using external knowledge • Search engine hits • Personalization (e.g., spreading activation) • Context-based • Query relevance Paper Publication title inProc Conference
Extractive methods (2): ranking term descriptions • Graph representation of term descriptions 1. Description graph 2. Term-description graph • Ranking term descriptions • Intrinsic metrics • Extrinsic metrics
Graph representation (1): description graph [Zhang et al., WWW’07] SubClassOf(Paper, Publication) SubClassOf(Paper, DataExactCardinality(1, title)) ObjectPropertyDomain(inProc, Paper) ObjectPropertyRange(inProc, Conference) SubClassOf(Paper, Publication) SubClassOf(Paper, DataExactCardinality(1, title)) ObjectPropertyDomain(inProc, Paper) ObjectPropertyRange(inProc, Conference)
Graph representation (2): term-description graph [Zhang et al., JCST’09; Cheng et al., JIST’11] SubClassOf(Paper, Publication) SubClassOf(Paper, DataExactCardinality(1, title)) ObjectPropertyDomain(inProc, Paper) ObjectPropertyRange(inProc, Conference) SubClassOf(Paper, Publication) SubClassOf(Paper, DataExactCardinality(1, title)) ObjectPropertyDomain(inProc, Paper) ObjectPropertyRange(inProc, Conference) Paper Publication title inProc Conference
Ranking term descriptions • Intrinsic metrics • Frequency • Centrality • Diversity • Cohesion/coherence • Extrinsic metrics • Query relevance SubClassOf(Paper, Publication) SubClassOf(Paper, DataExactCardinality(1, title)) ObjectPropertyDomain(inProc, Paper) ObjectPropertyRange(inProc, Conference)
Papers on summarizing ontologies • Weiyi Ge, Gong Cheng, Huiying Li, Yuzhong Qu. Incorporating Compactness to Generate Term-association View Snippets for Ontology Search. (IP&M’13) • Gong Cheng, Feng Ji, Shengmei Luo, Weiyi Ge, Yuzhong Qu. BipRank: Ranking and Summarizing RDF Vocabulary Descriptions. (JIST’11) • Xiang Zhang, Gong Cheng, Weiyi Ge, Yuzhong Qu. Summarizing Vocabularies in the Global Semantic Web. (JCST’09) • Xiang Zhang, Gong Cheng, Yuzhong Qu. Ontology Summarization Based on RDF Sentence Graph. (WWW’07)
Summarizing Semantic Data

More Related Content

PDF
Open Web Data for Education - Linked Data technologies for connecting open ed...
Mathieu d'Aquin
 
PDF
The Future of Value
Euro RSCG Worldwide
 
PPTX
Semantics T1
Ameliya Lee
 
PPSX
INTRODUCTION TO SEMANTICS- by Prof. Alyson Andrade at Regional University fr...
Alyson Andrade
 
PPTX
The Brief of SEMANTICS
Safitri Indah
 
PPTX
Group presentation
Charles Katey Adabah
 
PPT
semantics the study of meaning
Yoshinta Debbi A
 
PPTX
semantics
Ria Sari
 
Open Web Data for Education - Linked Data technologies for connecting open ed...
Mathieu d'Aquin
 
The Future of Value
Euro RSCG Worldwide
 
Semantics T1
Ameliya Lee
 
INTRODUCTION TO SEMANTICS- by Prof. Alyson Andrade at Regional University fr...
Alyson Andrade
 
The Brief of SEMANTICS
Safitri Indah
 
Group presentation
Charles Katey Adabah
 
semantics the study of meaning
Yoshinta Debbi A
 
semantics
Ria Sari
 

Similar to Summarizing Semantic Data (20)

PPTX
Mining Web content for Enhanced Search
Roi Blanco
 
PDF
Link analysis for web search
Emrullah Delibas
 
PPTX
DIY ERM (Do-It-Yourself Electronic Resources Management) for the Small Library
NASIG
 
PPTX
Graphinder semantic search
Thanh Tran
 
PPTX
Neo4j Training Introduction
Max De Marzi
 
PPTX
Semantic Data Retrieval: Search, Ranking, and Summarization
Gong Cheng
 
PPTX
Introduction to Information Architecture & Design - SVA Workshop 02/15/14
Robert Stribley
 
PPTX
Discovering knowledge using web structure mining
Atul Khanna
 
PPTX
information architecture presentation basics
FazliAzeem3
 
PPTX
Introduction to Information Architecture & Design - SVA Workshop 03/22/14
Robert Stribley
 
ODP
FOSDEM2014 - Social Network Benchmark (SNB) Graph Generator - Peter Boncz
Ioan Toma
 
PDF
Oke
Andrea Nuzzolese
 
PDF
Extending Schema.org
Richard Wallis
 
PPTX
PhD Defense
Roman Prokofyev
 
PDF
Entity Search on Virtual Documents Created with Graph Embeddings
Sease
 
PDF
Webinar 11-13-14 - DIY E-Resources Management: Basics of Information Architec...
NASIG
 
PPTX
Introduction to Information Architecture & Design - 12/06/14
Robert Stribley
 
PDF
week1 - What_Is_A_Knowledge_Graphs_S.pdf
moisesmarrary2
 
PPTX
Digital Medieval Commons
blalbritton
 
PPTX
Sylva workshop.gt that camp.2012
CameliaN
 
Mining Web content for Enhanced Search
Roi Blanco
 
Link analysis for web search
Emrullah Delibas
 
DIY ERM (Do-It-Yourself Electronic Resources Management) for the Small Library
NASIG
 
Graphinder semantic search
Thanh Tran
 
Neo4j Training Introduction
Max De Marzi
 
Semantic Data Retrieval: Search, Ranking, and Summarization
Gong Cheng
 
Introduction to Information Architecture & Design - SVA Workshop 02/15/14
Robert Stribley
 
Discovering knowledge using web structure mining
Atul Khanna
 
information architecture presentation basics
FazliAzeem3
 
Introduction to Information Architecture & Design - SVA Workshop 03/22/14
Robert Stribley
 
FOSDEM2014 - Social Network Benchmark (SNB) Graph Generator - Peter Boncz
Ioan Toma
 
Oke
Andrea Nuzzolese
 
Extending Schema.org
Richard Wallis
 
PhD Defense
Roman Prokofyev
 
Entity Search on Virtual Documents Created with Graph Embeddings
Sease
 
Webinar 11-13-14 - DIY E-Resources Management: Basics of Information Architec...
NASIG
 
Introduction to Information Architecture & Design - 12/06/14
Robert Stribley
 
week1 - What_Is_A_Knowledge_Graphs_S.pdf
moisesmarrary2
 
Digital Medieval Commons
blalbritton
 
Sylva workshop.gt that camp.2012
CameliaN
 
Ad

More from Gong Cheng (20)

PPTX
Towards Content-Based Dataset Search - Test Collections and Beyond
Gong Cheng
 
PPTX
从元数据到内容——新一代知识图谱搜索引擎初探
Gong Cheng
 
PPTX
知识图谱中的实体摘要：基于神经网络的方法
Gong Cheng
 
PPTX
Generating Compact and Relaxable Answers to Keyword Queries over Knowledge Gr...
Gong Cheng
 
PPTX
知识图谱中的关联搜索
Gong Cheng
 
PPTX
面向高考机器人的知识表示与推理初探
Gong Cheng
 
PPTX
知识图谱中的实体关联搜索
Gong Cheng
 
PPTX
Semantic Web related top conference review
Gong Cheng
 
PPTX
Relatedness-based Multi-Entity Summarization
Gong Cheng
 
PPTX
Generating Illustrative Snippets for Open Data on the Web
Gong Cheng
 
PPTX
常识推理在地理自动答题中的需求分析
Gong Cheng
 
PPTX
Efficient Algorithms for Association Finding and Frequent Association Pattern...
Gong Cheng
 
PPTX
HIEDS: A Generic and Efficient Approach to Hierarchical Dataset Summarization
Gong Cheng
 
PPTX
Taking up the Gaokao Challenge: An Information Retrieval Approach
Gong Cheng
 
PPTX
Summarizing Entity Descriptions for Effective and Efficient Human-centered En...
Gong Cheng
 
PPTX
知识的摘要
Gong Cheng
 
PPTX
Explass: Exploring Associations between Entities via Top-K Ontological Patter...
Gong Cheng
 
PPTX
Facilitating Human Intervention in Coreference Resolution with Comparative En...
Gong Cheng
 
PPTX
Towards Exploratory Relationship Search: A Clustering-based Approach
Gong Cheng
 
PPT
NJVR: The NanJing Vocabulary Repository
Gong Cheng
 
Towards Content-Based Dataset Search - Test Collections and Beyond
Gong Cheng
 
从元数据到内容——新一代知识图谱搜索引擎初探
Gong Cheng
 
知识图谱中的实体摘要：基于神经网络的方法
Gong Cheng
 
Generating Compact and Relaxable Answers to Keyword Queries over Knowledge Gr...
Gong Cheng
 
知识图谱中的关联搜索
Gong Cheng
 
面向高考机器人的知识表示与推理初探
Gong Cheng
 
知识图谱中的实体关联搜索
Gong Cheng
 
Semantic Web related top conference review
Gong Cheng
 
Relatedness-based Multi-Entity Summarization
Gong Cheng
 
Generating Illustrative Snippets for Open Data on the Web
Gong Cheng
 
常识推理在地理自动答题中的需求分析
Gong Cheng
 
Efficient Algorithms for Association Finding and Frequent Association Pattern...
Gong Cheng
 
HIEDS: A Generic and Efficient Approach to Hierarchical Dataset Summarization
Gong Cheng
 
Taking up the Gaokao Challenge: An Information Retrieval Approach
Gong Cheng
 
Summarizing Entity Descriptions for Effective and Efficient Human-centered En...
Gong Cheng
 
知识的摘要
Gong Cheng
 
Explass: Exploring Associations between Entities via Top-K Ontological Patter...
Gong Cheng
 
Facilitating Human Intervention in Coreference Resolution with Comparative En...
Gong Cheng
 
Towards Exploratory Relationship Search: A Clustering-based Approach
Gong Cheng
 
NJVR: The NanJing Vocabulary Repository
Gong Cheng
 
Ad

Recently uploaded (20)

PPTX
Introduction to Cardiovascular system_structure and functions-1
muralinath2
 
PPTX
TOTAL hIP ARTHROPLASTY Presentation.pptx
hararghemssc
 
PPTX
neck nodes and dissection types and lymph nodes levels
DipuSarma
 
PPT
The World of Physical Science, • Labs: Safety Simulation, Measurement Practice
joybejerano
 
PPT
protein biochemistry.ppt for university classes
bayewodajo27
 
PDF
The scientific heritage No 166 (166) (2025)
The scientific heritage
 
PDF
lecture 2026 of Sjogren's syndrome l .pdf
ssuserf13226
 
PDF
Formation of Supersonic Turbulence in the Primordial Star-forming Cloud
Sérgio Sacani
 
PPTX
DRUG THERAPY FOR SHOCK gjjjgfhhhhh.pptx.
vipvivek704
 
PDF
Mastering Bioreactors and Media Sterilization: A Complete Guide to Sterile Fe...
Sabina Sharma
 
PDF
ELS_Q1_Module-11_Formation-of-Rock-Layers_v2.pdf
savannaesar07
 
PPTX
G5Q1W8 PPT SCIENCE.pptx 2025-2026 GRADE 5
Rodel Gordzkie
 
PDF
Biophysics 2.pdffffffffffffffffffffffffff
MiraMusleh
 
PDF
SEHH2274 Organic Chemistry Notes 1 Structure and Bonding.pdf
ivan1108lau
 
PPT
POSITIONING IN OPERATION THEATRE ROOM.ppt
PrabakaranMP1
 
PPTX
Protein & Amino Acid Structures Levels of protein structure (primary, seconda...
Muhammad Sajid Afridi
 
PPTX
cpcsea ppt.pptxssssssssssssssjjdjdndndddd
amolpawasesng123
 
PPTX
ognitive-behavioral therapy, mindfulness-based approaches, coping skills trai...
OliveJolly1
 
PPTX
famous lake in india and its disturibution and importance
judesebina
 
PPTX
2Systematics of Living Organisms t-.pptx
RachanaT6
 
Introduction to Cardiovascular system_structure and functions-1
muralinath2
 
TOTAL hIP ARTHROPLASTY Presentation.pptx
hararghemssc
 
neck nodes and dissection types and lymph nodes levels
DipuSarma
 
The World of Physical Science, • Labs: Safety Simulation, Measurement Practice
joybejerano
 
protein biochemistry.ppt for university classes
bayewodajo27
 
The scientific heritage No 166 (166) (2025)
The scientific heritage
 
lecture 2026 of Sjogren's syndrome l .pdf
ssuserf13226
 
Formation of Supersonic Turbulence in the Primordial Star-forming Cloud
Sérgio Sacani
 
DRUG THERAPY FOR SHOCK gjjjgfhhhhh.pptx.
vipvivek704
 
Mastering Bioreactors and Media Sterilization: A Complete Guide to Sterile Fe...
Sabina Sharma
 
ELS_Q1_Module-11_Formation-of-Rock-Layers_v2.pdf
savannaesar07
 
G5Q1W8 PPT SCIENCE.pptx 2025-2026 GRADE 5
Rodel Gordzkie
 
Biophysics 2.pdffffffffffffffffffffffffff
MiraMusleh
 
SEHH2274 Organic Chemistry Notes 1 Structure and Bonding.pdf
ivan1108lau
 
POSITIONING IN OPERATION THEATRE ROOM.ppt
PrabakaranMP1
 
Protein & Amino Acid Structures Levels of protein structure (primary, seconda...
Muhammad Sajid Afridi
 
cpcsea ppt.pptxssssssssssssssjjdjdndndddd
amolpawasesng123
 
ognitive-behavioral therapy, mindfulness-based approaches, coping skills trai...
OliveJolly1
 
famous lake in india and its disturibution and importance
judesebina
 
2Systematics of Living Organisms t-.pptx
RachanaT6
 

Summarizing Semantic Data

  • 1. Summarizing Semantic Data Gong Cheng National Key Laboratory for Novel Software Technology Nanjing University, China Websoft
  • 2. What is semantic data? • Entity • Class • Property • Attribute • Relation
  • 3. What is semantic data? • Entity • Class • Property • Attribute • Relation Datasets
  • 5. What is semantic data summarization? Why? 1. Summarizing entity descriptions (a.k.a. entity summarization)
  • 6. What is semantic data summarization? Why? 2. Summarizing entity associations Alice Bob article-A paper-A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor
  • 7. What is semantic data summarization? Why? 3. Summarizing semantic datasets
  • 8. Two types of summaries • Extractive methods • summary = a subset of data • summarization = ranking and selection • Abstractive methods (a.k.a. non-extractive methods) • summary = a high-level abstraction of data • summarization = a more complex process
  • 10. Outline of this talk • Summarizing entity descriptions • Summarizing entity associations • Summarizing semantic datasets • Summarizing ontologies (if time permits)
  • 11. Outline of this talk • Summarizing entity descriptions • Summarizing entity associations • Summarizing semantic datasets
  • 12. Summarizing entity descriptions • Extractive methods (summary = a subset of property-value pairs) • Metrics for ranking property-value pairs • Intrinsic metrics • Extrinsic metrics • Structures for combining metrics • Abstractive methods • Not known yet Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman …
  • 13. Intrinsic metrics 1. Frequency 2. Centrality 3. Informativeness 4. Diversity
  • 14. Intrinsic metrics (1): frequency • Frequency of property • Frequency in the dataset • Frequency among entities of the same type • Frequency in this entity description • Frequency in the ontology (i.e., richness of definition) Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman … Property Value … influenced … … Property Value … type Artist creates … …
  • 15. Intrinsic metrics (1): frequency • Frequency of property value • Frequency in the dataset (note: entities in text) • Frequency in this entity description (note: indirect relations) Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman … Property Value … … Mona Lisa … … Lady with an Ermine … Property Value … … …Mona Lisa… … Indirect relations may also be counted.
  • 16. Intrinsic metrics (1): frequency • Frequency of property-value pair • Frequency among similar entities • Frequency in the dataset (why not?) Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman … Property Value … type Artist … influenced Richard Feynman … (a similar entity)
  • 17. Intrinsic metrics (2): centrality • Centrality of property value • Within the dataset: (weighted) PageRank • On the Web: authority of datasets referencing it Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman …
  • 18. Intrinsic metrics (2): centrality • Centrality of property-value pair • PageRank, weighted by inverse Google distance[Cheng et al., ISWC’11] Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman … name: Leonardo da Vinci type: Person creates: Mona Lisa …
  • 19. Intrinsic metrics (3): informativeness • Informativeness of property-value pair • Self-information of property-value pair[Cheng et al., ISWC’11] • Depth of class Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman … Property Value … type Person type Scientist … Person Artist Scientist
  • 20. Intrinsic metrics (4): diversity • Diversity of properties • To avoid common properties • To avoid properties having similar values Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman …
  • 21. Intrinsic metrics (4): diversity • Diversity of property-value pairs[Cheng et al., JoWS’15, WWW’15] • Similarity between text: string-based, word-based • Similarity between numbers • Semantic similarity: reasoning-based Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman … Person Artist Scientist type:Artist ⇒ type:Person
  • 22. Extrinsic metrics 1. Using external knowledge 2. Context-based
  • 23. Extrinsic metrics (1): using external knowledge • Using domain knowledge • Certain properties are known to be important. • Using indicators on the Web • Search engine hits • Bidirectional links in Wikipedia • Using user feedback • User clicks Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman …
  • 24. Extrinsic metrics (2): context-based • Entity search results • context = query • solution: query relevance [Cheng et al., IJSWIS’09]
  • 25. Extrinsic metrics (2): context-based • Entities in a document • context = contents of the document • solution: Class Vector Model[Cheng et al., WWW’15] Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman … vector = {Painting} … The Starry Night, from MoMA’s collection, reminds us of some work painted by Leonardo da Vinci. ... Property Value … type Painting … vector(context) = {Painting} vector = {Artist}
  • 26. Extrinsic metrics (2): context-based • Co-summarization • context = other entities • solution: • difference from other entities[Cheng et al., WWW’15] (for entity linking) • similarity with other entities[Cheng et al., JoWS’15] (for entity coreference resolution)
  • 27. Structures for combining metrics 1. Result combination 5 1 3 2 4 5 2 4 1 3 5 1 2 4 3 Ranked by Metric A Ranked by Metric B Ranked by Metric C Summary
  • 28. Structures for combining metrics 1. Result combination (cont.) Ranked by Metric A Ties broken by Metric B
  • 29. Structures for combining metrics 2. Arithmetic combination ɑ*MetricA + β*MetricB
  • 30. Structures for combining metrics • e.g., combinatorial optimization • Quadratic Knapsack Problem[Cheng et al., JoWS’15] • Quadratic Multidimensional Knapsack Problem[Cheng et al., WWW’15] Length constraint Similarity with and difference from other entities Inverse similarity Diagonal: informativeness One entity The other entity Inverse similarity
  • 31. Structures for combining metrics • e.g., weighted PageRank[Cheng et al., ISWC’11] Property Value name Leonardo da Vinci type Person type Artist dateOfBirth 1452-04-15 creates Mona Lisa creates Lady with an Ermine knownFor Mona Lisa influenced Richard Feynman … name: Leonardo da Vinci type: Person creates: Mona Lisa … Probability of jumpingProbability of following edges Inverse Google distance Informativeness
  • 32. Structures for combining metrics 3. Machine Learning • Decision trees • Linear regression
  • 33. Structures for combining metrics 4. Complex combinations • Result combination + arithmetic combination • Machine learning + arithmetic combination
  • 35. Outline of this talk • Summarizing entity descriptions • Summarizing entity associations • Summarizing semantic datasets
  • 36. Summarizing entity associations • Extractive methods • Finding and ranking associations between two entities (summary = a subset of paths) • Path finding and filtering • Intrinsic and extrinsic metrics for ranking paths • Structures for combining metrics • Finding and ranking associations between multiple entities (summary = a subset of subgraphs) • Abstractive methods • Ranking association patterns • Hierarchically organizing association patterns Alice Bob article-A paper-A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor
  • 37. Finding associations between two entities • Path finding • Dijkstra or A* • Bidirectional breadth-first search (bi-BFS) • Schema-based performance optimization Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor Paper Person Conference inProcOf cites, extends O(Δd)  O(Δd/2)
  • 38. Finding associations between two entities • Path filtering • By length • By entities, classes, relations • By keywords Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor
  • 39. Ranking associations between two entities • Intrinsic metrics • Frequency • Centrality • Informativeness • Diversity • Length • Conformity • Extrinsic metrics • Using external knowledge • Context-based • Structures for combining metrics
  • 40. Intrinsic metrics: frequency, centrality, diversity, length • Property frequency • Degree centrality • Diverse relations • Length Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor
  • 41. Intrinsic metrics: informativeness • Informativeness • Data-based informativeness: inverse relation frequency • Schema-based informativeness: depth of class/relation Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor
  • 42. Intrinsic metrics: conformity • Conformity to schema Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor Paper Person Conference inProcOf cites, extends
  • 43. Extrinsic metrics • Using external knowledge • Explicit: user-defined weights • Implicit: user’s Web browsing history • Context-based • Query relevance Alice Bob article-A paper-A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor
  • 44. Finding and ranking associations between multiple entities • association = a size-constrained connected subgraph (size = number of other entities) 3 associations via 2 other entities
  • 45. Finding and ranking associations between multiple entities • association = a size-constrained connected subgraph (size = diameter)[Cheng et al., ISWC’16] 3 associations having a diameter of 3
  • 46. Finding and ranking associations between multiple entities • Subgraph finding • n-directional breadth-first search • Distance-based performance optimization[Cheng et al., ISWC’16]
  • 47. Finding and ranking associations between multiple entities • Subgraph ranking (based on entity ranking) • PageRank • Query relevance • Number of short paths • Random walk with restart
  • 48. Finding and ranking associations between multiple entities • association = a Steiner tree (size-unconstrained, weight-minimized)
  • 49. Abstractive methods • Association pattern[Cheng et al., ISWC’14] paper-A conf-A inProcOfsecondAuthor reviewer paper-B conf-B inProcOffirstAuthor chair Paper Conference inProcOfauthor role Patterns Associations
  • 50. Abstractive methods • Association pattern[Cheng et al., ISWC’16] Patterns Associations
  • 51. Ranking association patterns • Metrics • Frequency • Informativeness • Diversity • Structures for combining metrics Paper Conference inProcOfauthor role
  • 52. Metrics: frequency • frequency = occurrences of canonical code[Cheng et al., ISWC’16] = isomorphic? eq 1r1C1r2C2r3eq 2$r4eq 3$$$$ (when T=e)
  • 53. Metrics: frequency • frequency = occurrences of canonical code[Cheng et al., ISWC’16] ? Solution: using query entities as proxies for classes to be ordered
  • 54. Hierarchically organizing association patterns • subClassOf/subPropertyOf  subPatternOf[Zhang et al., JIST’13] Paper Conference inProcOfauthor role Demo Conference inProcOfauthor reviewer Poster Conference inProcOfauthor chair
  • 56. Outline of this talk • Summarizing entity descriptions • Summarizing entity associations • Summarizing semantic datasets
  • 57. Summarizing semantic datasets • Extractive methods (summary = a subset of triples) • Centrality • Abstractive methods 1. Inferred schema 2. Flat partitioning 3. Hierarchical grouping
  • 58. Extractive methods • Triple ranking (based on entity ranking) • Centrality: degree, PageRank Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor
  • 59. Abstractive methods (1): inferred schema • summary = a graph-structured (sub-)schema inferred from data (grouping entities by classes) Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor Paper Person Conference inProcOf cites, extends
  • 60. Abstractive methods (1): inferred schema • Metrics for ranking classes and properties • Frequency • Centrality Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor Paper Person Conference inProcOf cites, extends
  • 61. Abstractive methods (2): flat partitioning • summary = entity partitions connected by relations • partitioning by shared classes (= inferred schema) • partitioning by shared attributes • partitioning by shared paths (a.k.a. bisimulation) Alice Bob article-A paper- A AAAI IJCAI paper-B paper-C paper-D inProcOf secondAuthor reviewer chair firstAuthor firstAuthor inProcOf citessecondAuthor cites extends firstAuthor Paper Person Conference inProcOf cites, extends
  • 62. Abstractive methods (3): hierarchical grouping[Cheng et al., IJCAI’16] • summary = a hierarchical grouping of entities • identified by property-value pairs • connected by relations A hierarchical grouping of entities Relations connecting sibling groups
  • 63. • Metrics for choosing groups (i.e., property-value pairs) • Coverage of data  large subgroups • Height of hierarchy  moderate-sized subgroups • Cohesion within groups  informative property-value pairs • Overlap between groups  controllable overlap • Homogeneity of groups  different values of the same property A hierarchical grouping of entities Relations connecting sibling groups Abstractive methods (3): hierarchical grouping[Cheng et al., IJCAI’16]
  • 64. • Combining metrics by combinatorial optimization (formulated as a multidimensional knapsack problem) maximizing moderateness of each subgroup maximizing cohesion within each subgroup disallowing large overlap between subgroups selecting ≤k subgroups (optionally) disallowing different properties Abstractive methods (3): hierarchical grouping[Cheng et al., IJCAI’16]
  • 65. Concluding remarks • Research • More application scenarios are to be identified. • New applications may promote new metrics. • More benchmarks are needed for evaluation. • Practice • Handy tools for semantic data summarization are missing. The 2016 ENtity Summarization Evaluation Campaign (ENSEC 2016) http://km.aifb.kit.edu/ws/sumpre2016/challenge.html
  • 66. Papers on summarizing entity descriptions • Gong Cheng, Danyun Xu, Yuzhong Qu. Summarizing Entity Descriptions for Effective and Efficient Human-centered Entity Linking. (WWW'15) • Gong Cheng, Danyun Xu, Yuzhong Qu. C3D+P: A Summarization Method for Interactive Entity Resolution. (JoWS’15) • Gong Cheng, Thanh Tran, Yuzhong Qu. RELIN: Relatedness and Informativeness-based Centrality for Entity Summarization. (ISWC’11) • Gong Cheng, Yuzhong Qu. Searching Linked Objects with Falcons: Approach, Implementation and Evaluation. (IJSWIS’09)
  • 67. Papers on summarizing entity associations • Gong Cheng, Daxin Liu, Yuzhong Qu. Efficient Algorithms for Association Finding and Frequent Association Pattern Mining. (ISWC'16) • Gong Cheng, Yanan Zhang, Yuzhong Qu. Explass: Exploring Associations between Entities via Top-K Ontological Patterns and Facets. (ISWC’14) • Yanan Zhang, Gong Cheng, Yuzhong Qu. Towards Exploratory Relationship Search: A Clustering-based Approach (JIST’13)
  • 68. Papers on summarizing semantic datasets • Gong Cheng, Cheng Jin, Yuzhong Qu. HIEDS: A Generic and Efficient Approach to Hierarchical Dataset Summarization. (IJCAI’16)
  • 70. Ontology • Terms • Publication • Paper • Conference • title • inProc • Term descriptions • SubClassOf(Paper, Publication) • SubClassOf(Paper, DataExactCardinality(1, title)) • ObjectPropertyDomain(inProc, Paper) • ObjectPropertyRange(inProc, Conference)
  • 72. Summarizing ontologies • Extractive methods 1. Ranking terms (summary = a subset of terms) 2. Ranking term descriptions (summary = a subset of term descriptions) 3. Ranking subgraphs (summary = a subgraph) • Abstractive methods • Not known yet
  • 73. Extractive methods (1): ranking terms • Intrinsic metrics 1. Frequency 2. Centrality 3. Diversity 4. Simplicity • Extrinsic metrics 1. Using external knowledge 2. Context-based
  • 74. Intrinsic metrics (1): frequency • Schema-based frequency • Data-based frequency
  • 75. Intrinsic metrics (2): centrality • Middleness in the hierarchy • Degree • Betweenness • PageRank Paper Publication title inProc Conference Publication Paper Book Article Poster
  • 76. Intrinsic metrics (3): diversity • Coverage of hierarchy Publication Paper Book Article Poster
  • 77. Intrinsic metrics (4): simplicity • Number of words in the name of a term Paper vs. PaperPublishedAtCCKS2016
  • 78. Extrinsic metrics • Using external knowledge • Search engine hits • Personalization (e.g., spreading activation) • Context-based • Query relevance Paper Publication title inProc Conference
  • 79. Extractive methods (2): ranking term descriptions • Graph representation of term descriptions 1. Description graph 2. Term-description graph • Ranking term descriptions • Intrinsic metrics • Extrinsic metrics
  • 80. Graph representation (1): description graph [Zhang et al., WWW’07] SubClassOf(Paper, Publication) SubClassOf(Paper, DataExactCardinality(1, title)) ObjectPropertyDomain(inProc, Paper) ObjectPropertyRange(inProc, Conference) SubClassOf(Paper, Publication) SubClassOf(Paper, DataExactCardinality(1, title)) ObjectPropertyDomain(inProc, Paper) ObjectPropertyRange(inProc, Conference)
  • 81. Graph representation (2): term-description graph [Zhang et al., JCST’09; Cheng et al., JIST’11] SubClassOf(Paper, Publication) SubClassOf(Paper, DataExactCardinality(1, title)) ObjectPropertyDomain(inProc, Paper) ObjectPropertyRange(inProc, Conference) SubClassOf(Paper, Publication) SubClassOf(Paper, DataExactCardinality(1, title)) ObjectPropertyDomain(inProc, Paper) ObjectPropertyRange(inProc, Conference) Paper Publication title inProc Conference
  • 82. Ranking term descriptions • Intrinsic metrics • Frequency • Centrality • Diversity • Cohesion/coherence • Extrinsic metrics • Query relevance SubClassOf(Paper, Publication) SubClassOf(Paper, DataExactCardinality(1, title)) ObjectPropertyDomain(inProc, Paper) ObjectPropertyRange(inProc, Conference)
  • 83. Papers on summarizing ontologies • Weiyi Ge, Gong Cheng, Huiying Li, Yuzhong Qu. Incorporating Compactness to Generate Term-association View Snippets for Ontology Search. (IP&M’13) • Gong Cheng, Feng Ji, Shengmei Luo, Weiyi Ge, Yuzhong Qu. BipRank: Ranking and Summarizing RDF Vocabulary Descriptions. (JIST’11) • Xiang Zhang, Gong Cheng, Weiyi Ge, Yuzhong Qu. Summarizing Vocabularies in the Global Semantic Web. (JCST’09) • Xiang Zhang, Gong Cheng, Yuzhong Qu. Ontology Summarization Based on RDF Sentence Graph. (WWW’07)