hunting for semantic clusters hierarchical structuring of cultural heritage objects within large...
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Hunting for semantic clusters– Hierarchical structuring of Cultural Heritage objects within large aggregations
Shenghui Wang1 Antoine Isaac2 Valentine Charles2
Rob Koopman1 Anthi Agoropoulou2 Titia van der Werf1
1OCLC Research, Leiden, The Netherlands
2Europeana Foundation, The Hague, The Netherlands
TPDL 2013
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 1 / 24
Outline
1 Introduction
2 Hierarchically structuring CH objects based on levels of similarity
3 Results and evaluation
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 2 / 24
Outline
1 Introduction
2 Hierarchically structuring CH objects based on levels of similarity
3 Results and evaluation
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 3 / 24
Large-scale aggregators
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 4 / 24
Search Europeana
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 5 / 24
Duplicates
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 6 / 24
Duplicates?
Same objects, different providers
Same page digitised three times
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 7 / 24
Duplicates?
Same objects, different providers
Same page digitised three times
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 7 / 24
Challenges in large-scale aggregators
Aggregation of metadata from heterogeneous collections leads to dataquality issues (e.g., duplicates)
Mapping from different formats and vocabularies to a shared datamodel may cause information missing (e.g., internal and external linksbetween objects)
Cultural Heritage objects could be linked differently (e.g., duplication,depiction/representation, derivation, succession, etc.)
Keyword-based search does not provide end users a global overview ofwhat is available.
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 8 / 24
Challenges in large-scale aggregators
Aggregation of metadata from heterogeneous collections leads to dataquality issues (e.g., duplicates)
Mapping from different formats and vocabularies to a shared datamodel may cause information missing (e.g., internal and external linksbetween objects)
Cultural Heritage objects could be linked differently (e.g., duplication,depiction/representation, derivation, succession, etc.)
Keyword-based search does not provide end users a global overview ofwhat is available.
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 8 / 24
Challenges in large-scale aggregators
Aggregation of metadata from heterogeneous collections leads to dataquality issues (e.g., duplicates)
Mapping from different formats and vocabularies to a shared datamodel may cause information missing (e.g., internal and external linksbetween objects)
Cultural Heritage objects could be linked differently (e.g., duplication,depiction/representation, derivation, succession, etc.)
Keyword-based search does not provide end users a global overview ofwhat is available.
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 8 / 24
Challenges in large-scale aggregators
Aggregation of metadata from heterogeneous collections leads to dataquality issues (e.g., duplicates)
Mapping from different formats and vocabularies to a shared datamodel may cause information missing (e.g., internal and external linksbetween objects)
Cultural Heritage objects could be linked differently (e.g., duplication,depiction/representation, derivation, succession, etc.)
Keyword-based search does not provide end users a global overview ofwhat is available.
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 8 / 24
Outline
1 Introduction
2 Hierarchically structuring CH objects based on levels of similarity
3 Results and evaluation
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 9 / 24
Hierarchical structuring based on levels of similarity
Our method contains three parts:
Fast clustering algorithm based on minhashes and compressionsimilarity
Field selection for focal semantic clusters
Hierarchically structuring records based on similarity
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 10 / 24
Fast clustering based on minhashes and compressionsimilarity
Two-step approach:
Grouping records which could potentially be further clustered
Transform metadata into a set of minhashesGroup records with similar minhashes
Iterative parallel clustering records based on compression similarity
Select cluster heads which are far apartGreedily assign records to the closest cluster headDivide clusters if the clusters are not ”compact” enough
By varying the similarity level, clusters with different compactness canbe produced.
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 11 / 24
Fast clustering based on minhashes and compressionsimilarity
Two-step approach:
Grouping records which could potentially be further clustered
Transform metadata into a set of minhashesGroup records with similar minhashes
Iterative parallel clustering records based on compression similarity
Select cluster heads which are far apartGreedily assign records to the closest cluster headDivide clusters if the clusters are not ”compact” enough
By varying the similarity level, clusters with different compactness canbe produced.
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 11 / 24
Focal semantic clusters
At level 100, many clusters contains duplicates, or records withalmost identical metadata.
At level 80, many clusters are of specific interests, e.g., pages of thesame book, pictures of the a same building, etc.
These focal semantic clusters often represent small cultural entities,which can be connected to other entities.
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 12 / 24
Focal semantic clusters
At level 100, many clusters contains duplicates, or records withalmost identical metadata.
At level 80, many clusters are of specific interests, e.g., pages of thesame book, pictures of the a same building, etc.
These focal semantic clusters often represent small cultural entities,which can be connected to other entities.
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 12 / 24
Focal semantic clusters
At level 100, many clusters contains duplicates, or records withalmost identical metadata.
At level 80, many clusters are of specific interests, e.g., pages of thesame book, pictures of the a same building, etc.
These focal semantic clusters often represent small cultural entities,which can be connected to other entities.
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 12 / 24
Field selection for focal semantic clusters
However, different data providers do not apply same standards in thesame way.
Same information could be put into different metadata fieldsThe extent to which an object is described varies a lot provider byprovider.
Not all metadata fields should be used for clustering.
Otherwise, the pages of one book are scattered in multiple clusters.
We applied a standard Genetic Algorithm to automatically select theimportant fields which give the best focal clusters.
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 13 / 24
Field selection for focal semantic clusters
However, different data providers do not apply same standards in thesame way.
Same information could be put into different metadata fieldsThe extent to which an object is described varies a lot provider byprovider.
Not all metadata fields should be used for clustering.
Otherwise, the pages of one book are scattered in multiple clusters.
We applied a standard Genetic Algorithm to automatically select theimportant fields which give the best focal clusters.
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 13 / 24
Field selection for focal semantic clusters
However, different data providers do not apply same standards in thesame way.
Same information could be put into different metadata fieldsThe extent to which an object is described varies a lot provider byprovider.
Not all metadata fields should be used for clustering.
Otherwise, the pages of one book are scattered in multiple clusters.
We applied a standard Genetic Algorithm to automatically select theimportant fields which give the best focal clusters.
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 13 / 24
Hierarchical structuring based on levels of similarity
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· · ·Field selection 3
Provider 3· · ·
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· · ·Field selection 2
Provider 2· · ·
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Field selection 1
Provider 1Level 100
Original
G.A.
Repr.
· · ·· · ·· · ·· · ·Level 80
· · ·· · ·Level 60
· · ·· · ·· · ·· · ·Level 40
· · ·· · ·level 20
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 14 / 24
Hierarchy example
20_651
40_4745 40_7923
19954396 19954417 19971448 19955162 19954431 19956753
80_17351 80_17198
19954427 19955460 19954333 19954398
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 15 / 24
Outline
1 Introduction
2 Hierarchically structuring CH objects based on levels of similarity
3 Results and evaluation
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 16 / 24
Experiments with a small dataset
We applied the method on 1.1 million records from the UK.
Manually check randomly chosen clusters and try to understand whatmade these records clustered together, i.e., identify the semantic linksbetween records
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 17 / 24
Categories of clusters
Same objects/duplicate records
Views of the same object
Derivative works
Parts of the same object
Collections
Thematic groupings
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 18 / 24
Categories of clusters
Same objects/duplicate records
Views of the same object
Derivative works
Parts of the same object
Collections
Thematic groupingsBowburn, boiler house
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 18 / 24
Categories of clusters
Same objects/duplicate records
Views of the same object
Derivative works
Parts of the same object
Collections
Thematic groupings
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 18 / 24
Categories of clusters
Same objects/duplicate records
Views of the same object
Derivative works
Parts of the same object
Collections
Thematic groupingsLetter from Capt. John LivingstonRAMC
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 18 / 24
Categories of clusters
Same objects/duplicate records
Views of the same object
Derivative works
Parts of the same object
Collections
Thematic groupings
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 18 / 24
Categories of clusters
Same objects/duplicate records
Views of the same object
Derivative works
Parts of the same object
Collections
Thematic groupings
”Rural life”
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 18 / 24
Working with the full Europeana dataset
23.6M records from 2428 data providers across Europe (a data dumpon Feb 2013)
At level 100, we found more than 200K clusters which contain highlysimilar records
At level 80, we found nearly 1.5 million focal clusters from allindividual data providers.
Similarity level #Records to be clustered #Clusters Time
100 23,595,555 200,245 6m2.82s80 23,595,555 1,476,089 *60 6,407,615 382,268 3m35.26s40 2,431,753 212,389 2m28.79s20 1,068,188 84,554 1m20.99s
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 19 / 24
Working with the full Europeana dataset
23.6M records from 2428 data providers across Europe (a data dumpon Feb 2013)
At level 100, we found more than 200K clusters which contain highlysimilar records
At level 80, we found nearly 1.5 million focal clusters from allindividual data providers.
Similarity level #Records to be clustered #Clusters Time
100 23,595,555 200,245 6m2.82s80 23,595,555 1,476,089 *60 6,407,615 382,268 3m35.26s40 2,431,753 212,389 2m28.79s20 1,068,188 84,554 1m20.99s
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 19 / 24
Working with the full Europeana dataset
23.6M records from 2428 data providers across Europe (a data dumpon Feb 2013)
At level 100, we found more than 200K clusters which contain highlysimilar records
At level 80, we found nearly 1.5 million focal clusters from allindividual data providers.
Similarity level #Records to be clustered #Clusters Time
100 23,595,555 200,245 6m2.82s80 23,595,555 1,476,089 *60 6,407,615 382,268 3m35.26s40 2,431,753 212,389 2m28.79s20 1,068,188 84,554 1m20.99s
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 19 / 24
Working with the full Europeana dataset
23.6M records from 2428 data providers across Europe (a data dumpon Feb 2013)
At level 100, we found more than 200K clusters which contain highlysimilar records
At level 80, we found nearly 1.5 million focal clusters from allindividual data providers.
Similarity level #Records to be clustered #Clusters Time
100 23,595,555 200,245 6m2.82s80 23,595,555 1,476,089 *60 6,407,615 382,268 3m35.26s40 2,431,753 212,389 2m28.79s20 1,068,188 84,554 1m20.99s
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 19 / 24
Field selection for focal semantic clusters
For the 10 providers with most records (covering 35% of the wholeEuropeana dataset), it took 161 minutes on average.
Datasets with 200-250 records cost 21 minutes on average.
#Providers metadata field1 2358 dc:title
2 436 dc:type
3 328 dc:language
4 315 dc:rights
5 309 dc:subject
#Providers field combination1 1521 dc:title
2 37 dc:title dc:type
3 28 dc:title dc:creator
4 23 dc:title dc:identifier
5 20 dc:description
(a) Top 10 most selected fields (b) Top 5 most selected field combinations
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 20 / 24
Field selection for focal semantic clusters
For the 10 providers with most records (covering 35% of the wholeEuropeana dataset), it took 161 minutes on average.
Datasets with 200-250 records cost 21 minutes on average.
#Providers metadata field1 2358 dc:title
2 436 dc:type
3 328 dc:language
4 315 dc:rights
5 309 dc:subject
#Providers field combination1 1521 dc:title
2 37 dc:title dc:type
3 28 dc:title dc:creator
4 23 dc:title dc:identifier
5 20 dc:description
(a) Top 10 most selected fields (b) Top 5 most selected field combinations
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 20 / 24
Manual evaluation
Randomly select 100 clusters at each level
7 evaluators categorised these clusters, based on the categories foundin the first round
Cluster CategorySimilarity Level
100 80 60 40 20
Same objects/duplicate records 11 10 1 0 0Views of the same object 61 33 6 2 5Parts of an object 10 11 3 1 2Derivative works 2 1 0 0 0Collections 1 4 27 13 43Thematic grouping 9 34 36 29 22Nonsense 2 3 30 57 28
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 21 / 24
Manual evaluation
Randomly select 100 clusters at each level
7 evaluators categorised these clusters, based on the categories foundin the first round
Cluster CategorySimilarity Level
100 80 60 40 20
Same objects/duplicate records 11 10 1 0 0Views of the same object 61 33 6 2 5Parts of an object 10 11 3 1 2Derivative works 2 1 0 0 0Collections 1 4 27 13 43Thematic grouping 9 34 36 29 22Nonsense 2 3 30 57 28
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 21 / 24
Conclusions
Finding similar CH objects is the first step towards identifyingsemantic links and groups of objects within large-scale aggregations.
We developed a fast and scalable clustering algorithm, applied aGenetic Algorithm to select important fields and proposed aninfrastructure to hierarchically structuring CH objects.
Our evaluation shows the clusters at high similarity levels are usuallyaccurate and useful, while those at lower levels need moreinvestigation.
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 22 / 24
Multidimensional similarities
Sir James Eyre (1734-1799), Chief Justice of the Common Pleas
(Government Art Collection)
Eyre, James(Austrian National Library)
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 23 / 24
Multidimensional similarities
Sir James Eyre (1734-1799), Chief Justice of the Common Pleas
(Government Art Collection)
Sir John Eardley Wilmot (1709-1792) Chief Justice of the Common Pleas
(Government Art Collection)
Eyre, James(Austrian National Library)
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 23 / 24
Continue hunting for semantic clusters
Thank you!
Shenghui Wang (shenghui.wang@oclc.org)Antoine Isaac (aisaac@few.vu.nl)Valentine Charles (Valentine.Charles@kb.nl)Rob Koopman (Rob.Koopman@oclc.org)Anthi Agoropoulou (anthi.agoropoulou@gmail.com)
Titia van der Werf (titia.vanderwerf@oclc.org)
Shenghui Wang, Antoine Isaac, Valentine Charles, Rob Koopman, Anthi Agoropoulou, Titia van der Werf ( OCLC Research, Leiden, The Netherlands, Europeana Foundation, The Hague, The Netherlands)Hunting for semantic clusters TPDL 2013 24 / 24
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