The increasing adoption of Linked Data principles has led to an abundance of datasets on the Web. However, take-up and reuse is hindered by the lack of descriptive information about the nature of the data, such as their topic coverage, dynamics or evolution. To address this issue, we propose an approach for creating linked dataset profiles. A profile consists of structured dataset metadata describing topics and their relevance. Profiles are generated through the configuration of techniques for resource sampling from datasets, topic extraction from reference datasets and their ranking based on graphical models. To enable a good trade-off between scalability and accuracy of generated profiles, appropriate parameters are determined experimentally. Our evaluation considers topic profiles for all accessible datasets from the Linked Open Data cloud. The results show that our approach generates accurate profiles even with comparably small sample sizes (10%) and outperforms established topic modelling approaches.

1. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
A Scalable Approach for Efficiently Generating
Structured Dataset Topic Profiles
Besnik Fetahu1
, Stefan Dietze1
, Bernardo Pereira Nunes2
, Marco
Antonio Casanova2
, Davide Taibi3
, Wolfgang Nejdl1
1L3S Research Center, Leibniz Universit¨at Hannover
2Department of Informatics - PUC-Rio
3Institute for Educational Technologies, CNR
May 29, 2014

2. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
1 Introduction
2 Problem and Motivation
3 Approach
Resource Instance and Type Extraction
Resource Sampling Approaches
Constructing profiles: Dataset-topic graph
Topic Ranking Approaches
4 Experimental Setup
Baselines
5 Evaluation Results
Efficiency of Dataset Profiling
Scalability of Dataset Profiling
6 Conclusions

3. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
1 Introduction
2 Problem and Motivation
3 Approach
Resource Instance and Type Extraction
Resource Sampling Approaches
Constructing profiles: Dataset-topic graph
Topic Ranking Approaches
4 Experimental Setup
Baselines
5 Evaluation Results
Efficiency of Dataset Profiling
Scalability of Dataset Profiling
6 Conclusions

4. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Introduction
• Increasing amount of Web Data

5. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Introduction
• Increasing amount of Web Data
• Data heterogeneity: representation, language, quality and
domains

6. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Introduction
• Increasing amount of Web Data
• Data heterogeneity: representation, language, quality and
domains
• Sparsely connected datasets

7. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Introduction
• Increasing amount of Web Data
• Data heterogeneity: representation, language, quality and
domains
• Sparsely connected datasets
• Lack of descriptive metadata about datasets

8. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Introduction
• Increasing amount of Web Data
• Data heterogeneity: representation, language, quality and
domains
• Sparsely connected datasets
• Lack of descriptive metadata about datasets
• Exhaustive techniques for data analysis

9. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Introduction
• Increasing amount of Web Data
• Data heterogeneity: representation, language, quality and
domains
• Sparsely connected datasets
• Lack of descriptive metadata about datasets
• Exhaustive techniques for data analysis
• Efficiency heavily dependent on information need

10. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Introduction
• Increasing amount of Web Data
• Data heterogeneity: representation, language, quality and
domains
• Sparsely connected datasets
• Lack of descriptive metadata about datasets
• Exhaustive techniques for data analysis
• Efficiency heavily dependent on information need
• Ease of access and representation of datasets

11. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
1 Introduction
2 Problem and Motivation
3 Approach
Resource Instance and Type Extraction
Resource Sampling Approaches
Constructing profiles: Dataset-topic graph
Topic Ranking Approaches
4 Experimental Setup
Baselines
5 Evaluation Results
Efficiency of Dataset Profiling
Scalability of Dataset Profiling
6 Conclusions

12. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Why dataset profiling?
• Growing number of datasets: 227 datasets
• Data represented as triples: 31 billion triples
• Multi-lingual content: 18 languages
• Broad set of topics covered
• Inter-dataset links
Domain # Data. Triples
Media 25 1,841,852,061
Geographic 31 6,145,532,484
Government 49 13,315,009,400
Publications 87 2,950,720,693
Cross-domain 41 4,184,635,715
Life sciences 41 3,036,336,004
User-generated 20 134,127,413

13. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Why dataset profiling?
Find datasets covering the domain of “Renewable Energy”?
• Sparsity: Datasets that cover the topic?
• 38 out of 228 datasets contain
topic coverage information.

14. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Why dataset profiling?
Find datasets covering the domain of “Renewable Energy”?
• Sparsity: Datasets that cover the topic?
• 38 out of 228 datasets contain
topic coverage information.
• Scalability: Use SPARQL filter clause?
• regex(*) filter clause needs
to check all triples that contain
a specific keyword.

15. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Why dataset profiling?
Find datasets covering the domain of “Renewable Energy”?
• Sparsity: Datasets that cover the topic?
• 38 out of 228 datasets contain
topic coverage information.
• Scalability: Use SPARQL filter clause?
• regex(*) filter clause needs
to check all triples that contain
a specific keyword.
• Disambiguity: What are all the possible forms of
renewable energy?
• solar energy, wind energy, geothermal. . .

16. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
1 Introduction
2 Problem and Motivation
3 Approach
Resource Instance and Type Extraction
Resource Sampling Approaches
Constructing profiles: Dataset-topic graph
Topic Ranking Approaches
4 Experimental Setup
Baselines
5 Evaluation Results
Efficiency of Dataset Profiling
Scalability of Dataset Profiling
6 Conclusions

17. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Profiling Overview
1 Metadata extraction

18. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Profiling Overview
1 Metadata extraction
2 Resource sampling

19. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Profiling Overview
1 Metadata extraction
2 Resource sampling
3 Entity/topic extraction

20. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Profiling Overview
1 Metadata extraction
2 Resource sampling
3 Entity/topic extraction
4 Profile graphs

21. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Profiling Overview
1 Metadata extraction
2 Resource sampling
3 Entity/topic extraction
4 Profile graphs
5 Profiles representation

22. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Dataset Profiling Example

23. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Dataset Profiling Example

24. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Dataset Profiling Example

25. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Dataset Profiling Example

26. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Resource Instance and Type Extraction
• Simple SPARQL SELECT queries
• Avg. indexing time 10% (7min) vs. 100% (4hrs).
• Approximately ∼300 million resource instances
10
100
1000
10000
100000 uriburner
bluk-bnb
bio2rdf-kegg-pathway
nom
enclator-asturias
b3katlobid-resources
twc-ieeevis
educationalprogram
ssisvu
farm
bio-chem
bl
world-bank-linked-data
event-m
edia
eea
eunishungarian-national-library-catalog
bio2rdf-pubm
ed
linked-user-feedback
oecd-linked-data
bio2rdf-goa
pscs-catalogue
bio2rdf-genbank
linkedm
db
bfs-linked-data
bio2rdf-reactom
e
british-m
useum
-collection
bio2rdf-ncbigene
datos-bcn-cl
l3s-dblp
bio2rdf-sgd
hellenic-fire-brigade
Log-scaleindexingtime
100%
10%

27. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Resource Sampling Approaches
Entity and Topic Extraction
Resource Sampling
• random: randomly select a resource instance for analysis
1
DBpedia Spotlight

28. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Resource Sampling Approaches
Entity and Topic Extraction
Resource Sampling
• random: randomly select a resource instance for analysis
• weighted: weigh a resource by the number of datatype
properties used to describe it wk = |f (rk)|/max{|f (rj )|}
1
DBpedia Spotlight

29. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Resource Sampling Approaches
Entity and Topic Extraction
Resource Sampling
• random: randomly select a resource instance for analysis
• weighted: weigh a resource by the number of datatype
properties used to describe it wk = |f (rk)|/max{|f (rj )|}
• centrality: weigh a resource by the number of types
used to describe it ck = |Ck|/|C|
Topic Extraction
• Resources as documents by combining all textual literals
• Perform NED1 and extract corresponding DBpedia entities
• Extract topics as DBpedia categories from entities via
dcterms:subject
1
DBpedia Spotlight

30. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Constructing profiles: Dataset-topic graph
1 Profile graph nodes: datasets,
resources, topics
2 Weighted graph edges: ∆ D, t
3 Edge weights: ∆ Di , t = ∆ Dj , t
4 Compute ∆ Di , t by assessing the
importance of t given the resources
of Di as prior knowledge
5 The given prior knowledge biases
the importance of t in the profile
graph towards Di
2
6 Incrementally add datasets in the
profile graph, by simply computing
the weights ∆ Dk , t
2
Scott White and Padhraic Smyth. 2003. Algorithms for estimating relative
importance in networks. In 9th ACM SIGKDD (KDD ’03).

31. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Topic Ranking Approaches
Topic filtering
Topic pre-filtering:
NTR(t, D) =
Φ(·, D)
Φ(t, D)
+
Φ(·, ·)
Φ(t, ·)
• Filter noisy topics
• φ(·, ·) - number of entities
associated with topic t
• Closely related to the tf-idf
weighting scheme
Topic Ranking
• PageRank with Priors (PRankP)
• HITS with Priors (HITSP)
• K-Step Markov (KStepM)

32. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
1 Introduction
2 Problem and Motivation
3 Approach
Resource Instance and Type Extraction
Resource Sampling Approaches
Constructing profiles: Dataset-topic graph
Topic Ranking Approaches
4 Experimental Setup
Baselines
5 Evaluation Results
Efficiency of Dataset Profiling
Scalability of Dataset Profiling
6 Conclusions

33. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Experimental Setup
Datasets and Ground-truth
• 129 dataset from lod-cloud3
• 6 ground-truth datasets with manually assigned topic
indicators for their resources
Dataset Properties #Resources
yovisto
skos:subject, dbp:{subject, class,
discipline, kategorie, tagline}
62879
oxpoints dcterms:subject,dc:subject 37258
socialsemweb-thesaurus
skos:subject, tag:associatedTag,
dcterms:subject
2243
semantic-web-dog-food dcterms:subject, dc:subject 20145
lak-dataset dcterms:subject, dc:subject 1691
Evaluation Metrics
• NDCG@k (k=1, . . . , 1000)
• Compare the induced ranking by the graphical models
against the ideal ranking
3
At the time of experimentation only 129 dataset endpoints were
responsive.

34. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Baselines
• tf-idf: Consider resources as documents. Extract for
each dataset the top {50, 100, 150, 200} terms.
• LDA: Consider dataset as documents4. Extract top
weighted topic terms. For every dataset extract top {50,
100, 150, 200} with a number of topics {10, 20, 30, 40,
50}.
4
In this case it does not matter if datasets are considered at the
resource level or aggregated.

35. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
1 Introduction
2 Problem and Motivation
3 Approach
Resource Instance and Type Extraction
Resource Sampling Approaches
Constructing profiles: Dataset-topic graph
Topic Ranking Approaches
4 Experimental Setup
Baselines
5 Evaluation Results
Efficiency of Dataset Profiling
Scalability of Dataset Profiling
6 Conclusions

36. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Efficiency of Dataset Profiling
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
1 100 200 300 400 500 600 700 800 900 1000
NDCGrankingscore
NDCG rank
Profiling accuracy for all topic ranking approaches
K-Step Markov + NTR
PageRank with priors + NTR
HITS with priors + NTR
LDA
tf-idf
0.16
0.18
0.2
0.22
0.24
0.26
0.28
0.3
0.32
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Sample Size
K-Step Markov profiling accuracy (Centrality Sampling)
KStepM + NTR
KStepM

37. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Scalability of Dataset Profiling
100
1000
10000
0 20 40 60 80 100
0
0.05
0.1
0.15
0.2
0.25
0.3
Log-scaletimeperformance
NDCGrankingscore
Sample Size
Time Performance vs. Profiling Accuracy
HITS with priors time
HITS with priors ranking
K-Step Markov time
K-Step Markov ranking
PageRank with priors time
PageRank with priors ranking
• 5% and 10% already provide
stable profiling accuracy
• Avg. 7mins for indexing 10%
of resources per dataset vs.
4hrs per dataset
• 2mins for ranking dataset
profiles with 10% of resources
vs. 45mins for 100%
• NED runtime 10% vs. 100%?

38. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Motivation Example Revisited!

39. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Conclusions and Future Work
• Structured dataset profiles
• Scalable approach through sampling
• Efficient profiling through topic filtering and ranking
• Incremental generation of dataset profiles
• Dataset profiles as a set of links (entity and topic links)
• Provenance information of links (e.g. resources from
which an entity is extracted)
• Profiles for dataset recommendation, search, etc.
Resources
• Profiles Endpoint:
http://data-observatory.org/lod-profiles/sparql
• Profiles Webpage:
http://data-observatory.org/lod-profiles/

40. Introduction
Problem and
Motivation
Approach
Resource
Instance and
Type Extraction
Resource
Sampling
Approaches
Constructing
profiles:
Dataset-topic
graph
Topic Ranking
Approaches
Experimental
Setup
Baselines
Evaluation
Results
Efficiency of
Dataset Profiling
Scalability of
Dataset Profiling
Conclusions
Thank you! Questions?
#eswc2014Fetahu