Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (20)
En vedette
En vedette (20)
Similaire à Linked Data Quality Assessment – daQ and Luzzu
Similaire à Linked Data Quality Assessment – daQ and Luzzu (20)
Dernier
Dernier (20)
Linked Data Quality Assessment – daQ and Luzzu
- 1. Linked Data Quality Assessment – daQ and Luzzu Jeremy Debattista University of Bonn Presentation at the Ontology Engineering Group (UPM)
- 2. …who am I? • B.Sc (Hons) in Computer Science – University of Malta – Thesis: Collaborative Editing and Expert Finding • M.App Sc in Computer Science – DERI, National University of Ireland, Galway – Thesis: Ontology-based rules for User-Controlled Support in Ubiquitous Environments • PhD Candidate – University of Bonn
- 3. … my PhD – the big picture • Work related to Data Quality (in LD) – representing quality metadata (daQ) – assessing data quality (Luzzu) – identifying new metrics from standard vocabularies (like PROV-O)
- 4. … the need for Quality Metadata • Convincing data consumers to use our published data • Filtering datasets • Poor Quality Perspective – Big Data Veracity
- 5. … the daQ vocabulary
- 6. … the daQ vocabulary
- 7. … the daQ vocabulary • Metadata as Named Graphs • Usage of abstract class concept • Metric assessment as Observations • Preserving Provenance information
- 8. … daQ on the Web http://purl.org/eis/vocab/daq
- 9. … daQ Applications • daQ validator – Validates quality metric schemas extending the daQ (will be online soon) – e.g. checking that each dimension is in exactly one category… • Luzzu – next slides
- 10. … Luzzu – QA Framework • A comprehensive QA framework – assesses LD quality using user-provided metrics (we have a number of LOD metrics already) in a scalable manner – provides queryable metadata (daQ) – provide quality reports which can be used for cleaning • Java Based with maven integration • http://eis-bonn.github.io/Luzzu
- 11. … Luzzu – QA Framework Knowledge) Layer) Quality)Assessment)Unit) Processing)Unit) Assessment) Layer) Seman9c)Schema)Layer) Annota9on)Unit) Opera9ons)Unit) Communica9on)Layer) LQML)Comp.)Unit)
- 12. … Luzzu – QA Framework Dataset& Processing&Unit& Annota0on&Unit& Metric&1& Metric&2& Metric&n&…" Quality&Assessment&Unit& Communica0on&Layer&
- 13. …what’s missing in Luzzu • Make Luzzu work better on Big Data Platforms – We already have a SPARK Processor – How can metrics be scaled on different cores? Something like map-reduce maybe?
- 14. … data quality lifecycle 2.# Assessment# 3.#Data# Repairing#and# Cleaning# 4.#Storage/ Cataloguing/ Archiving## 5.# Explora@on/ Ranking# 1.#Metric# Iden@fica@on# and# Defin i @on#
- 15. … quality metrics • Traditional naïve way • Probabilistic Techniques (A paper was presented at ESWC this year)
- 16. … probabilistic technique hypothesis Probabilistic approximation techniques would : (H1) drastically improve computational time (H2) give close to accurate results
- 17. … probabilistic techniques used Reservoir Sampling Bloom Filters Clustering Coefficient Estimation Dereferenceability Links to External Data Providers Extensional Conciseness Clustering Coefficient of a Network
- 18. … some results Reservoir Sampling Bloom Filters Clustering Coefficient Estimation Dereferenceability Links to External Data Providers Extensional Conciseness Clustering Coefficient of a Network Precision: approx. 75% Time Saved: > 2 Orders of Magnitude Precision: 100% Time Saved: > 2 Orders of Magnitude
- 19. … some results Reservoir Sampling Bloom Filters Clustering Coefficient Estimation Dereferenceability Links to External Data Providers Extensional Conciseness Clustering Coefficient of a Network Precision: approx. 97% Time Saved: > 3 Orders of Magnitude
- 20. … some results Reservoir Sampling Bloom Filters Clustering Coefficient Estimation Dereferenceability Links to External Data Providers Extensional Conciseness Clustering Coefficient of a Network Precision: approx. 95% Time Saved: > 1 Order of Magnitude
- 21. … what am I working on • Large Scale/Data web Scale evaluation Journal Paper – assessing the quality of LOD Cloud datasets • daQ (Journal Paper)
- 22. … what do we do at Bonn • Open Government Data – Publishing and Consumption – Data Value Chains, Value Creation, Budgeting • Portal for publication and consumption of open data – Lowering of semantic data to shallower domain specific formats (RDB, CSV etc..) • RDF Visualisations and Recommendations
- 23. … what do we do at Bonn • Dataset Change Detection • Collaborative Authoring and Open Educational Content • Low-threshold agile methodology for collaborative vocabulary development • Mapping of AutomationML to RDF
Notes de l'éditeur
- there are various reasons why dataset should contain quality metadata convincing data consumers: is the published data fit to the user’s needs filtering datasets: if the publisher does not care about his data, then why should a consumer use it? Poor quality perspective: LD is a good use case for Veracity in Big Data, but it is often overlooked due to its poor quality perspective. If the big data community is convinced otherwise, LD might be used more often on bigger platforms. Therefore we have to start by assessing data quality and stamp our datasets in a machine readable format.
- Represent Quality Metadata in Named Graphs that can be attached to datasets CDM are abstract classes… these are only conceptual.. more concrete classes should be represented as sub-classes A dataset can be assessed multiple metrics. Each metric can be assessed over the dataset infinite times, each time the new value represented as an observation Each observation is also a Provenance Entity, enabling the representation of concepts such as the activity agent and how a metric was executed (for example parameter setting in reservoir techniques)
- The general architecture
- The processing workflow
- We identified the data quality lifecycle, which could be part of a bigger lifecyle like the LODStack or even to bigger more generic processes like data value chains Metric Identification and Definition – Choosing the right metrics for a dataset and task at hand; Assessment – Dataset assessment based on the metrics chosen Data Repairing and Cleaning – Ensuring that, following a quality assessment, a dataset is curated in order to improve its quality; Storage, Cataloguing and Archiving – Updating the improved dataset on the cloud whilst making the quality metadata available to the public Exploration and Ranking – Finally, data consumers can explore cleaned datasets according to their quality metadata
- our hypothesis is that probabilistic approximation techniques would drastically improve computational time when compared with the naïve implementations which gives 100% accurate results having said that the probabilistic techniques will still give a close to accurate results given the right parameter settings.
- Therefore to sum up the metrics using the Res Sampling techniques: The deref metric gave around 75% precision, whilst the order of magnitude can easily go over 2 with small datasets having 1M triples The links to External DP metric gave us 100% precision, whilst the difference in the time can be easily noticeable when datasets grow larger.
- From the results we saw that the precision was on average 97%, whilst the computational time takes more than 3 order of magnitude in most of the cases
- sum up