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Data Integration across the Linked Data Lifecycle

Slide Deck

Making sense of your Data: Data Integration across the Linked Data Lifecycle

When integrating data into the wild, we witness a different kind of data, namely unstructured such as text, semi-structured such as XML, JSON, and structured data residing in the legacy relational databases. In the context of Big Data, it is always challenging to make sense of such a volume of heterogeneous data.

Semantic Web is the approach to tackle the problem of data integration by using the RDF layer on top, by either re-converting, i.e., re-materializing in a triple store, or by merely mapping them. RDF data is represented as a graph, which is closer to how human thinks and more agile in the perspective of data changes compared to other data models.

Taxonomies and ontologies as two pillars play a central role in this process of data integration in RDF. We explain the iterative model of data integration across Linked Data Lifecycle, where we dissect different phases such as: data ingestion, cleaning, authoring, linking, enrichment, provisioning and analysis.

We motivate each of the phases by showing real examples, demos or use cases, leveraging components of PoolParty Semantic Suite such as Taxonomy, Ontology management; PoolParty Extractor; PoolParty GraphEditor; PoolParty GraphSearch; PoolParty UnifiedViews.

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