GraphDB as Company Data Central

The recording of this webinar is available on YouTube.

Smarten up company data using FIBO as a semantic model and external data from GLEI, LCC and Wikidata. How GraphDB can help you create a graph model of your data and enrich it with reference data, signals from unstructured sources, records from transactional sources.

Scenario:

1. FIBO (example for data model): Ingest Set Autocomplete, Show EDA visualizations Create semantic indices (text and graph) Demo graph similarity;
2. F500 data (example for custom data): Ingest F500 CSV via OntoRefine Reconciliation against WikiData Reconciliation against GLEI Query to create sameAs between GLEI and WikiData ids Create F5002FIBO mappings via the interface SPARQL query to generate mapping suggestions on the basis of text similarity between column name and FIBO class;
3. Text analytics (Signals from unstructured content): Use Metadata studio or via GraphDB WB Register Tag CES Google NLP Send a couple of lines of content to both services and review and save annotations. View the event/topic annotations linked to the Facebook record (linked by Wikidata ID);
4. Link to pricing data (Virtualization demo): Review mapping sendс a query showing the price change as a result of the event identified in the content, related to Facebook above Setup/Prerequisites.
5. GLEIF data – separate repo Pricing data – the end-of-day share price (linked by ticker) stored in a relational database. Content sample – I am thinking of the example of the falling share price of Facebook.

Who is this webinar for:

• Information and Solution Architects who working on an enterprise knowledge graph for the key data assets.

Expected duration:

• 45 minutes presentation
• 15 minutes Q&A session