Semantic Enrichment ARMA Chicago Spring Seminar April 18, 2018

Transcription

1 Semantic Enrichment ARMA Chicago Spring Seminar April 18, 2018

2 Presentation Overview What is Semantics? Semantic Building Blocks Why Use Semantic Technology Semantic Layers Source of Semantic Information Semantics in Search, IG, Knowledge Management Semantic Use Cases

3 What is Semantics? You use it everyday...

4 What is Semantics? You use it everyday...

5 What is Semantics? You use it everyday...

6 Semantic technology is a set of methods and tools that provide advanced means for categorizing and processing content and data, as well as for discovering relationships within varied data sets.

7 Semantic Example Apple CEO, Tim Cook, Introduces 2 New, Larger iphones, Smart Watch at Cupertino Flint Center Event Apple CEO, Tim Cook, Introduces 2 New, Larger iphones, Smart Watch at Cupertino Flint Center Event Location Organization Person

8 Change your perception that we need to think beyond documents and think of content as data

9 The core difference between Semantic Technologies and other technologies for data, the relational database for instance, is that the Semantic Technology deals with the meaning rather than the structure of the data.

10 Presentation Overview What is Semantics? Semantic Building Blocks Why Use Semantic Technology Semantic Layers Source of Semantic Information Semantics in Search, IG, Knowledge Management Semantic Use Cases

11 3 Building Blocks OWL (Web Ontology Language) Triple Store Graphs SPARQL

12 OWL Modeling Language Ontology RDF Triple

13 RDF Triples & SPARQL Triples :Subject :Predicate :Object John livesin London IsIn England Semantics is a way to model data using a format called RDF and query it with the SPARQL query language. It adds contextual meaning around the data so it can be better understood, searched, and shared, enabling both people and computers to see and discover relationships in the data.

14 KEVIN BACON EXAMPLE

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16 The Oracle of Kevin Bacon

17 OWL, Graphs & SPARQL John livesin London IsIn England Inference!!!

18 3 Building Blocks OWL (Web Ontology Language) Triple Store Graphs SPARQL

19 Presentation Overview What is Semantics? Semantic Building Blocks Why Use Semantic Technology Semantic Layers Source of Semantic Information Semantics in Search, IG, Knowledge Management Semantic Use Cases

20 Mommy, Where Do Triples Come From? 1. The World at Large Linked Open Data DBpedia GeoNames

21 Mommy, Where Do Triples Come From? 1. The World at Large Linked Open Data DBpedia GeoNames 2. Facts from your domain Proprietary company data An industry-wide ontology such as FIBO

22 Mommy, Where Do Triples Come From? 1. The World at Large Linked Open Data DBpedia GeoNames 2. Facts from your domain Proprietary company data An industry-wide ontology such as FIBO 3. Facts & document Document metadata (author, publish date, source, etc.) Entities and events in free-flowing text

23 RDF: Distributed Graph Data attends Dean Allemang knows wrote Semantic Web for the Working Ontologist Sem Web Meetup knows speaking at SemTechBiz speaking at John Snelson likes attends speaking at works for Company ABC works for Stephen Buxton

24 RDF: Distributed Graph Data Dean Allemang attends Sem Web Meetup meetup.com attends Stephen Buxton

25 RDF: Distributed Graph Data Dean Allemang linkedin.com knows John Snelson works for Stephen Buxton works for Company ABC

26 RDF: Distributed Graph Data Dean Allemang wrote Semantic Web for the Working Ontologist workingontologist.com

27 RDF: Distributed Graph Data Dean Allemang knows Semantic Web for the Working Ontologist likes facebook.com John Snelson

28 RDF: Distributed Graph Data Dean Allemang speaking at SemTechBiz speaking at John Snelson Stephen Buxton speaking at semanticweb.com

29 RDF: Distributed Graph Data attends Dean Allemang knows wrote Semantic Web for the Working Ontologist Sem Web Meetup attends knows Stephen Buxton speaking at SemTec hbiz speaking at works for speaking at MarkLogic John Snelson works for likes meetup.com linkedin.com workingontologist.com facebook.com semanticweb.com

30 Presentation Overview What is Semantics? Semantic Building Blocks Why Use Semantic Technology Semantic Layers Source of Semantic Information Semantics in Search, IG, Knowledge Management Semantic Use Cases

31 Why Use Semantic Technology Auto-recognition of topics and Concepts Information and Meaning Extraction Categorization

32 Presentation Overview What is Semantics? Semantic Building Blocks Why Use Semantic Technology Semantic Layers Source of Semantic Information Semantics in Search, IG, Knowledge Management Semantic Use Cases

33 Building a Semantics Layer A semantic layer is a business representation of corporate data that helps end users access data autonomously using common business terms. A semantic layer maps complex data into familiar business terms such as product, customer, or revenue to offer a unified, consolidated view of data across the organization.

34 Auto-Recognition Example

35 Auto-Recognition Example

36 Auto-Recognition Example

37 WorkLight Example

38 Presentation Overview What is Semantics? Semantic Building Blocks Why Use Semantic Technology Semantic Layers Source of Semantic Information Semantics in Search, IG, Knowledge Management Semantic Use Cases

39 Search, IG, Knowledge Management Semantic Annotation Advanced Search Relationship Discovery

40 Semantic Annotation Data about Data People, Things, Places, Organizations, Transactions Disambiguation

41 Intelligent Search Intelligent Thesaurus Systemitize Tribal Knowledge FIBO Vocabulary What it is not Where it is

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43 RhinoDox Solution

44 Presentation Overview What is Semantics? Semantic Building Blocks Why Use Semantic Technology Semantic Layers Source of Semantic Information Semantics in Search, IG, Knowledge Management Semantic Use Cases

45 Legal: Intelligent Search Example Background: A Fortune 500 Chemicals and Water Treatment Company acquired a smaller New York based company in January Apart of the acquisition plan, the parent company had to inventory the new company s 8,000+ contracts to determine if legionella language was included in the terms and conditions, so they could evaluate the contract risk. Solution: Develop a custom ontology for legionella language. Legionella can also be expressed as Legionnaire s Disease or pneumophila. A semantics tool will analyze all the contracts and extract the terms that are used to describe Legionella. This comparison can be done for any type of data extraction.

46 Insurance: Intelligent Search Example Background: A policy holder filed a claim under her married name Julia Huang. A claims adjuster needs to validate that information provided is correct. However, looking in the system the adjuster can t find any policies issued under Julia Huang. In order to find the correct policy, the adjuster reaches back out to the user to verify her personal information only to find out that the policy is under her maiden name Tran. Solution: Leverage a semantics tool that extracts data from policies and claims. Using graph database technology, the system will automatically correlate Julia Huang to Julia Tran based on additional policy holder information, like address. A semantics tool can infer the claim filed and the policy in system are related to the same person allowing the adjuster to process claims faster and drive higher customer satisfaction.

47 Thank You!