Andreas BlumauerCEO & Managing Partner

Semantic Web Company / PoolParty Semantic Suite

Semantics as the Basis of Advanced Cognitive Computing

(with a focus on Cognitive Search & Analytics)

1

Introduction

2Semantic Web

Company

founder & CEO of

Andreas Blumauer

developer and vendor of

2004founded

6.0

current Version

active at

based on

Vienna

located

part of EnterpriseKnowledge Graphs

manages

standard for

part of

enriches

>200serves customers

editor of

Taxonomies

is about

Ontologies

standard for

graduates

A quick question at the beginning

Will Artificial Intelligence make Subject Matter Experts obsolete?

3 Imagine you want to build an application that helps to identify wine and cheese pairings.

Which performs best?

Applications solely based on machine learning, those ones which are based on experts' knowledge only, or a combination of both?

Another question at the beginning

Will Artificial Intelligence make Subject Matter Experts obsolete?

4 Imagine you want to build an application that helps to identify patients and treatments pairings.

Which will you prefer?

Applications solely based on machine learning, those ones which are based on doctors' knowledge only, or a combination of both?

A key assumption of this talk

Effectiveness of cognitive systems is limited by the machines’ current inability to explain their decisions and actions to human users.

5

From: David Gunninghttps://www.darpa.mil/

Explainable Artificial Intelligence (XAI)

Another key assumptionof this talk

People do not search for documents only, they seek facts about things and smaller chunks of information.

Machines shall help to create links across data silos to give answers to questions.

6

Converging AI Technologies

What makes a system a cognitive system?

▸ Adaptive. Cognitive systems learn through their interactions with data and humans. They may resolve ambiguity and tolerate unpredictability.

▸ Contextual. Cognitive systems are capable of extracting relevant information from big and diverse data sets for users in their (work) context.

▸ Iterative. Systems may interact easily with users so they can define their needs comfortably, even if a problem statement is ambiguous or incomplete. They may also interact with other processors, devices, and cloud services, as well as with people.See also: https://cognitivecomputingconsortium.com/

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How Semantic Computing and Machine Learning complement each other

8Structured Data

Machine Learning

Cognitive Applications

How Semantic Computing and Machine Learning complement each other

9Unstructured Data

Structured Data

Machine Learning

Cognitive Applications

How Semantic Computing and Machine Learning complement each other

10Unstructured Data

Structured Data

Knowledge Graphs

Machine Learning

Cognitive Applications

Four-layered Information Architecture

11

Towards a Digital Twin

Proposal for a Cognitive Computing Platform Architecture

12Unstructured Data

Structured Data

Knowledge Graphs

Machine Learning

Semantic Layer

IoT & Cognitive Applications

Digital Twin

A digital twin continuously learns and updates itself from multiple sources to represent their near real-time status, working condition or position. This learning system learns

▸ from itself using sensor data that conveys various aspects of its operating condition

▸ from human experts, such as engineers with deep and relevant industry domain knowledge

▸ from other similar machines, and ▸ from the larger systems and environment in which it may be a

part of. From: Wikipedia

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Use Case #1The Wine & Cheese Recommender

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How to overcome the knowledge acquisition bottleneck?

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Knowledge Domain

Knowledge Modellers

Knowledge Model

semantic gap

Domain Experts

The idea

Build a Graph-based Recommender Systems and use Semantic Web Standards

16 Dry

Medium-bodied

High acidity

Weingut Weinrieder

Grüner Veltliner

Alte Reben

is characterized by

Nutmeg

Full-bodied

Warm finish

Tobacco

is characterized by

Nagelkaas

Cumin

Clove

Hard cheese

Higher fat

?is characterized by

matches

matches

does not match

The result

A scalable and configurable application based on an enterprise semantic platform: PoolParty GraphSearch

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Use Case #2Make use of Shadow Concepts

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Bionics

How does nature go around similar learning bottlenecks?

19 Bla bla bla bla. Bla bla bla bla

The stove is on. The stove is hot!

Ontological model → reasoningTaxonomical model → is-a abstractions

Bla stove bla bla. Bla bla bla hot

Switched on devices are dangerous devices.

Switched on devices are dangerous, only if the operating temperature is above 100 degrees and the automatic shutdown mechanism is broken.

The stove is on. The stove is hot!

Statistical model/cooccurences → is related

The stove is on. The stove is hot!

Bla bla bla bla Bla bla bla bla.

Graphs + Machine Learning

PoolParty as a supervised learning system

20Content Manager

Integrator

Taxonomist/Ontologist

ThesaurusServer

Extractor

PowerTagging

uses API

is user of

is user of

is basis of

is basis of

Index

annotates

enriches

Corpus Learning/ Semantic Analysis

CMS

extends

is basis of

analyzesuses API

proposesextensions

Co-occurence model

21ReferenceCorpus

- Websites- PDF, Word, …- Abstracts from

DBpedia- RSS Feeds

Term 8

Term 3

Term 7

Term 8

Term 6

Term 9

Term 5

Term 10

- Relevant terms and phrases- Relevancy of terms- co-occurence between terms and terms

Term 1

Term 4

Term 2

Shadow Concepts

Use co-occurences between concepts and terms to extract ‘shadow concepts’

22 This site is a 15th-century Inca site located 2,430 metres above sea level. It is located in Cusco, Peru.

It is situated on a mountain ridge above the Sacred Valley through which the Urubamba River flows. Most archaeologists believe that it was built as an estate for the Inca emperor Pachacuti. Often mistakenly referred to as the "Lost City of the Incas", it is the most familiar icon of Inca civilization. The Incas built the estate around 1450, but abandoned it a century later at the time of the Spanish Conquest.

Inca site

Machu Picchu

CuscoInca

empire

Inca emperor

Peru

Spanish Conquest

Sacred Valley

Chankas

Lost City

Pachacuti

In addition to explicitly used concepts and terms, Machu Picchu is extracted from the article as a Shadow Concept. As a prerequisite, one has to provide and analyze a representative text corpus first.

Example:

Use Shadow Concepts to improve Recommender Systems

23Mini Countryman

And it’s probably more of a crossover than ever, with the design to match, Being a Mini, the Countryman is clearly meant to be the driver’s car among small crossovers. The suspension is sophisticated, and there are lots of chassis options (a stiffer sports setup, variable damping, the electronically controlled ALL4 all-wheel-drive).

But it’s also the crossover for people who’ve bags of cash to blow on personalisation and luxury.

There’s been a lot of effort on ramping up the cabin quality, but then the outgoing Countryman was a sad let-down in that department.

On the outside, plastic wheel-arch extensions, with eyebrow creases in the metalwork above, as well as roof bars and sill protectors all add to the visual crossover-ness. This remains the only Mini with angular rather than oval headlamps, and there’s a load of visual posturing going on in the lower face.

There are eight versions at launch, and they’re exactly what you’d expect. It’s Cooper or Cooper S, each fuelled by petrol or diesel, each of them with front drive or ALL4. Oh and an eight-speed auto, too, if you count that as a separate choice. The Cooper petrol is a three-cylinder, the rest fours.

You get extra kit as standard versus the old car, including navigation, Bluetooth, emergency call and park sensors. Upgrades include a bigger touch-screen nav with high-definition traffic, various posher seats, a HUD, and driver aids. Oh and a cushion thingy that folds out from the boot so you can sit on the rear bumper without getting your clothes mucky.

In June 2017 a Cooper E will launch, which has the Cooper three-cylinder petrol driving the front wheels, and an electric motor for the rears, with a capacity to do a claimed 25 miles of gentle all-electric running. So it has the performance of a Cooper S ALL4 with the tax-busting advantages of a plug-in hybrid. And you wouldn’t use any fuel if you commuted a short distance.

The platform is BMW’s contemporary transverse-engined hardware, in the bigger of its two sizes. That means it shares a lot with the BMW X1. The 4WD system is more sophisticated than the previous Countryman’s. The proportion of drive to the rear is computed by a controller that takes into account parameters including grip, steering angle and throttle position, as well as whether you’ve got the sports mode and sports traction systems selected.

Use Case #3Improving Classification Algorithms

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Use Semantic Knowledge Models to improve Document Classifiers

25Prof. Farhad

Ameri

Engineering Informatics

works at

researches

We observed 10-20% improvement in precision of the classification process as a result of using a semantic thesaurus.

ClassificationAlgorithms

improves

uses

Supplier classification framework

develops

A Thesaurus-guided Text Analytics Technique

for Capability-based Classification of

Manufacturing Suppliers

CIE/SEIKM Best Paper 2017

publishes

Infoneer’s Manufacturer Classification Framework (MCF)

26

Use Case #4A Perfect Wedding based on AI

27

How knowledge acquisition methods play together?

28 Natural languages

Taxonomies

Schemas/O

ntologiesStat

istic

al m

odel

s

Computational Linguists

Taxonomists

Data

Sci

entis

ts Ontologists

Why ‘The Knot’ uses Machine Learning

29▸ Vendor similarity ▸ Vendor matching ▸ Image similarity ▸ Reverse image search▸ Image tag generator (auto-classification)▸ Recommendations ▸ Card sort user response analysis ▸ Style predictor

▸ XO Group runs ‘The Knot’ since 1996▸ NYSE: XOXO (S&P 600 Component)▸ 1.5 million active members▸ The Knot has helped marry 25 million couples▸ Partnering with 250,000 wedding vendors ▸ Millions of vendor reviews

The Learning Curve

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▸ To understand▹ Content aboutness in a defined framework▹ Data relationships and context within a

unified organizational model ▹ Connections across disparate datasets

▸ To increase precision▹ Hierarchical or other mapped relationships allow for

recommending similar content when exact matches not found▹ Granularity allows for more specific recommendations▹ Consistency across structure results more precise analysis and

predictions

Source: Suzanne Carroll, Data Science Product Director at XO Group

Why Data Scientists need Semantic Models

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Thank you for your interest!

Andreas BlumauerCEO, Semantic Web Company

▸ Mail andreas.blumauer@semantic-web.com▸ Company https://www.semantic-web.com ▸ LinkedIn https://www.linkedin.com/in/andreasblumauer▸ Twitter https://twitter.com/semwebcompany ▸ Blog https://www.linkedin.com/today/

author/andreasblumauer

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