Universal Publishing, Semantics, Data and Responsive FT

Jem Rayfield

Head of Solution Architecture

Agenda

• Financial Times?• Universal Publishing• Semantic Publishing• Ontology aware NLP and semantic disambiguation• API always first• Responsive semantic http://next.ft.com (ALPHA)• Recommendation, engagement and reach

The FT Audience: The world’s most wealthy and powerful people

“The old “page editor” is dead; long live today’s content editor”

Global network of over 500 journalists

The FT is where our readers wish to consume it

Universal Publishing Platform

Fundamentals of a Universal Publishing strategy…

Creating the right tools

Making the right things

Making the process efficient

Organised assets

Customer data

Good Metadata

Stop binding to platforms

API for services

Access and usage

Making Managing Exposing

This is what we are investing in…

Universal Publishing Platform

Stop being obsessed by individual platforms…

The game is much bigger than this…

Does my CSS look big in this?

Universal Publishing Platform

Think universally to avoid this…

Back end stuff

Database Stuff

Publishing stuff User stuff

Legacy stuff

Worrying stuff

Expensive stuff

Annoying stuff

Product

API First

API

Instead start your project like this…

Product

Back end stuff

Database Stuff

Publishing stuff User stuff

Legacy stuff

Worrying stuff

Expensive stuff

Annoying stuff

Dynamic Semantic publishing

USER EXPERIENCE

ONTOLOGY

TRIPLE STORE

Graph of Data

Ontology

Presentation

Annotate and Understand everything (Semantic fingerprint)

Go deep, model your domain. (Business) Collaboration: (FT, BBC, Reuters, Bloomberg, EuroMoney)

Open identifiers for storiesand newsevents

Ontology aware NLP and Semantic Disambiguation

RDF

Generic Analysis…

KB Gazetteer…………

Disambiguation………

Relevance Ranking

Some words about talking Apple the company. Also mentioning a person named Steve Jobs says a tweet on twitter

? Apple: Organization? Steve Jobs: CEO? ……….

V Steve Jobs: CEO- Steve Jobs:

football player- ……….

V Steve Jobs- …….- ……….

V Apple- …….- ……….

1. Apple (78%)2. Steve Jobs (69%)3. Twitter (58%)4. …

Curate

Update

Retrain & Adapt

ProtoDemoVideo?

Entity relevance

• Rank entities by their relatedness to the article

• Accuracy 80%+

• We consider various frequencies of entity mentions in the article and in the entire set of articles

• Positions in the article fields or in the first paragraphs of the body boost the relevance

Confidence and Relevance. How?

The relevance of an entity in arbitrary document may depend on:

Text context and the vicinity of an entity/concept within the text.

(Confidence)

Ontological graph context and the vicinity of an entity/concept within

the graphs knowledge model

The frequencies of entities in the corpus and document. (Relevance)

API, API, API……API

• APIs first

• APIs for everything (Content, Concept, Data)

• API a product?• Content Syndication to B2B partners

• Build multiple products on a single set of APIs• Flipboard• Google News Stand• http://next.ft.com• Etc…

APIsDemo?Video?

http://next.ft.com [ALPHA] - Responsive + Semantic

• Stream/Page per Concept, Page per company [2million+], Page per Person[1million+], Page per location etc..etc..

A lot of output Complex navigation

• Far too many pages/streams for far too few journalists

• UPP semantic annotation architecture automates content aggregation (articles, images, video, comment etc..)

• Time coded, metadata annotated, on demand video

NextFT

Recommendations

Serve relevant articles

to increase user engagement

and improve usability

Primary Objective

How Can We Increase Engagement?

By augmenting 1st party data with semantic data and applying related content we can increase engagement

1st Party Data

User’s Click Data

Engagement

Semantic Data Related Content Increased Engagement

How Do We Achieve This?

By using semantic data we achieve a more detailed view than FT’s 1st party data. This is due to exposing relationships between organisations and people. If an FT user exists in the semantic database we can utilise the additional data

FT 1st party data

Series1

1st Data Party

PeersCompetitorsColleaguesOrganisations

Sub-OrganisationsIndustryJob Role

Related Content

Series1

Increasing Reach

By increasing engagement we have a greater set of related content from which to build profiles. Also the semantic data provides a more granular view. This together with the relationships which semantic data surfaces allows for better content aggregation.

PeersCompetitorsColleaguesOrganisations

Sub-OrganisationsIndustryJob Role

Related Content

User’s Click DataCEOs in

Automotive Industry

User A Click Data

User C Click Data

User D Click Data

User B Click Data

Using this data we can create profiles e.g. CEOs in insurance. By recommending commonly consumed content to those users who share the same profile we can increase our reach.

Increased Engagement

Contextual Recommendations

User Behaviour Recommendation

Active Registered Users New Subscribers

Look for common patterns

of site usage

Target other registered users with

similar behaviours

Automated process that delivers ~15% of new subs per week with minimal CPA

Modelling subscription propensity

It’s just about listening to your audience

Behavioural Recommendations

Combined Contextual + Behavioural Recommendations

User Actions: Limited to User Reads Article

User Actions: A wider behavioural perspective

Data: User and Content metadata are crucial

Use of data…is absolutely crucialFocus on Data comes from…

First 120 years of FT little user data

Now we are 125 years old…

…we know better

Thank you. Questions?

Jem.rayfield@ft.com @jemrayfield