IC-SDV 2018: Aleksandar Kapisoda (Boehringer) Using Machine Learning for Automatic Classification of Companies

Using Machine Learning for Automatic
Classification of Companies
IC-SDV 2018
Nice, France April 23rd, 2018
Aleksandar Kapisoda

• General Information
• Project Update
• Deeping the SEARCHCORPUS
• How to improve?
• What is What?
• Approaches, Learnings & Results
• Next steps
• Conclusions
Content
IC-SDV 2018. Aleksandar Kapisoda
Using Machine Learning for Automatic Classification of Companies

General Information
• Family-owned global corporation
• Founded 1885 in Ingelheim, Germany
• Focus on Human pharmaceuticals, Animal health and
biopharmaceutical contract manufacturing
• Around 45,700 employees worldwide
• Four R&D sites worldwide
• R&D expenditure of EUR 3.1 billion
• 17 production facilities (human pharmaceuticals) in
11 countries
• Net sales of around EUR 15.9 billion
• 143 affiliated companies worldwide
• Investment in tangible assets: EUR 645 million
Status: 31.12.2016
Boehringer
Ingelheim Center
Our headquarter
in Ingelheim

General Information
Scientific
Information
Center
• 1960 Central Library
• 1990 Scientific Information Services
• 2003 Scientific Library
• 2006 Scientific Information Center
Scope: Access to Knowledge
MainTasks:
• Global acquisition of external data
(scientific databases, literature)
• Scientific Information Sources & Consultanty

Project Update Biotech SEARCHCORPUS®
Project Update
&
Status Quo
2015: Collecting company URLs
Quickly collected 80.000+ companies but also “unwanted”
targets like beauty farms, pharmacies..
2016 & 2017: Drastic improvement of data quality
Relevance Filtering
(Tagging with domain Ontologies &Taxonomies)
2018: Focusing the Search Space
Classification of the Search Space
Today > 45.000 biotech companies in 140 countries with more
than 8.5 Mio. Web pages
Usage:
As the word about the Biotech Company SEARCHCORPUS®
spreads within BI we get new diverse research targets:
• Competitive intelligence
• Business development & Licensing

Deeping the SEARCHCORPUS
https://adexchanger.com/managing-the-data/deepening-data-lake-second-party-data-increases-ai-enterprises/
Data Lake is a storage repository that holds a vast amount of data until it is needed
Marketers with sparse data often
do not have enough data to create
measureable outcomes in
audience targeting through
modeling. Source: Chris O’Hara

SEARCHCORPUS is our storage repository that holds a vast amount of crawled data until it is needed
2015 - 2017
In our SEARCHCORPUS we too
much data to create measureable
outcomes in audience targeting
the right company.

Deeping the SEARCHCORPUS – Data Lake
Second-party data is simply
someone else’s first-party data.
When relevant insights for is
added to a data lake, the result is a
more robust environment for
deeper data-led insights for both
targeting and analytics. Source:
Chris O’Hara.

Second-party data is in our case
an Information Scientist who has
domain expertise, she owns the
relevant insights. This
environment is more robust for
deeper data-led insights for
targeting the relevant company.
2018

Using the domain expertise
• Question:
What ist the Question?
• Content:
Which information wants our internal customer?
• Search:
What are the right keywords?
Where to search?
• Results
with the relevant insights
Information
Scientist

Status Quo
• Actually we have in our SEARCHCORPUS too much
data to create measureable outcomes in audience
targeting the right company.
• Big Pharma, Biotech Companies, CRO, Digital Health,
Life Science & University
Challenge:
Deeper data-led insights for targeting the relevant company.
.
Look for specific types of businesses
e.g.: Distinguish CROs from R&D startups
Status Quo
&
Challange

Focusing the Search Space
• We are looking for a company that licenses its process
• We are not looking for a service provider who could
produce a drug for you according to your procedure
(which you do not have).
• 40% CROs in this case wrongly positive.
• We exclude all unsuitable business models and we
gain 100% less false positives.
• This reduces the search time or optimizes the quality
of the results
GoalSearch Space
.

Improve significantly the Search Precision
in Biotech Company SEARCHCORPUS®
An abstract classification of the sources
e.q. Business Model (CRO)
Goal

How to improve the Search Precision?
https://sipmm.edu.sg/how-artificial-intelligence-revolutionize-procurement/

What is What?
Artificial Intelligence (AI):
Machines in-built with Approach to achieve
Machine Learning (ML):
Machines with Human Brain Human Intelligence
Support Vector Machines
Deep Learning (DL):
Techniques to train the Machine’s Brain
Neural Networks are the foundation for DL
https://blogs.systweak.com/2016/12/artificial-learning-machine-learning-and-deep-learning-know-the-difference/

Approaches, Learnings & Results
https://blogs.systweak.com/2016/12/artificial-learning-machine-learning-and-deep-learning-know-the-difference/
Classification
based on
website
structure
Deep Learning
Machine
Learning
Feed
Forward
Neural
Networks
Recurrent
Neural
Networks
Support
Vector
Machine
Choosing the training Algorithms
Training
Algorithms

Corporate Standard Presentation 2017
Preparing the ground for Learning
Creating learning set:
Our Information Specialist categorized 50 company
websites into a small training sets
Focus areas:
• Big Pharma
• Biotech
• CROs
• Digital Health
• Life Science
• University
Training set

Artificial Neural Networks - A Pathway to Deep Learning
http://adventuresinmachinelearning.com/neural-networks-tutorial/
Artificial Neural Networks are computing systems
vaguely inspired by the biological neural networks
that constitute animal brains. Such systems "learn"
tasks by considering examples, generally without
task-specific programming.
Artificial Neural Network as a Black Box:
Deep Learning
Artificial Neural Networks

Support Vector Machine – Automated Classification & Clustering
In machine learning, support vector machines which are
supervised learning models with associated learning
algorithms that analyze data used for classification and
regression analysis.
Word2vec is an efficient algorithm which uses a simple neural
network for unsupervised learning of word embedding on a
large, unlabeled corpus.
How does Automated Classification & Clustering works?
Consists of diving the items that make up a collection into
categories or classes
The goal is to accurately predict the target class for each
record in new data.
https://groups.google.com/forum/#!topic/gensim/EwK-6JgkWVI
Machine
Learning
Support
Vector
Machine

• Businesses of the same type may have very small (1 page) or
very large websites (1000s of pages)
• Looking at the link structure as an image does not reveal
anything about the type of business
Result:
• Our crawlers know the structure of the company websites
• Does the website structure reflect the type of business?
(Then we could e.g. use image classification algorithms)
Approach 1
Classification based on website structure
Classification
based on website
structure

• Recognition rates not satisfying
• Training too expensive (lots of nodes due to large input vector)
Result:
• Feed Forward Neural Networks andTerm Frequency
For the training of the Neural Network we used two-layered
Backpropagation networks, which are classic FFNN classifiers for
non-linear problems.
For this approach we converted the input data into a vector
using aTF-IDF1)
preprocessor trained on our large corpus.
1)
Term frequency – inverse document frequency identifies important terms by setting the number of
occurrences of a term in a document in relation to the number of documents in the corpus that contain
this term.
Apporach 2
Feed Forward Neural Networks
Deep Learning

• No conversion, because of small training sets (50 samples
only)
• Recognition rate not satisfying
Result:
• Recurrent Neural Networks are good on learning sequences
RNNs can make decisions based on the text that is converted
into an input vector (Word2Vec / Doc2Vec) and takes the
sequence of the words into account, thus allowing to find
patterns in the content, memorize them and bind them to
specific classes.
Apporach 3
Recurrent Neural Networks forText Classification
Deep Learning

• For all 6 real world samples we got > 96% average recognition
rate
• Preparation and training is easy enough for data scientists to
create new classes on the fly without programming effort
Result:
SVMs can create non-linear classifiers by transforming the input
space onto a high dimensional one.Therefore they offer a good
compromise between complexity and performance
To obtain a reasonably sized input vector (remember, we classify
a whole website which may have several 100 MB of content), we
use the preprocessor from our FFNN approach with some magic.
Final Approach
Support Vector Machine with Normalized Input
Machine
Learning

Next Steps
Integration Optimization More Sources Further development
The verified approach for
website classification
based on Support Vector
Machines will be
integrated into the Deep
SEARCH 9® development
environment.
The Biotech
SEARCHCORPUS® will be
further optimized by
dynamically deviding the
search space into clusters
that reflect the focus of all
current research targets.
Additional sources will be
crawled as new websites
can be classified before
they are added to the
SEARCHCORPUS®.
The classification
approach will be further
developed with the goal to
recognize companies
developing technologies
not being monitored so
far.

Conlusions

Acknowledgements
Klaus Kater
Developing Partner
Deep Search 9
Dr. Gabriele Becher
Boehringer Ingelheim Pharma GmbH & Co. KG

Contact Information
Aleksandar Kapisoda
aleksandar.kapisoda@boehringer-ingelheim.com
Discovery Research Coordination – Scientific Information Center

Thank You!
Questions?
For more information have a look at:
www.boehringer-ingelheim.com
www.opnme.com
© Boehringer Ingelheim International GmbH 2017

IC-SDV 2018: Aleksandar Kapisoda (Boehringer) Using Machine Learning for Automatic Classification of Companies

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IC-SDV 2018: Aleksandar Kapisoda (Boehringer) Using Machine Learning for Automatic Classification of Companies