On https://analyticsanddatasummit.org/ 2020, we presented a use case for bootstrapping AI, automating the building of an annotated reference corpus for NER.

1. Bootstrapping Machine Learning using Knowledge Graphs
Semantic AI for
Named Entity Recognition
Sebastian Gabler
Director of Sales, Semantic Web Company
Robert David
CTO, Semantic Web Company

2. © Semantic Web Company 2020
Software Engineers &
Consultants for NLP,
Semantics and Machine
learning
Introducing Semantic Web Company
Founded in 2004
Based in Vienna
Privately held
Developer & Vendor of
PoolParty Semantic Suite
Participating in
projects with
€2.5 million
funding for R&D
SWC named to
KMWorld’s
‘100 Companies
That Matter in
Knowledge
Management’ in
2016, until 2019
60+ FTE
revenue growth/year
~25%
ISO 27001:2013
certified
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Raw data
Rules
Programs
Answers
Raw data
Answers
Machine
Learning
Raw data
Machine
Learning
Knowledge
Graph
Knowledge /
Answers
Explanations
Data
Programmatical
Approach
Machine
Learning
Knowledge
Graph
Basedon:KnowledgeGraphs-TheThirdEraof
ComputingbyDanMcCreary
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Towards self-optimizing machines
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Predictions
Rules

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Components and Features
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Make Sense - not Terms
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Austria’s capital, lies in the country’s east on the Danube
River. Its artistic and intellectual legacy was shaped by
residents including Mozart, Beethoven and Sigmund Freud.
The city is also known for its Imperial palaces, including
Schönbrunn, the Habsburgs’ summer residence. In the
MuseumsQuartier district, historic and contemporary
buildings display works by Egon Schiele, Gustav Klimt and
other artists.
Schaut man vom Kahlenberg auf die Donau
hinunter, kann man Wien mit allen Sinnen spüren.
Weinberge sind da zu sehen, dahinter glänzt das
bauliche Erbe der mitteleuropäischen Metropole.
Ein halbes Jahrtausend wurde hier
Weltgeschichte geschrieben. Kunstgeschichte
sowieso.

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Extraction of related terms and concepts
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Document
Corpus
▸ 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-occurrence between terms and concepts
Term 1
Term 4
Term 2

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Using Graph Embeddings for Fast Named Entity
Disambiguation
Character-Based Neural Networks for NLP in the Real World
Existing Work in vicinity
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▸ Training an entity recognizer requires a large annotated corpus.
▸ Machine learning uses manually annotated training data:
▹ Available for common entity types like Persons, Organisations, Places
▹ Not available for many specific knowledge domains
▹ We need a domain expert to do the annotations
The NER Challenge
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Complementary Approach
▸ The PoolParty Extractor allows thesaurus-based entity recognition
▹ limited to the known vocabulary
▹ text context-independent
▸ Statistical NER models allows text-based entity recognition
▹ provide open-vocabulary entity recognition
▹ primarily based on the text context
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▸ Have a well-curated knowledge model: MesH, diseases branch
▸ Have training data: PubMed Corpus
▸ Have working data: Find diseases mentioned in newspapers
▸ Have a benchmark: BioBERT
▸ Trending topic: Coronavirus disease (COVID-19) outbreak
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Use Case: Disease Annotator

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Disease Annotator Sample Application

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▸ Semantic AI Approach:
▹ Automatically annotate training data
▹ Use a structured domain knowledge model
▹ Apply the model to a corpus
▹ Use the result annotations as training data
▹ Train the entity recognizer
Semantic AI Approach
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Evaluating the approach - Data
▸ Knowledge Model: Medical Subject Headings (MeSH)
▹ Controlled vocabulary for life sciences
▹ Evaluation: Diseases branch
▸ Training Corpus: PubMed Corpus (100k sentences)
▸ Ground Truth: Manually annotated NCBI Disease corpus (BioBERT)
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Evaluation: Corpus folding I
▸ Compare NER with Thesaurus annotations
▹ PubMed corpus @ 80% training data + 20% test data
▹ Full MeSH diseases branch as model
▸ Results:
▹ Precision: 95.21% Recall: 91.62% F1: 93.38%
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Evaluation: Corpus folding II
▸ Compare NER with ground truth:
▹ NCBI Disease corpus - manually annotated
▹ NCBI Disease corpus - automatically annotated
▸ Results:
▹ Precision: 82.15% Recall: 74.66% F1: 78.22%
■ Compare with BioBERT @ 89% (F1) - 11%
■ Compare with OpenNLP on NCBI training set + 5%
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Evaluation: Thesaurus folding
▸ Compare NER with Thesaurus annotations
▹ MESH diseases branch @ 80% training data+ 20% test data
▹ PubMed corpus
▸ Evaluations:
▹ Compare with full knowledge model Extractor annotations
▹ Compare with hidden part of the folds
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Semantic AI Benefits
▸ Bootstrapping path for Named Entity Recognizer
▹ Automated building of annotated training corpus
▹ Useful for agile recognizer training (app 10 minutes / cycle)
▹ No manual annotation efforts required
▸ Improvement on (vocabulary-based) Concept Extraction:
▹ High-quality thesaurus candidates
▹ Improved disambiguation
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▸ Establish feedback loop
▸ Provide generic connectors (Oracle ML)
▹ Leverage architecture benefits
▹ Provide integrated solution
▸ Run PoCs for similar use cases
▹ Fictional characters (Audiovisual)
▹ Emerging news topics (Broadcast)
▹ ...
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Next steps...

20. © Semantic Web Company 2020
Impulse: Sebastian Gabler
Product Owner: Robert David
Project Management: Alexi Lopez-Lorca
NER bootstrapping process: Alexis Dimitriadis
NER process development: Sotiris
Karampatakis
Development: Guilherme Rodrigues &
Konstantin Dzekov
Demo Application: Juliane Pinero-Winkler &
Ali Marhubi
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Credits
Nine Persons, 7 Nations,
1 company

21. © Semantic Web Company 2020
Thank you!
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Q & A