Text is the medium used to store the tremendous wealth of scientific knowledge regarding the world we live in. However with its ever increasing magnitude and throughput, analysing this unstructured data has become a tedious task. This has led to the rise of Natural Language Processing (NLP), as the go-to for examining and processing large amounts of natural language data. This involves the automatic extraction of structured semantic information from unstructured machine-readable text. The identification of these explicit concepts and relationships help in discovering multiple insights contained in text in a scalable and effective way. A major challenge is the mapping of un-structured information from raw texts into entities, relationships and attributes in the knowledge graph. In this talk, we demonstrate how Grakn can be used to create a text mining knowledge graph capable of modelling, storing, and exploring beneficial information extracted from medical literature. #### Syed Irtaza Raza, Software and Biomedical Engineer @ Grakn Labs Syed is a Software and Biomedical Engineer at Grakn, primarily working on introducing the world on how to use a knowledge graph such as Grakn to build cognitive/intelligent systems in the Biomedical domain. To achieve this, he is implementing innovative examples as templates and ideas for how clients and community members may apply in their own specific projects of any field. With a background in Electrical, Software and Biomedical Engineering, Syed’s mission is to discover and implement intelligent biomedical tools that are only possible with Grakn as a knowledge graph. This is a clip from the Grakn London Meetup at the Royal Academy of Engineering (March 2019). Join the community: www.grakn.ai/community

1. T H E K N O W L E D G E G R A P H
Follow our work at grakn.ai/community
@graknlabs
Text Mined Knowledge Graphs
By Syed Irtaza Raza
Software and Biomedical Engineer
@syedirtazaraza

2. Follow us @GraknLabs
What is Text Mining?

3. Follow us @GraknLabs
What is Text Mining?
Text mining is the automatic extraction of structured semantic information
from unstructured machine-readable text.

4. Follow us @GraknLabs
What is Text Mining?
Text mining is the automatic extraction of structured semantic information
from unstructured machine-readable text.

5. Follow us @GraknLabs
What is Text Mining?
Text mining is the automatic extraction of structured semantic information
from unstructured machine-readable text.
Text Mining

6. Follow us @GraknLabs
What is Text Mining?
Text mining is the automatic extraction of structured semantic information
from unstructured machine-readable text.
Text Mining

7. Follow us @GraknLabs
What is Text Mining?
Text mining is the automatic extraction of structured semantic information
from unstructured machine-readable text.
?Text Mining

8. Follow us @GraknLabs
What are the Challenges of Going Beyond Text Mining?

9. Follow us @GraknLabs
What are the Challenges of Going Beyond Text Mining?
Integration
Difficult to ingest and integrate
complex networks of text mined
output across bodies of text

10. Follow us @GraknLabs
What are the Challenges of Going Beyond Text Mining?
Integration Normalisation
Difficult to ingest and integrate
complex networks of text mined
output across bodies of text
Difficult to contextualise extracted
knowledge from text with existing
knowledge

11. Follow us @GraknLabs
What are the Challenges of Going Beyond Text Mining?
Integration Normalisation Discovery
Difficult to ingest and integrate
complex networks of text mined
output across bodies of text
Difficult to contextualise extracted
knowledge from text with existing
knowledge
Difficult to investigate insights
contained in text in a scalable and
efficient way

12. Follow us @GraknLabs
How can we Solve These Challenges?
Integration Normalisation Discovery

13. Follow us @GraknLabs
How can we Solve These Challenges?
Integration Normalisation Discovery
Ingest and integrate complex networks
of text mined output into one
collection – a knowledge graph

14. Follow us @GraknLabs
How can we Solve These Challenges?
Integration Normalisation Discovery
Ingest and integrate complex networks
of text mined output into one
collection – a knowledge graph
Impose an explicit structure on text
mined data to contextualise the
relationships with existing knowledge

15. Follow us @GraknLabs
How can we Solve These Challenges?
Integration Normalisation Discovery
Ingest and integrate complex networks
of text mined output into one
collection – a knowledge graph
Impose an explicit structure on text
mined data to contextualise the
relationships with existing knowledge
Use automated reasoning and
analytics to investigate and interpret
insights contained across text in a
scalable and efficient way

16. Follow us @GraknLabs
Integration Normalisation Discovery
Ingest and integrate complex networks
of text mined output into one
collection – a knowledge graph
Impose an explicit structure on text
mined data to contextualise the
relationships with existing knowledge
Use automated reasoning and
analytics to investigate and interpret
insights contained across text in a
scalable and efficient way

17. Follow us @GraknLabs
What Is Grakn?
GRAKN.AI is the knowledge base
foundation for intelligent systems
a.k.a.
“A KNOWLEDGE GRAPH”
Knowledge Storage System
Knowledge Inference Knowledge Analytics

18. Follow us @GraknLabs
How Do We Build A Text Mined Knowledge Graph?
1. Model and migrate text mined output into a knowledge graph – Grakn.
2. Discover and interpret new insights.

19. Follow us @GraknLabs
How Do We Build A Text Mined Knowledge Graph?
1. Model and migrate text mined output into a knowledge graph – Grakn.

20. Follow us @GraknLabs
Medical Text
Medical Literature
Electronic Health Records
Patient Medical History
Diagnostic Test Reports
Clinical Reports

21. Follow us @GraknLabs
Medical Text
Medical Literature

22. Follow us @GraknLabs
Text Mining/NLP Tools

23. Follow us @GraknLabs
Text Mining/NLP Tools

24. ~~~~~~~~~~
~~~~~~~~~~
~~~~~~~~~~
Mined-Text
What Does Text Mined Output Look Like?
…..

25. ~~~~~~~~~~
~~~~~~~~~~
~~~~~~~~~~
Mined-Text
Natural Language Processing
What Does Text Mined Output Look Like?
…..

26. ~~~~~~~~~~
~~~~~~~~~~
~~~~~~~~~~
Mined-Text
Natural Language Processing
~~~~~~~~~~
Sentence
~~~~~~~~~~
Sentence
~~~~~~~~~~
Sentence
What Does Text Mined Output Look Like?
…..
…..

27. ~~~~~~~~~~
~~~~~~~~~~
~~~~~~~~~~
Mined-Text
Natural Language Processing
~~~~~~~~~~
Sentence
~
Token
~
Token
~
Type
~
Type
What Does Text Mined Output Look Like?
…..
…..
…..
…..

28. ~~~~~~~~~~
~~~~~~~~~~
~~~~~~~~~~
Mined-Text
Natural Language Processing
~~~~~~~~~~
Sentence
~
Relation
~
Token
~
Token
~
Type
~
Type
~
Type
What Does Text Mined Output Look Like?
…..
…..
…..
…..

29. ~~~~~~~~~~
~~~~~~~~~~
~~~~~~~~~~
Mined-Text
Natural Language Processing
~~~~~~~~~~
Sentence
~
Relation
~
Token
~
Token
~
Type
~
Type
~
Type
What Does Text Mined Output Look Like?
Sentiment
…..
…..
…..
…..

30. ~~~~~~~~~~
~~~~~~~~~~
~~~~~~~~~~
Mined-Text
Natural Language Processing
~~~~~~~~~~
Sentence
~
Relation
~
Token
~
Token
~
Type
~
Type
~
Type
What Does Text Mined Output Look Like?
Sentiment
…..
…..
…..
…..
~
Confidence
~
Confidence
~
Confidence
~
Confidence

31. Relation
~~~~~~~~~~
~~~~~~~~~~
~~~~~~~~~~
Mined-Text
Natural Language Processing
Syed is proud to be the brother of Zainab.
Sentence
~ Zainab
Token
Syed
Token
Person
Type
Person
Type
Sibling
Type
1.0
Confidence
1.0
Confidence
0.9
Confidence
What Does Text Mined Output Look Like?
Sentiment
…..
…..
…..
…..
…..
0.9
Confidence

32. Follow us @GraknLabs
How Do We Model in Graql?
Entities
Entities

33. Follow us @GraknLabs
How Do We Model in Graql?
Entities
Entities

34. Follow us @GraknLabs
How Do We Model in Graql?
Entities
Relationships
Attributes
Entities
Entities

35. Follow us @GraknLabs
How Do We Model in Graql?
Entities
Relationships
Entities
Entities
Attributes
Attributes

36. What is Done Currently?

37. What is Done Currently?

38. What is Done Currently?

39. What Should be Done?

40. Follow us @GraknLabs
How Do We Migrate Data Into Grakn?
const query = await transaction.query (
“insert $t isa token, has lemma ” Syed”, has type ” person”; “
);
InsertQuery query = Graql.insert(
var(”t").isa(”token”).has(”lemma”, ”Syed”).has(”type”, ”person”)
);
query = transaction.query(
“insert $t isa token, has lemma ” Syed”, has type ” person”;”
)

41. Follow us @GraknLabs
How Do We Build A Text Mined Knowledge Graph?
2. Discover and interpret new insights.

42. Follow us @GraknLabs
How Do We Discover An Insight?
What knowledge is extracted
from a PubMed article?
Question

43. Follow us @GraknLabs
How Do We Discover An Insight?
What knowledge is extracted
from a PubMed article?
Question
=
Graql

44. Follow us @GraknLabs
~~~~~BRAF inhibitor Dabrafenib~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Trametinib and Dabrafenib treat Melanoma
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~MEK inhibitor Trametinib~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
drug
disease
protein
inhibition
treatment
gene
drug
treatment
inhibition

45. Follow us @GraknLabs
How Do We Discover An Insight?
Which PubMed articles
mention the disease
Melanoma and the gene BRAF?
Question

46. Follow us @GraknLabs
How Do We Discover An Insight?
Which PubMed articles
mention the disease
Melanoma and the gene BRAF?
Question
=
Graql

47. Follow us @GraknLabs
melanomaBRAF
article
article
article
article
article
…..
…..

48. Follow us @GraknLabs
How Do We Discover An Insight?
Give me a drug related to the
disease melanoma?
Question

49. Follow us @GraknLabs
How Do We Discover An Insight?
Give me a drug related to the
disease melanoma?
Question
=
Graql

50. Follow us @GraknLabs
DEMO…

51. Follow us @GraknLabs
How Do We Interpret An Insight?

52. Follow us @GraknLabs
How Do We Interpret An Insight?
Pubmed-article
titleabstractpmid
sentence
mined-
relation
treatment
tokentoken
melanomaDabrafenib
When

53. Follow us @GraknLabs
How Do We Interpret An Insight?
Pubmed-article
titleabstractpmid
sentence
mined-
relation
treatment
Dabrafenib
drug
melanoma
disease
treatment
tokentoken
melanomaDabrafenib
therapeutic
Treated-condition
When Then

54. Follow us @GraknLabs
How Do We Do Reasoning in Graql?

55. Follow us @GraknLabs
How Do We Do Reasoning in Graql?

56. Follow us @GraknLabs
How Do We Do Reasoning in Graql?
Pubmed-article
titleabstractpmid

57. Follow us @GraknLabs
How Do We Do Reasoning in Graql?
Pubmed-article
titleabstractpmid
sentence

58. Follow us @GraknLabs
How Do We Do Reasoning in Graql?
Pubmed-article
titleabstractpmid
sentence
mined-
relation
treatment
tokentoken

59. Follow us @GraknLabs
How Do We Do Reasoning in Graql?
Pubmed-article
titleabstractpmid
sentence
mined-
relation
treatment
tokentoken
melanomaDabrafenib

60. Follow us @GraknLabs
How Do We Do Reasoning in Graql?
Pubmed-article
titleabstractpmid
sentence
mined-
relation
treatment
tokentoken
melanomaDabrafenib
Dabrafenib
drug
melanoma
disease

61. Follow us @GraknLabs
How Do We Do Reasoning in Graql?
Pubmed-article
titleabstractpmid
sentence
mined-
relation
treatment
tokentoken
melanomaDabrafenib
Dabrafenib
drug
melanoma
disease
treatment

62. Follow us @GraknLabs
ENTITY ATTRIBUTE RELATIONSHIP
Architecture
Schema
Text Insights
Automated
Reasoner
Knowledge
Representation
System
Medical
literature
Clinical
Reports
Medical
History
NLP
Gene Cluster Identification
Protein Interactions
Gene-Disease Associations
Protein-Disease Associations
Drug-Disease Associations
Patient Matching
Clinical Decision Support
…..
…..
Migrator

63. Follow us @GraknLabs
Integration Normalisation Discovery
Ingest and integrate complex networks
of text mined output into one
collection – a knowledge graph
Impose an explicit structure on text
mined data to contextualise the
relationships with existing knowledge
Use automated reasoning and
analytics to investigate and interpret
insights contained across text in a
scalable and efficient way

64. Follow us @GraknLabs
What Else Can We Use Grakn For In Bio-Medicine?
Biological
Networks
Drug
Discovery
Gene
Therapy
Precision
Medicine
Drug
Repurposing
Antibiotic
Resistance
Patient
Management

65. Follow us @GraknLabs
Questions?
Text Mined Knowledge Graphs
By Syed Irtaza Raza