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 unstructured 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.

1. Tomás Sabat Stöfsel
Text Mined Knowledge Graphs
Welcome everyone! Please leave your name and where you’re calling in from
in the chat.

2. What is Text Mining?
Twitter: @GraknLabs

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

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

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

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

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

8. What are the Challenges of Going Beyond Text Mining?
Twitter: @GraknLabs

9. 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
Twitter: @GraknLabs

10. 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
Normalisation
Difficult to contextualise extracted
knowledge from text with existing
knowledge
Twitter: @GraknLabs

11. 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
Normalisation
Difficult to contextualise extracted
knowledge from text with existing
knowledge
Discovery
Difficult to investigate insights contained
in text in a scalable and efficient way
Twitter: @GraknLabs

12. How Can we Solve These Challenges?
Twitter: @GraknLabs

13. How Can we Solve These Challenges?
Integration
Ingest and integrate complex networks of
text mined output into one collection – a
knowledge graph
Twitter: @GraknLabs

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

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

16. Grakn is the knowledge base
foundation for intelligent systems
a.k.a.
a Knowledge Graph
Knowledge Storage System
Knowledge Inference Knowledge Analytics
Twitter: @GraknLabs

17. Articles, papers, etc
High-Level Architecture
NLP
Graql

18. 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.
Twitter: @GraknLabs

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

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

21. Medical Text
Medical Literature
Twitter: @GraknLabs

22. Text Mining/NLP Tools
Twitter: @GraknLabs

23. Text Mining/NLP Tools
Twitter: @GraknLabs

24. ~~~~~~~~~~
~~~~~~~~~~
~~~~~~~~~~
Mined-Text
What Does Text Mined Output Look Like?
…..
Twitter: @GraknLabs

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

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

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

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

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

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

31. Relation
~~~~~~~~~~
~~~~~~~~~~
~~~~~~~~~~
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
Sentiment
…..
…..
…..
…..
…..
0.9
Confidence
Twitter: @GraknLabs
What Does Text Mined Output Look Like?
Natural Language Processing
Mined-Text

32. What is Done Currently?
Twitter: @GraknLabs

33. What is Done Currently?
Twitter: @GraknLabs

34. What is Done Currently?
Twitter: @GraknLabs

35. What Should be Done?
Twitter: @GraknLabs

36. How Do We Model in Graql?
Twitter: @GraknLabs

37. How Do We Model in Graql?
Twitter: @GraknLabs
Entities

38. How Do We Model in Graql?
Relationships
Twitter: @GraknLabs
Entities

39. How Do We Model in Graql?
Relationships
Attributes
Twitter: @GraknLabs
Entities

40. 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”;”
)
Twitter: @GraknLabs

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

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

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

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

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

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

47. Twitter: @GraknLabs
BRAF
article
article
article
article
article
melanoma
…
…

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

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

50. DEMO…
Twitter: @GraknLabs

51. How Do We Interpret An Insight?
Twitter: @GraknLabs

52. How Do We Interpret An Insight?
titleabstractpmid
sentence
treatment
melanomaDabrafenib
When
Twitter: @GraknLabs
Pubmed-article
mined-
relation
token token

53. How Do We Interpret An Insight?
disease
therapeutic
treated-condition
Then
Twitter: @GraknLabs
When
titleabstractpmid
sentence
treatment
melanomaDabrafenib
Pubmed-article
mined-
relation
token token
treatment
Drug: Dabrafenib
Disease: Melanoma

54. How Do We Do Reasoning in Graql?
Twitter: @GraknLabs

55. How Do We Do Reasoning in Graql?
Twitter: @GraknLabs

56. How Do We Do Reasoning in Graql?
Twitter: @GraknLabs
titleabstractpmid
Pubmed-article

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

58. How Do We Do Reasoning in Graql?
Twitter: @GraknLabs
titleabstractpmid
sentence
treatment
Pubmed-article
mined-
relation
token token

59. How Do We Do Reasoning in Graql?
Twitter: @GraknLabs
titleabstractpmid
sentence
treatment
melanomaDabrafenib
Pubmed-article
mined-
relation
token token

60. How Do We Do Reasoning in Graql?
Twitter: @GraknLabs
titleabstractpmid
sentence
treatment
melanomaDabrafenib
Pubmed-article
mined-
relation
token token
Disease: Melanoma Drug: Dabrafenib

61. How Do We Do Reasoning in Graql?
Twitter: @GraknLabs
Disease: Melanoma Drug: Dabrafenibtreatment
titleabstractpmid
sentence
treatment
melanomaDabrafenib
Pubmed-article
mined-
relation
token token

62. Articles, papers, etc
High-Level Architecture
NLP
Graql

63. What makes Grakn a Knowledge Base for Text Mining?
Integration
Ingest and integrate complex networks of
text mined output into one collection – a
knowledge graph
Normalisation
Impose an explicit structure on text
mined data to contextualise the
relationships with existing knowledge
Discovery
Use automated reasoning and analytics
to investigate and interpret insights
contained across text in a scalable and
efficient way
Twitter: @GraknLabs

64. Thank you for attending this webinar!
Follow us on:
@graknlabs
Tomás: @tasabat
Daniel: @meanwhile_inn
Join our chatroom on:
https://discord.gg/graknlabs