Unstructured Text in Big Data: the Elephant in the Room David Milward (Linguamatics, UK) A traditional approach has been to extract structured data from the unstructured text. When it comes to big data, manual or semi-automated methods just don't scale. Even with fully automated methods, it is infeasible to extract all the facts and relationships buried in the text to produce a 'knowledge base of everything' that answers every possible end-user question. In recent years, there has therefore been a trend towards using text mining over unstructured data to directly answer questions, effectively creating novel databases on the fly. However, this has widened the gap between information professionals and end users. In this talk I will explain how multiple approaches are being adopted to exploit the skills of information professionals to improve decision support for end users. These include specialised real-time querying, alerting based on semantic queries, semantic enrichment of documents, and population of semantic stores or linked data.

1. Unstructured Text in Big Data
The Elephant in the Room
David Milward
ICIC, October 2013

2. Click toto edit Master title style
Unstructured Big style
Click edit Master titleData
• Big Data
– Volume, Variety, Velocity
80%
• Estimated 80% of all data
is unstructured
– Need to be able to make
decisions based on this
data
• Ever increasing amount of
data makes it harder and
harder to filter by hand
– Need more automation
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20%

3. Click toto edit Master Data style
From edit Master title style
Click Unstructured title to Structured
Identify
concepts and
relationships
Structured, relational data
Normalized concept
identifiers
Traditional text mining works in batch mode
Unstructured or
Semi-Structured
Content Sources
Linguamatics Confidential
Requires each extraction to be programmed in,
or learnt from a large amount of annotated
material

4. Click Agile Text Mining
I2E to edit Master title style
Click to edit Master title style
Querying
Results
Information
Consumers
Confidential

5. Click toto edit Master title style
Increasing Range style
Click edit Master titleOf Applications
Sentiment
Analysis in
Social
Media
Vocabulary
Development
Mining
Conference
FDA Drug
In the beginning...
Labels Abstract
Mining
Electronic
Medical
Records
Mining
SAR Competitive
ProteinExtractionIntelligenceProtein
Target
Interactions
Identification
&
Prioritization
Safety/Tox
Confidential
Plant
Metabolic
Pathways
Patent
Analysis
Extracting
Chemical Numerical
Search
and
Experimental
Data
Biomarker
Discovery
In-licensing
Opportunities
Key Opinion
Leader
Identification Patent FTO
Systems
Biology
Drug
Repositioning

6. Click toto edit Master title style
The edit Master title style
ClickDatabase Approach
• Can we predetermine what is required?
• One approach is to try to extract everything … but in practice have
to make assumptions, whether by hand or automatically
– Do we include negative statements?
– Do we include speculation?
– What context do we include?
• Need a good idea of how the data will be used to know what needs
to be extracted
• There are cases where this works well e.g.
– have useful structured data and want to add to it from free text
– have new records structured, but want to bring in information from legacy
free text
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Extracting for a Relational Database
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• Extracting all relevant information from pathology reports
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Extracting for a Semantic
Click edit Master title style Store
• Output as a set of triples
• Because all the parts have URIs, the parts are webaddressable
Treat
Peptide
Psoriasis
Express
Associate
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Web
Infectious
Disease

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The edit Master title style
ClickAd edit Master title style
• No need to build a database
• Regard text mining queries as ways to create a database on
the fly
• Keep all the unstructured free text, and query for what you
need when you need it
• Even index structured data with text mining to link
information together
• Used very successfully by knowledge professionals:
– Just like a search engine, there are thousands of questions people
want to ask of the data
– You can’t predict them all beforehand
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10. ClickHoc Querying style style
Ad to edit Master title
Click to edit Master title
• Find relationships that you want
for any particular information
request
• Treat the text mining as an
“agile” database to answer
thousands of different questions
e.g. metabolites from fruit
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11. ClickWe NeedMaster title style
Do to edit Master title style
Click to edit More?
• I2E Agile Text Mining has opened up text mining to the end
user, but it is still mainly used by knowledge professionals
• Given the importance of unstructured big data, can we bring
the benefits of text mining to an even wider audience?
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Workflow Automation
Click edit Master title style
• Successful queries converted into regular
workflows using Pipeline Pilot or KNIME
• Real-time workflows for up-to-date dashboards,
alerts
• Further analysis, visualization, integration with
other data
Pipeline Pilot
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13. Click toto Trials Analysis from I2E
Clinical edit Master title
Click edit Master title style style
Text Mining Results
Colours
indicate
Phase
Using Pipeline
Pilot visualization
Dates
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14. Click toto edit I2Etitle style Web Apps
Embedding Master title
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10/21/2013
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4734_8.pptx

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Click edit Master title style
Form-Based Querying
• Create web interfaces
connecting to I2E server
• Information
professionals develop
sophisticated queries
• Queries are
parameterized to allow
end users to customize
• Javascript allows
portability to mobile
devices such as iPads

16. Click toto edit Master title style
Enhancing Enterprise
Click edit Master title styleSearch
• Use text mining to annotate concepts to feed into a search
engine to:
– provide concept search
– provide concepts for facets
– provide intelligent thumbnails for documents, pulling out key
information
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17. Value
Click toto edit Master titledocuments
Semantic annotation
Click edit Master title styleof style
Relationships
Concepts
Keywords
Search Engine
NLP-based Text Mining

18. Click toto edit Master title style
Provide Concepts style
Click edit Master titleas Facets for
Enterprise Search
Document
Refine the search
information and
based on
teaser
terminologies
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Document preview
and additional
information

19. Click toto edit Master title style
Dictionary Matching Click edit Master title style Companies

20. Click toto edit MasterInstitutions
Pattern Matching title
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• Not a fixed list: can find previously unknown institutions

21. Click toto edit MasterMutations
Pattern Matching title
Click edit Master title -style style

22. Click toto edit MasterChemicals
Pattern Matching title
Click edit Master title -style style
• Assign structure to novel chemicals
• Distinguish exemplified compounds
• Distinguish compounds given properties

23. Click toto edit Master title style
Whatever the title style
Click edit MasterContent...
• ... I2E can mine and extract with precision
Scientific literature
Twitter

24. Click totoWherever style style
…. and edit Master
Click edit Master title title
Internal Content
MEDLINE Patents
Drug Labels
Clinical Trials
Content
Provider
Publisher
I2E OnDemand
I2E Enterprise
I2E Platform
External
Content
I2E Platform
• I2E 4.1 Linked Servers
• Users can query local information or information on the cloud
• Proprietary content can reside within Enterprise
• Standard sources e.g. patents can be hosted on the cloud, and shared by all users
• Data from content providers can reside on their sites
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25. Click toto edit Master title style
Bringing Master title style
Click editthe Elephant Down to
Size
• Data warehouses for data that you know you need
• Embedding text mining in other applications e.g. Enterprise
Search to provide benefits of semantic search
• Building specialized new interfaces to provide self-service
applications for end-users
• Continuing role for ad hoc text mining
– text mining can ask almost any question of the data
– you can never predict every question
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