This document provides an overview of Anzo Unstructured, a natural language processing (NLP) platform from Cambridge Semantics. It discusses the core capabilities of Anzo Unstructured, including intake of various file formats, extraction of entities and relationships, and semantic analysis. It also outlines example use cases in pharma and finance. The document demonstrates the configuration and visualization of Anzo Unstructured pipelines and annotations.

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Introduction to Anzo Unstructured
June 29, 2016
Richard Mallah
Director of Unstructured and Advanced Analytics
richard@cambridgesemantics.com

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Agenda
• Anzo Unstructured and the Anzo Smart Data Platform
• Core Capabilities of Anzo Unstructured
• Configuration, Operations, and Output
• Example Use Cases in Pharma and Finance
• Exploring Document-Derived Analytics
• Visualizing Additional Annotators and Capabilities

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Introduction to Cambridge Semantics (CSI)
The Anzo Smart Data Platform is used to create data analytics and
management solutions with diverse data from varied sources
Company:
 Founded in 2007 by senior team from IBM’s Advanced Internet Technology Group
 Privately Funded
 Select customers:
Software:
 Market leading Anzo software suite is built on open Semantic Web standards
 Currently 3rd generation of the product in production use

4. ApplicationsMiddlewareEnterprise
DataFabric
Anzo.js
Client Library
Anzo Enterprise Server
(SOA; OSGI, RDF & OWL over JMS)
Anzo.Net
Client Library
Anzo .java/.Net
Client Library Anzo Relational Replicator
Reasoning
& Rules
Workflow
Semantic
Services
Anzo
Connect
Enterprise
Directory Connect
Anzo
Unstructured
Anzo for Excel Applications and BI ToolsAnzo on the Web
Anzo
Graph
Database
Anzo
Content
Repository
RDBMS
Data Mart/
Warehouse
Enterprise
Applications
Directory
(LDAP, AD)
• Virtualize data using W3C
semantic standards
• Operationalize industry
standards e.g., FIBO, LEI
• Real-time data events
• Granular security and access
control
• Ontology, Mapping,
Visualization & Service registries
Rich Client Apps
………
 Full/Incremental ETL
 Web Services
 Federated SPARQL
 NLP
 Text Analytics
 Semantic Analysis
3rd Party
Databases &
Applications External Data Sources
Unstructured
Content
 RDBMS
 Teradata
 Hadoop
 SalesForce
The Anzo Smart Data Platform

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Anzo Smart Data Lake
Anzo Smart Data Lake Server
Anzo Enterprise Server
• Self-service analytics,
visualization and data discovery
• Data curation, annotation and
application workflow
• MPP graph query engine for
interactive analytics at scale
• ODATA Integration for 3rd party
analytics tools
• Metadata, ontology and
mapping catalog
• Model-driven data provisioning
and loading
• Text analytics
• Canonical entity linking and
transformation
• Scalable Graph and Document
Storage
Anzo Graph Query Engine
Anzo Ingestion Servers
Anzo Unstructured

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Anzo Ontology Editor

7. What Solutions Benefit From Anzo?
• For aggregation of data from multiple, diverse data sources
• For integration of internal data with external data across the Web or
firewalls
• For solutions involving data sources, business rules, analytics and
actions that are not evident in advance
• For solutions that change often
• For analyzing diverse data sources with a diverse variety of access
control requirements with a need for full provenance and traceability
• For evolving solutions benefiting from ongoing involvement from
domain experts to update data models, data sources, and analytics as
needed
• For formal and informal day-to-day business activities that require
workflow, alerts, and automation
• For collecting & analyzing data that doesn’t currently have any system
of record (e.g. “shadow IT” systems)

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Anzo Unstructured Capabilities
Overview
• Intake Sources
– Social Media
– Local Directories
– Enterprise CMSs
– Structured Databases
– Web Sites & Boards
– Spreadsheets
– Google Search Appliance
– Mail Servers
– + dozens more
• File Formats
– Office Documents
– PDFs
– Web Pages
– Email Messages
– + dozens more
• Multilingual
– European, Asian, and Middle Eastern Languages
– Native-Language Annotation
– Document Translation
– Annotation Translation
– Phonetic Name Normalization/Indexing
– Cross-Lingual Concepts Automapped
• Extraction Categories
– Entities
– Relationships
– Granular Sentiment
– Topic Classification
– Patterns and Concepts
– and more
• Concept Types Extracted
– MedicalHistoryAilment
– LegalStatuteSection
– BiomarkerForDisease
– AnalystEarningsEstimate
– JobTitle
– SentimentTopic
– + thousands more
– + easily user-extended/customized
• Semantic Analysis
– Concept-Based Relationships
– Relationship Compounding
– Annotation Harmonization
– Multi-NLP Weighting/Voting
– Ontology Growing
– Ontology Alignment
• Semantic Search
– Concept-Based Full-Text Search
– Facet On Concept or Type
– Mix Structured & Unstructured Filters
– Visualize Annotations In Context
– External Index Federation
– Multi-Stage Searching/Filtering/Clustering
• Structured/Unstructured Integration
– Find/link structured resources in text
– Analyze text within structured columns
– Populate new structured resources
from text
– Auto-enrich entities found in unstructured
– Auto-extend schemas from unstructured
properties

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Anzo Unstructured NLP Plugins
Overview
• Anzo Unstructured is both a pluggable framework supporting a
large number of ready-made third-party NLP integrations, and also
has significant NLP capabilities bundled along with it
– Plugins on the following pages are a small number of our many supported
NLP capabilities from a variety of sources
• Among the annotators include out of the box are:
– Autotagger and Classifier Annotator (Statistical, can fall back to rule-based)
– Autotagger and Classifier Annotator (Rule-Based, can fall back to statistical)
– Standard Entity Extractors (People, companies, locations, job titles, dates, etc.)
– Custom Knowledgebase Annotator (Lever your taxonomies, thesauri, databases)
– Fuzzy Rule Network Annotator (Find concepts by related, surrounding, contextual concepts)
– Significant Phrase Annotator (Automatically extracts the important concepts)
– Document Section Annotator (Autogenerate table of contents and contextualize more)
– Pattern Annotators (Find part no., id no., statute section, or any custom pattern)
– Custom Relationship Annotator (Find events or relationships spanning different extractions)

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Optional NLP Plugin Technology Partners

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Semantic Post-Processing of NLP
• Harmonization
– Normalized formats for knowledge integration
• Cooperation
– Multiple annotators strengthen, correct, and increase the network effect
of relationships
• Probabilistic Reasoning
– Semantic knowledge integration includes both deduction and inference
• Filtering
– The set of concepts, overlaps, affects, and relationships can be
automatically filtered down to reduce noise
• Enrichment
– Web services, semantic services, internal and external databases and
knowledgebases, and pluggable computations can be used to add more
context and data to your new domain object
• Machine Learning and Predictive Analytics
– Train on some gold standard and do some supervised classification
– Incrementally build a conceptual cluster space for predictive analytics

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Point and Click Configuration of Unstructured Pipelines

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Point and Click Configuration of Annotation

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Unstructured Pipeline Operations Monitor

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Dashboarding Structured/Unstructured
Knowledge Integration
Structured
property
Multiple NLP
Technologies
Harmonized
Overlapping
annotations
Enriched
property
Unstructured
entity
Unstructured
relationship
Archived copy for review,
validation & provenance
(both HTML Format &
Original )

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The CSI Semantic Knowledge Integration Approach
to Enterprise Text Analytics
• Use Multiple NLP Engines or Annotators
• Leverage a Knowledge Integration Platform
– Make the annotators cooperate
– Enrich the annotations with internal or external data
– Link annotations with existing structured data
– Filter them down to the most relevant set
– Harmonize ontologies and instances
– Deal with probabilistic or uncertain information
• Quality Control
– Manual curation and automated QC
– Workflow, provenance lineage
• Easily Deal with Data Changes and Schema Changes
– Both are dealt with in real-time at runtime
– Maintenance is orders of magnitude more efficient

17. Use Cases in Pharma
• PV & Safety Data Management - Automatic tagging of case reports with
customized curation workflow, text mining, and contextual search
• R&D Competitive Intelligence – Explore the competitive landscape for
Therapeutic Area, Indication, Target, Company, Compound, & Partners
• R&D Informatics– Understand and correlate your internal research and
how it may be related to any external developments or research
• Clinical Trial Site Selection and Optimization - Site selection, KOL search,
trial planning
• Scientific Affairs/Medical Science Liaisons - Track Key Opinion Leaders
(KOL) in literature and clinical trials & analyze feedback from medical
professionals and patients
• Information Landscape - Track and monitor data stewardship and usage
through the organization to drive more efficient usage.
• Commercial Analytics – Sales and Marketing, Rx Data, Text Analytics

18. Use Cases in Financial Services
• Compliance Policy & Procedure Management - Monitor structured and
unstructured data sources for relevant regulatory changes; have
collaborative workflows for policy & documentation development,
approval, and control; and establish targeted policy dissemination and
attestation workflows.
• Compliance Surveillance & Investigation– Legal and Compliance analysts
can create structures and views that provide analysis, rules, and alert
thresholds easily changed on-the-fly by investigators, who can then
comprehend and interact with the big data picture.
• Market and Customer Intelligence- Understand how clients and prospects
are thinking about your firm and competitors’ offerings
• Research - Automated analytics of news, chatter, IMs, secondary research
reports, emails, sentiment, etc. for research alerts, semantic search, and
relationship visualization, forming an integrated intelligence platform for
analysts, including Complex Event Processing.
• Information Landscape - Track and monitor data stewardship and usage
through the organization to drive more efficient usage.
• Commercial Analytics – Sales and Marketing, Tx Data, Text Analytics

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Point and Click Configuration of Annotation

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Point and Click Configuration of Annotation

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Relationship Explorer
Find Unexpected Connections Between Companies | Follow Paths Out or In From Anything | Follow the Money

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Incremental Semantic Overlays: Product, Brand, Offering

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Semantic Correspondence Linking and Overlay

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Asking Cross-Ontology Questions

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Cross-Ontology Questions Meet The Network Effect

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Multi-Ontology Knowledge Graph Exploration

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Deep News View
Customizable Fundamental, Technical, and Thematic Filters | View Only Most Recent n Minutes | Semantic Search

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Rapid Concept Drilldown
GPS for Concepts | Assisted Skimming | Interactive Annotation-Driven Navigation | Auto-translates Foreign Languages

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Example: Customizable Stock Centric Surveillance
Dashboards Per Stock | Per Cohort | Per Industry | Per Custom Sector | Analyst Can Define Filters and Drilldowns

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Example: Competitor Sentiment Comparison
Longitudinal | Sentiment Aggregation | By Cohort | From Single Stock Selection | Visualize Leaders and Followers

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Example: Intraday Sentiment
Drill Down | Intraday Topic-Granular Sentiment | Attribute Price Action Drivers | Investigate Unusual Volumes

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Longitudinal and Outlier Business Intelligence
Unstructured Data Becomes Structured

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Anzo Unstructured NLP Plugins
CSI Web Scraper Annotator

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Contextual Semantic Overlay

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I1
I2 I3
E1
E2
I4
I1
I2 I3
E1
E2
I4
I1
I2 I3
E1
E2
I4
Main
Pipeline
Purple
Helper
Pipeline
Green
Helper
Pipeline

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Fuzzy Concept Matching Example: Skills
Understanding and Recognition in Semantic Search

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Fuzzy Concept Matching Example: Skills
Concept Curation
• Use Excel to define each skill concept with any combination of methods
• Multiple values are comma-separated
• Patterns support wildcards, y within n words of x, and intuitive groupings
• Define more atomic concepts before more compound concepts

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Anzo Unstructured NLP Plugins
CSI Document Classifier

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Indirect Filters on Domain-Specific Summaries
Auto-Summarization | Extensive Filters | Integration with Multiple Sources of News and Research | Assisted Reader

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Cross-Lingual Annotation and Optional Translation

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Multiple Languages, One Concept

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In Situ Translation and Annotation

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Automated Redaction

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Anzo Unstructured NLP Plugins
CSI Significant Phrase Annotator

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Anzo Unstructured NLP Plugins
CSI Custom Relationship Annotator

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Anzo Unstructured NLP Plugins
Linguamatics I2E Annotator, Biomarkers for Diseases

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Anzo Unstructured NLP Plugins
SciBite Termite Annotator

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Anzo Unstructured NLP Plugins
Lexalytics Salience

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Simplified Views for Non-Technical Users
Semantic Search Made Easy

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Anzo Unstructured Capabilities
APIs and SDK
Create new pipeline components for any of these tiers:
– Document Crawler / Listener
• Obtain documents of any format from any source
– Document Rich Text, Thumbnail, and Metadata Extraction
• Deal with custom or less-common file formats completely pluggably
– Document Format Cleansing and Transformation
• Remove unwanted artifacts specific to your documents or translate to a particular
format or language
– Full-Text Indexing
• Pluggable corpus-level indexing and search
– Annotator
• Already supports GATE, UIMA, and FrAU annotation frameworks
• Provides access to annotations from any other annotator, cleansed text, format-
analyzed document, and original file, supporting mixed-representation annotation
• Multithreading safe
– Semantic Postprocessor
• Recombine, filter, and restructure annotations

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