This document discusses using natural language processing (NLP) to analyze unstructured text data and extract key information to close the loop on knowledge management. NLP can help overcome data silos by linking structured and unstructured information. It allows identifying entities, relationships, and sentiments to understand meaning and context in order to act purposefully. The goal is to discover, analyze, and combine customer experiences to improve strategies.

1. scopeKM
Knowledge Management
„Closed Loop“ with Computer Linguistics
Discover, analyze, and combine customer experiences
and successfully reflect them into strategy
Presentation
Juraj Schick
scopeKM GmbH, Zürich
05.12.2014 1

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Knowledge Management
Agenda
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1. Challenges and Opportunities
2. The view of the things behind: Identify, apply and deepen
the knowledge

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Knowledge Management
Purpose of the text analysis
€Recognize relevance, assess the situation and act purposefully
€ The solution uses the Natural Language Processing (NLP) to „understand” facts,
sentiments and their context.
€ The barriers of otherwise isolated data silos can be overcome and joint analysis
of structured and unstructured information (text flow) enabled.
€ The relevant information will be detected, extracted, and semantically enriched
with company-specific metadata. Purpose: creation of customer or product-
specific documentation to ensure the end-to-end management using workflow
engine.
€ NLP allows to extract information about interesting entities and their
relationships in order to derive precise and relevant RDF-triples (Subject <>
Predicate <> Object). In this way, the causes of events can be determined – the
answer to the question of "why something happens" – in order then to act
targeted and appropriate to the situation.
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Knowledge Management
The new challenges
€The challenges of technological change
€ Steadily increasing amount of data (terabyte/d. +).
€ Complexity of relations with disproportionately rising costs (many customer
channels) and inhibited learning strategies (lack of closed loop).
€ Changing communication requirements (from online to mobile).
€ Data silos (file server, CMS, CRM/call center, mail server, etc.) with
unstructured information (80% + of all data in the enterprise), which are not
usable with conventional methods.
€ Non - or little structured knowledge processes as a cross-cutting functions.
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Knowledge Management
Understand the meaning and
context, act purposefully
€The large and growing data volume and complexity can be overcome
only by (partial) automation of services
€ Opening up of all internal and external sources, linking the structured (BI) with
the non-structured data (text flow).
€ Closed Loop: automatically understand and analyze, communicate and kick off
stimulate, act as well as track and learn.
€ Only the answers assessed by a confidence factor as successful and which
are available in the context of the task are presented .
€ Machine learning systems have the ability to •understand‚ and to •interpret‚
the issue in a broader context and ƒ if necessary ƒ to ask questions in
order to clarify the task.
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Knowledge Management
„Closed Loop“ - CEM
Decisions
Analysis
Tracking/
Learning
Actions
CEM*
comprehensive &
prospective
collaborative or
automatically
in real-time &
informed
pervasively &
continuously
* CEM Customer Experience Management
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Discover, analyze,
combine and reuse

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Knowledge Management
The new chances
€ The early discovering of new trends and the correct action towards
the changed customer behavior.
€ Industrialization of processes and benchmarking (Best Practice incl. KPI).
€ Linking macro with micro-analyses for improved understanding of natural and
human-caused risks.
€ Macro-analyses: Identification of trends and patterns for the determination
of the strategy and methodology (discover new insights about the
opportunities, behavior, performance, etc.)
€ Micro-analyses: Knowledge about single customers or support of single
activities, like diagnoses, assessments and consultations.
€ Ability to discover operational challenges and opportunities in real time and to
answer them proactively.
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Knowledge Management
Agenda
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1. Challenges and Opportunities
2. The view of the things behind: Identify, apply and deepen
the knowledge

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Knowledge Management
With NLP, different linguistic representation levels are processed sequentially:
€ Tokenization: is the process of breaking a stream of text up into words, phrases, symbols,
or other meaningful elements called tokens.
€ Morphology: is the identification, analysis, and description of the structure of a given
language's morphemes and other linguistic units, such as root words, affixes, parts of
speech, etc.
€ Syntactic analysis/parsing: is the process of analyzing a string of symbols, conforming
to the rules of formal grammar, recognizing the basic scheme of the source sentence
(subject, predicate, secondary elements), and processing Part-of-speech-Tagging.
€ Semantic analysis: meaning is assigned in different single steps to the sentences or its
parts.
NLP Natural Language Processing
€To understand and to classify the facts and the sentiments, the language – the
written text - must be understood. This is accomplished with the NLP.
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Knowledge Management
Semantics as a Solution
€ Semantics are based on relationships between data and are therefore ideal tool for
linking and searching for complex structured and unstructured data with the
standard query SPARQL.
€ Example insurance: Inquiry "All beneficiaries of health insurance, earning more
than $ 100,000 in 2010 and living in Atlanta, GA“ indicates the combination of
data on income, geography and time.
€ With semantic solutions, all sources are linked and isolated data silos avoided.

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Luxid ® : Components of the
Solution
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Luxid ® Annotation Factory as a pipeline for the NLP
€ Extraction of Topics, Entities and Relationships from the text
€ Categorization of documents and their clustering
€ Extraction engine for syntax, statistics, taxonomy, etc.
€ definition of rules for clustering, displaying, etc.
Luxid ® Skill Cartridge Library for different applications or
application areas, such as taxonomy/ontology, text mining, opinion
mining or marketing, finance, medicine, business administration,
pharmacy and chemistry, etc.
Luxid ® Content Enrichment Studio for customizations of
existing or development of completely new applications.

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The triple – the logical statement
about resources
€ A triple is a way of encoding information about objects in the RDF (resource
description framework). The triple is a basic form of the ontology.
€ The triple represents an assertion of subject and object in relationship with each
other. Relations are directed from the subject to the object and concretized with
the predicate. Triples, which relate to the same subjects or objects, form a
semantic network.
Subject Predicate Object
ZKB grants mortgage
Mortgages require equity
The mortgage has an interest rate
ZKB is a bank
In the example table each line forms a triple:
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E.g.: analysis of the
inferences of aspects
without formal
representation:
Interest rate= f(equity)

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€ The triples as statements about objects (resources or entities) are stored in
knowledge bases. Then the knowledge bases can be integrated seamlessly into
the workflow applications and analysis tools of the end users.
€ Luxid „ extracts business information from unstructured texts, structured it in
the triples, which can subsequently be queried, visualized and analyzed. For
example:
€ Which business relations are there between a potential partner and the
competitors?
€ How are our products judged in the media, compared to analogous
products of our competitors?
€ Which customers comment on what concerns about the product Abc in
relation to a competitor's product?
Linking all data – company-wide
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Knowledge Management
Notations and their hierarchy
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Descriptors
Annotations Metadata Categories
Concepts Terms
Relationships
Entities
€ Annotations: Descriptors created at text level
(concepts) or document level (terms)
€ Concepts: Normalized forms created in text analyses to
which terms and expressions are mapped
€ Entities: Concepts that represent self-explanatory units of
information, possibly with attached attributes (e.g. the
expression United States… is mapped to entity •USA…)
€ Relationship: Concepts that link several entities at the
sentence level, possibly with assigned role for
corresponding entity and attached attributes (•Company
A acquired Company B in January 2014 …; Company A:
subject, Company B: object, in January 2014: the context
of the relationship
€ Terms: The most important words or groups of words in a
document, which are used for clustering or categorization
€ Categories: Classification of documents according to the
concepts they contain (taxonomy/ontology).

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Which domain metadata ?
€ People names: Customers, Collaborators, Links to the directory
€ Organisations: Subsidiaries, Departments, Suppliers, Competitors,
Partners
€ Internal References: Project, Contract, Customer, Geography, Market
Segment, etc.
€ Product- und Service names: Product taxonomy, Accessories, Options,
etc.
€ Enterprise Terminology and Categories: Technical vocabulary,
Document categories
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Automatic extraction of metadata
On May 10th Microsoft bought Skype for $8 Billion.
Terms
Num ProperPrep Verb Proper Prep U NProp Card
CompanyPrep Action Company Prep Monetary Expr.Date
Entities
Relations
Acquisition
Purchaser Microsoft
Target Skype
Amount $8 billion
Date May 10th
On May 10th Microsoft bought Skype for $8 Billion.
Roles
Attributes
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T T T T T T T T T T T T
T T T T T T T T T T T T
T
T T T T
TT TTTT TTT TTTT
Creation of the
taxonomy/ontology
Tagging of documents
Definition of the triples
Taxonomy/ontology to
the document-layer
Application of the
taxonomy/ontology
Seamless
synchronization:
transparency for the end-
user
Luxid® Webstudio: automatic
ontology management
+
+
€ Preview shows the annotations in real-time
€ NLP driven proposals for improving the thesaurus
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Opinion Mining
€ With the Opinion Mining Skill Cartridge content created by users will be
analyzed in order to identify synthetic views in them – with all Pros and Contras
– and to evaluate the tell-tale signs within the information flows.
€ The subjective information-bearing linguistic character in the text is recognized
and used for identification and qualification of evaluative expressions.
€ The result of data mining are then valuable and non-obvious Insights.
€ The sentiment analysis represents a part of the overall Opinion Mining solution.
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Key Elements of Opinion Mining
€Fine granularity of evaluation result is achieved by determining polarity and
intensity of the statement.
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OpinionSource Target
Negative Positive Weak Strong…
Affect
Polarity Intensity
Judgement
Evaluation type
…

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The Screenshot shows, that positive and negative opinions are expressed on a TV product and ist features
from BrandX, with more or less intensity. The Proof zone provides the full sentence for context.
Sentiment analysis
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