2015 11-4-v14-ickm2015-keynote

Dr. Dickson Lukose Artificial Intelligence Lab. 6th November 2015

Application of Semantic Technology in Knowledge Management

11th Interna*onal Conference on Knowledge Management November 4-‐6 Osaka, Japan

Contents

•  Types of Knowledge Management •  Data, Information & Knowledge Challenges •  Semantic Technology Platform

– Text Understanding – Natural Language Query – Data Harmonization

•  Knowledge Management Portals •  Social Media Intelligence •  Social Network Intelligence •  Conclusions

2 © 2015 MIMOS Berhad. All Rights Reserved.

People & process-oriented KM initiatives •  Cultural and Readiness

Assessment •  Formulation of a KM Strategy,

Framework & Strategic Planning •  Knowledge Audit & Knowledge

Management Audit •  Change Management •  KM Assessment including the

definition of metrics & reporting of Intellectual Capital (IC)

•  Community of Practices / Special Interest Groups (SIG)

Technology-oriented KM deployments •  Search Engine configuration,

testing & deployment •  Taxonomy development,

maintenance & governance •  Collaboration System •  Enterprise Portal •  Electronic Document Management

System (EDMS) •  Knowledge / Information

Repositories •  Content Management System

(CMS) & Applications (CMA) •  E-Learning •  Intelligent System •  Blogging / Weblogs / RSS

Readers / Wikis

3

Type of Knowledge Management Projects Technology-oriented KM deployments •  Search Engine configuration,

testing & deployment •  Taxonomy development,

maintenance & governance •  Collaboration System •  Enterprise Portal •  Electronic Document Management

System (EDMS) •  Knowledge / Information

Repositories •  Content Management System

(CMS) & Applications (CMA) •  E-Learning •  Intelligent System •  Blogging / Weblogs / RSS

Readers / Wikis

Lessons learnt from nearly two hundred cases of KM journeys by Hong Kong and Asian Enterprises Prof. Eric Tsui, Hong Kong PolyU

© 2015 MIMOS Berhad. All Rights Reserved.

World of Data


4

Structured

Unstructured

Knowledge Audit

2015 MIMOS Berhad. All Rights Reserved.

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Enterprise Data Linked Open Data

Sensor Web

ACTIONABLE KNOWLEDGE

ASSETS

Structured, Semi-‐Structured & Unstructured

Unstructured Structured & Semi-‐Structured

Ontology

Technical WriTng

Term Databanks

Machine TranslaTon

Human TranslaTon

InformaTonRetrieval

Knowledge Engineering

Consumer InformaTon

R&D

StandardisaTon

Nomenclature

Terminology

AGROVOC

MYGMO

PADIPEDIA

AGRIS

HERBAL MEDICINE


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SNOMED-CT STW

Knowledge Portal Challenges


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Knowledge Portal

Natural Interface

High Density VisualizaTon

IntegraTon and Insight into Social Media

CoP Network AnalyTcs

CollaboraTve Problem Solving

NOT What is the Content, BUT, What is

in the Content

Making Sense of Social Big

Data

Big Data Challenges: Velocity Diversity Volume

Seamless Integra*on of

SME in Problem Solving

Semantic Technology Platform


TEXT UNDERSTANDING

What is Text Understanding?

Conceptualiza*on

John is going to the bank by bus

Person

Human

Animate

Financial insTtuTon Road vehicle

Agent Des*na*on using Instrument Inanimate

Knowledge Graph

go


English Text:

10

male-person: “John” go: * bank: *

bus: *

agnt dest

inst


Penduduk mendapat bantuan daripada kerajaan

Human

Animate

resource organizaTon

Inanimate

Knowledge Graph

mendapat

Malay Text:

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Agent Resource originaTng Source


Conceptualiza*on

大卫在图书馆等了 3小时。


Person

Human

Animate

Library

wait

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3 hours

Mandarin Text:


Knowledge Graph

Agent Loca*on for Time LocaTon

Conceptualiza*on

English NLP

Sentence

Knowledge Graph

Syntax Structure


Malay NLP Sentence: Meningkatkan harga barang dan minyak kerana inflasi negara.

Annotated sentence: Meningkatkan_VB harga_NN barang_NN dan_CC minyak_NN kerana_CC inflasi_NN negara_NN

Knowledge Graph:


Mandarin NLP

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Text

Segmented Text

Part-of-Speech Results


Mandarin NLP

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Text

Segmented Text

Entity Recognition

Results


Mi-NLP Demo


NATURAL LANGUAGE QUERY

Traditional Search

Too many results

Pa]ern matching Pa]ern matching


Traditional Search


Knowledge Base Preparation

Mi-‐CLIP

Mi-‐HARVESTER

Mi-‐KRAKEN

Mi-‐NLP

KNOWLEDGE BASE

World Wide Web

Linked Open Data

Enterprise Database 21


Natural Language Query Interface


Semantic Query


Visual Semantic Query


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DATA HARMONIZATION

SNOMED CT terminology

311,000+ concepts

Hypertensive complicaTon

[SCT_449759005]

Kidney disease [SCT_90708001]

NeoplasTc disease [SCT_55342001]

Hypertensive renal disease

[SCT_38481006]

Neoplasm of kidney

[SCT_126880001]

Renal impairment [SCT_236423003]

Chronic renal impairment

[SCT_236425005]

Kidney disease

Concept ID: 90708001

Preferred term: Kidney disease

Synonym(s): -‐  Renal Disease -‐  Nephrosis -‐  Disorder of kidney -‐  Nephropathy -‐  Disease of kidney -‐  Renal disorder

26 4,060,716+ triples 1,360,000+ relaTonships

Disorder of abdomen ∧ Kidney finding ∧ Disorder of body cavity ∧ Disorder of kidney and/or ureter ∧ ∃Finding_site.Kidney structure

Logical formula: What can SNOMED CT be used for?


Data HarmonizaTon Process

HIS1

HIS2

HIS3

DB2

DB3

DB1

Schema Tables

Tables Schema

Schema Tables

IT Staff (Consolidate the data)

Subject Ma]er Expert (Interpret the data)

-‐ Time Consuming -‐ Limited Resources -‐ Skills-‐dependent -‐ Prone to errors

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q Automate processes q Enable seman*cs q  Improve repor*ng accuracy

SNOMED CT Big Data: Freetext Challenge


ID Pa*ent Name Gender Symptoms Diagnosis

1 xx M FaTgue, Chest pain Heart failure

2 yy F Breathlessness Asthma

ID Nama Pesakit Jan*na Tanda-‐tanda Diagnosis

1 aa L LeTh, Sakit dada Lemah jantung

2 bb P Sesak nafas Penyakit lelah

ID Pat.Name Sex Disorder Diagnosis

1 kk Male Tiredness, Pain in chest Cardiac failure

2 ll Female Dyspnea BHR

HIS1

HIS2

HIS3 SNOMED CT (Refsets)

Challenge of freetext

[371484003] [371484003] [371484003]

[346741003] [346741003] [346741003]

[263495000] [263495000] [263495000]

[243814003] [243814003] [243814003]

[439401001] [439401001] [439401001]

[276179002] [276179002] [276179002]

[84229001] [29857009] [84229001] [29857009] [84229001] [29857009]

[84114007] [84114007] [84114007] [267036007] [267036007] [267036007]

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Query: how many cases of Dyspnea?

1 3

[195967001] [195967001] [195967001]


Mi-Harmony Demo


Question Answering System

Mi-CLIPTM

Text Understanding

SQ VSQ

Knowledge Graph

Texts

Question

Mi-Reasoner Answer

English NLP

Malay NLP

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Mandarin NLP


KNOWLEDGE MANAGEMENT PORTALS

Knowledge Audit


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Sensor Web


ASSETS



Ontology

Technical WriTng

Term Databanks

Machine TranslaTon

Human TranslaTon

InformaTonRetrieval


Consumer InformaTon

R&D

StandardisaTon

Nomenclature

Terminology

AGROVOC

MYGMO

PADIPEDIA

AGRIS

HERBAL MEDICINE


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SNOMED-CT STW

PADIPEDIA KNOWLEDGE REPOSITORY

PADDY RESEARCH

GROWTH

STAGE SEEDLING TILLERING BOOTING HEADING FLOWERING MILKY RIPENING

PADDY DIAGNOSTIC ENGINE PADDY COMMUNITY

PROJECT PUBLICATION MACHINE TECHNOLOGY INTELLECTUAL PROPERTY


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HERBAL MEDICINE KNOWLEDGE REPOSITORY

HERBS

TRADITIONAL MEDICINE PHARMACOLOGY

PROCESS PLANT PART ROOT FLOWER STEM LEAF SEEDS

PLANT ANATOMY

HUMAN ANATOMY

FRUIT 2015 MIMOS Berhad. All Rights Reserved.

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PADIPEDIA ONTOLOGY DESIGN


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Technology

isPreparedForRMK

hasTargetDateOfCompletion

xsd:date

xsd:integer

isCommercializedBy

isRelatedToCluster

hasCompletionDate

hasComDesc

hasComDate

hasPriority

hasRelatedProject

hasDevDesc

isRelatedToCommodity

dc:description

dc:title

rdf:type

PadiClassification

xsd:string

xsd:string

xsd:date

xsd:date

foaf:Project

Cluster

Commodity

xsd:string

xsd:string

xsd:integer

CorporateKPI

hasCorporateKPI

org:Organization

diagnose:hasMainSymptom

diagnose:hasPestDetail

agrovoc:hasScientificName

agrovoc:hasCommonName

diagnose:hasCharDetail

diagnose:isConfirmedBy

padi:hasManagement

diagnose:hasControlDetail

diagnose:hasStageDetail

xsd:string

agrovoc:PestLifeStage

rdf:type

EggLarvaPupaAdult

diagnose:destructAt

padi:occurAt

agri:isManagedBy

diagnose:hasPestCausal

xsd:string

xsd:string

agrovoc:Pest

diagnose:PestDetail

diagnose:MainSymptom

diagnose:RiceHealthDamage

agrovoc:GrowthStage

diagnose:affectsPlantPart

agrovoc:PlantPart

xsd:string

diagnose:hasSymptomChar

xsd:string

xsd:string

agrovoc:Management

agrovoc:Control

xsd:string diagnose:hasSymptom

xsd:string

diagnose:hasSymptomDetail

diagnose:hasSymptomImage

diagnose:hasLifeCycleDetail

diagnose:PestLifeCycle

diagnose:hasLifeStage

agrovoc:PestLifeStage

diagnose:SymptomDetail

diagnose:SymptomChar

diagnose:Symptom

diagnose:hasCharImage

xsd:string

diagnose:isContributed

Byfoaf:Person

diagnose:hasPestImage

Rice

growsIn

hasCroppingSystem

CroppingSystem

agri:hasCropProfile

agri:CropProfile

hasFamilyhasGenus

Family Genus

agri:hasProductionPractices

ProtectionTechnology

agri:hasSpecies

Species

agri:hasProductionPractices

ProductionTechnology

All properties have “agrovoc” namespace in frontunless different namespace is specified.

growsIn

Climate

influences

AgroclimaticZone

AgroEcoSystem

influences

padi:RiceEcosystem

hasComponent

padi:RainfedRiceCroppingSystem

padi:RainfedUplandpadi:RainfedLowland

padi:IrrigatedRiceCroppingSystem

padi:SingleCroppingSystempadi:DoubleCroppingSystem

SolarRadiationTemperatureRelativeHumidityRainfallDaylength

agri:ClimateComponent

Variety

padi:RiceVariety

hasVariety

Soil

skos:altlabel

xsd:string

agri:GeneralDescription

Origin

agri:hasOrigin

agri:hasImportance

agri:Importance

agri:NutritionalImportanceagri:StatisticalImportancepadi:SafetyAndHealthImportance

agri:hasGeneralDescription

padi:Graminae padi:

Oryza

padi:CultivatedSpecies

padi:OSativa

WildSpecies

padi:ORufipogonpadi:OOfficinalispadi:OGlaberimmapadi:ONivara

padi:RiceBasedCropRotationSystem

System

padi:AerobicRiceBasmatiVarietypadi:HerbicideTolerantVarietyHighYieldVarietypadi:JasmineVarietypadi:SpecialtyRicepadi:TraditionalRice

padi:HybridRice

padi:InbredRice

padi:RiverineAlluviumpadi:MarineAlluvium

RiceEcotype

padi:hasRiceEcotype

padi:Indicapadi:Javanicapadi:Japonica

MineralSoil

OrganicSoil

padi:ShallowOrganicSoilpadi:ModerateAndDeepOrganicSoilpadi:Agriculture

Importance

padi:SeedlingMediumpadi:SiliconeBasedFertilizerpadi:BioCharcoalpadi:SoilImprovementMaterialpadi:BioComposite

HERBAL MEDICINE ONTOLOGY DESIGN


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Herb

xsd:stringxsd:string

xsd:string

xsd:string

xsd:string

ChemicalConstituent

hasIPNINohasTROPICOSNo

hasSynonym

skos:prefLabel

hasDescription

xsd:string

hasChemicalConstituent

FamilyName

hasFamilyName

dcterms:Location

isOriginatedFrom

dcterms:Location

hasDistributedPlace

hasLink

hasReference

xsd:string

hasFloraOfChina

hasNaturalOccurance

dcterms:Location

hasTemperature

HerbTemperature

hasAnnualRainfall

propagatedBy

hasAltitude

HerbAltitude

PlantPart

SoilType

hasSoilType

Climate

hasClimate

hasRequiredFertilizer

hasDiseaseDetail

hasPestDetail

Fertilizer

DiseaseDetail

PestDetail

hasImageLink

hasVideoLink

xsd:string

xsd:string

hasSynonymLink

xsd:float

HerbAnnualRainfall

hasMinimumQuantity

hasMeasurementUnit

xsd:float

hasQuantity

hasMeasurementUnit

hasMinimumQuantity

hasMeasurementUnit

MeasurementUnit

hasMaximumQuantity

hasMaximumQuantity

PlantDisease

hasDisease

hasDiseaseControl

DiseaseControl

PestControl

PesthasPest

hasPestControl

xsd:string

HerbProduct

hasProduce

PlantPart

CommercialProduct

hasCommercialProduct

hasPlantPartUsed

causedBy

CausalAgent

xsd:floatxsd:float

xsd:float

MeasurementUnit

MeasurementUnit

hasDescription

xsd:string

hasDescription

xsd:string

herb:Herb

TraditionalMedicine

usedToTreat

HumanDisease

herb:hasPlantPartUsed

herb:PlantPart

hasMedicationPrescription

MedicationPrescription

herb:hasReference

HerbUsed

hasHerb

herb:hasMeasurementUnit

herb:Measurement

Unit

xsd:float

herb:hasQuantity

herb:hasQuantity

xsd:float


hasFrequency

hasTreatmentMethod

Activity

xsd:string

hasDurationDuration

Prescription

process:hasProcess

process:Process

has TraditionalMedicine

herb:hasLink

xsd:string

herb:hasQuantity


xsd:string

herb:Measurement

Unit

xsd:float

herb:Measurement

Unit

applyOn

BodyAreatradmed:

TraditionalMedicine

Process

expandTo

herb:hasDescription

xsd:string

followByhasProcess

hasInitialProcess

herb:Herb

ProducedChemical

Phamacology

tradmed:HumanDisease

hasProducedChemical


herb:Measurement

Unit

xsd:float

herb:ChemicalConstituent

herb:hasChemicalConstituent

herb:hasLink

hasPharmacology

hasReference

protectAgainst

hasPharmacologyToxicity

herb:hasQuantity

ChemicalToxicity

hasConcentrationUnit

useTestModel

TestModel

hasPharmacologyActivity

PharmacologyActivity

leadToToxicity PharmacologyToxicity

hasReference

xsd:string

hasChemicalExtract

useExtract

ExtractDetail

herb:hasQuantity


hasConcentrationUnit

ExtractTypehasExtractType

extractFromherb:PlantPart

hasTestModeldescription

herb:Measurement

Unit

xsd:string

xsd:float

xsd:string

xsd:string

herb:Measurement

Unit

herb:Measurement

Unit

Padipedia Demo


SOCIAL MEDIA INTELLIGENCE

Challenges and Solution

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Internet

Expensive, Lengthy and subject to Error

Manual Searching and Monitoring Automa*c

Eliminate the manual process, Save Tme and cost

Aid in Decision Making

Centralized Analy*cs

Dashboards


Social Media Analytics

Internet

3.  Visual reports on Insights presented to decision maker for further ac8on

1. Harvest content from the internet (world wide web, social web, content web)

Automated Data HarvesTng 2.  Social Media Analy8cs to

generate insights about the topic of interest

Social Media Analysis


Domain Ontology 41

Mi-Intelligence Process Overview

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Expansion

HarvesTng

Processing

SubjecTve AnalyTcs

Visual AnalyTcs

Domain Ontology

Insights

GATHER DATA

SEMANTIFY DATA

DISCOVER KNOWLEDGE

IDENTIFY PATTERNS Domain

Ontology

ESTABLISH SEARCHSPACE


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Automated Data Harvesting


1. Harvest content from the internet (world wide web, social web, content web)

Add new topics to analyse

Summary of data harvested and processed

Search topics

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Content Insights


Where are the posts coming from?

Drill down to discover detailed informaTon

about posts Who are the users?

Where are the posts coming from?

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Sen*ments

Domain Ontology

Emo*ons

Anxiety machine understandable

domain knowledge

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2.  Social Media Analy8cs to generate insights about the topic of interest

Sub topics related to the main topic

SenTments from social media

SenTments from different regions


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2.  Social Media Analy8cs to generate insights about the topic of interest

Drill down to discover detailed informaTon

about posts



Narrow down to the individuals posts of

interest

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Mi-Intelligence Demo


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SOCIAL NETWORK INTELLIGENCE


Harvest, Extract, Harmonize, and Transform Data

Informa8on related to Suspects

Drugs Cases

JKDM

WCO

Rule Mining (Machine Learning) SME

(Risk Officer)

JKDM HQ

Suspect KB

Rules

Social Network IdenTficaTon (Mi-‐Target)

750 Suspects 30 Million Facts

Suspect Data Preparations

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Suspect Network Analysis

Harvest, Extract, Harmonize, and Transform Data

Informa8on related to Suspects

Suspect Network Analysis Engine (Mi-‐Visuali*cs)

Poten*al High Risk Individuals •  Most InfluenTal •  Top Connectors

Intelligence Officers

Suspect Watchlist

Drugs Cases

JKDM

WCO

Suspect KB


Other Informa*on


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2015 MIMOS Berhad. All Rights Reserved. 52

Rule Mining (Machine Learning)

SME (Risk Officer)

Passenger Risk Profiling

Adap*ve Personal

Demographic Analysis

(Mi-‐Target)

Observa*on: •  Travelling Purpose •  DuraTon of Stay •  Profession •  Physical Appearance

Flight Informa*on:

• Flight Code • Airline Operator • DeparTng Airport • Arrival Airport

Personal Informa*on:

• Name • Passport Number

• NaTonality • Age

Suspect Watchlist

JKDM HQ

Airports

Suspect KB

Rules

Profiling Rules using Ego-Group


Suspect Watchlist

Hector B.L

Hector Beltran Leyva

Adap*ve Personal

Demographic Analysis

(MI-‐Target)

Suspect KB

Rules

Suspect Network Analysis Engine (Mi-‐Visuali*cs)


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Mi-Visualitics Demo


CONCLUSION

Where was Semantic Technology applied in Knowledge Management ?

•  Knowledge Harvesting (LOD) •  Social Media Harvesting & Filtering •  Knowledge Asset Organization &

Management •  Data Harmonization •  Text Understanding •  Natural Language Question Answering •  Semantic Search •  Social Media Intelligence •  Social Network Intelligence •  Knowledge Management Portal


ありがとうございます

Knowledge Audit


58


Sensor Web


ASSETS



Ontology

Technical WriTng

Term Databanks

Machine TranslaTon

Human TranslaTon

InformaTonRetrieval


Consumer InformaTon

R&D

StandardisaTon

Nomenclature

Terminology

AGROVOC

MYGMO

PADIPEDIA

AGRIS

HERBAL MEDICINE


59

SNOMED-CT STW

2015 11-4-v14-ickm2015-keynote

Data & Analytics

Transcript of 2015 11-4-v14-ickm2015-keynote