SlideShare une entreprise Scribd logo
1  sur  6
Télécharger pour lire hors ligne
KNOWLEDGE GRAPH
THE NEURORGANON APROACH
By Athanassios I. Hatzis, PhD
Tuesday18th of December 2012
From Neural Networks
To Concept Networks
 NEURON
  The fundamental unit of neural networks


 NULON and IR
  NULON - The Neurorganon Upper Level Ontology
       The fundamental upper-ontology for the construction of concept
       networks
  IR     Information Resource/Reference
       The fundamental unit of construction in NULON
The Problem               User Perspective
 From
      The World Wide Web of Documents
 To
      The Giant Global Graph of Concepts
 From Document sharing to Content sharing
 From Document collections to Linked Information
 From Hierarchical Structure to Graph Structure
 From Document searching to Graph queries
 From Document Publishing to Content Publishing
 From Anonymous Files to Authoritative Content
 From Social Networks to Decentralized Communities
The Problem    Machine Perspective
   View Model (Presentation)
     Human Understanding

   Graph Model
    Concept Modeling
                               The Gap
   Data Model (Representation)
    Machine Storage/Access/Retrieval
Five Layer Neurorganon Architecture
 Data Layer    Meta-Data Layer   Conceptual   Analysis Layer   Presentation
    (DL)            (ML)         Layer (CL)        (AL)         Layer (PL)


Data Sources
                 Acquisition
                                                Inference
                   Engine
                                                 Engine
                                  Graph                         Web
                                  Engine                       Engine

                  Indexing                      Statistics
                   Engine                        Engine

   Graph
  Database
Implementation
 Web Engine (PL)          Statistical Engine (AL)
   Web Templates            Analytical modeling
                          Indexing Engine (ML)
   Information graphics
                            Interoperability
 Inference Engine (AL)      Tagging
   Reasoning Engine       Acquisition Engine (ML)
   Search Engine            Extraction
                            Transformation
 Graph Engine (CL)
                            Integration
   Logical Layer
                          Data Sources (DL)
   Conceptual Layer         Documents
 Database Engine (DL)       Relational DBs
   Graph Database           Structured Data

Contenu connexe

Tendances

Bi g data_urban modeling_applications_23092013
Bi g data_urban modeling_applications_23092013Bi g data_urban modeling_applications_23092013
Bi g data_urban modeling_applications_23092013
Vahid Moosavi
 

Tendances (8)

TinkerPop 2020
TinkerPop 2020TinkerPop 2020
TinkerPop 2020
 
Big Graph : Tools, Techniques, Issues, Challenges and Future Directions
Big Graph : Tools, Techniques, Issues, Challenges and Future Directions Big Graph : Tools, Techniques, Issues, Challenges and Future Directions
Big Graph : Tools, Techniques, Issues, Challenges and Future Directions
 
Graph analytics in Linkurious Enterprise
Graph analytics in Linkurious EnterpriseGraph analytics in Linkurious Enterprise
Graph analytics in Linkurious Enterprise
 
DC02. Interpretation of predictions
DC02. Interpretation of predictionsDC02. Interpretation of predictions
DC02. Interpretation of predictions
 
Bi g data_urban modeling_applications_23092013
Bi g data_urban modeling_applications_23092013Bi g data_urban modeling_applications_23092013
Bi g data_urban modeling_applications_23092013
 
2. BigQuery ML を用いた時系列データの解析 (ARIMA model)
2. BigQuery ML を用いた時系列データの解析 (ARIMA model)2. BigQuery ML を用いた時系列データの解析 (ARIMA model)
2. BigQuery ML を用いた時系列データの解析 (ARIMA model)
 
Workshop on Real-time & Stream Analytics IEEE BigData 2016
Workshop on Real-time & Stream Analytics IEEE BigData 2016Workshop on Real-time & Stream Analytics IEEE BigData 2016
Workshop on Real-time & Stream Analytics IEEE BigData 2016
 
Forces and Threats in a Data Warehouse (and why metadata and architecture is ...
Forces and Threats in a Data Warehouse (and why metadata and architecture is ...Forces and Threats in a Data Warehouse (and why metadata and architecture is ...
Forces and Threats in a Data Warehouse (and why metadata and architecture is ...
 

Similaire à From NEURON to NULON

Tracing Networks: Ontology Software in a Nutshell
Tracing Networks: Ontology Software in a NutshellTracing Networks: Ontology Software in a Nutshell
Tracing Networks: Ontology Software in a Nutshell
enoch1982
 
Adcom2006 Full 6
Adcom2006 Full 6Adcom2006 Full 6
Adcom2006 Full 6
umavanth
 
Natural Language Processing & Semantic Models in an Imperfect World
Natural Language Processing & Semantic Modelsin an Imperfect WorldNatural Language Processing & Semantic Modelsin an Imperfect World
Natural Language Processing & Semantic Models in an Imperfect World
Vital.AI
 
Introduction to Apache Drill - Big Data Bellevue Meetup 20131023
Introduction to Apache Drill - Big Data Bellevue Meetup 20131023Introduction to Apache Drill - Big Data Bellevue Meetup 20131023
Introduction to Apache Drill - Big Data Bellevue Meetup 20131023
Timothy Chen
 
Syntactic Mediation in Grid and Web Service Architectures
Syntactic Mediation in Grid and Web Service ArchitecturesSyntactic Mediation in Grid and Web Service Architectures
Syntactic Mediation in Grid and Web Service Architectures
Martin Szomszor
 
Semantic Technolgy
Semantic TechnolgySemantic Technolgy
Semantic Technolgy
Talat Fakhri
 
stackconf 2022: Introduction to Vector Search with Weaviate
stackconf 2022: Introduction to Vector Search with Weaviatestackconf 2022: Introduction to Vector Search with Weaviate
stackconf 2022: Introduction to Vector Search with Weaviate
NETWAYS
 

Similaire à From NEURON to NULON (20)

Tracing Networks: Ontology Software in a Nutshell
Tracing Networks: Ontology Software in a NutshellTracing Networks: Ontology Software in a Nutshell
Tracing Networks: Ontology Software in a Nutshell
 
STI Summit 2011 - Digital Worlds
STI Summit 2011 - Digital WorldsSTI Summit 2011 - Digital Worlds
STI Summit 2011 - Digital Worlds
 
Adcom2006 Full 6
Adcom2006 Full 6Adcom2006 Full 6
Adcom2006 Full 6
 
Azure databricks ml
Azure databricks mlAzure databricks ml
Azure databricks ml
 
Vital AI: Big Data Modeling
Vital AI: Big Data ModelingVital AI: Big Data Modeling
Vital AI: Big Data Modeling
 
Ted Willke, Senior Principal Engineer & GM, Datacenter Group, Intel at MLconf SF
Ted Willke, Senior Principal Engineer & GM, Datacenter Group, Intel at MLconf SFTed Willke, Senior Principal Engineer & GM, Datacenter Group, Intel at MLconf SF
Ted Willke, Senior Principal Engineer & GM, Datacenter Group, Intel at MLconf SF
 
Koss 1605 machine_learning_mariocho_t10
Koss 1605 machine_learning_mariocho_t10Koss 1605 machine_learning_mariocho_t10
Koss 1605 machine_learning_mariocho_t10
 
51 Use Cases and implications for HPC & Apache Big Data Stack
51 Use Cases and implications for HPC & Apache Big Data Stack51 Use Cases and implications for HPC & Apache Big Data Stack
51 Use Cases and implications for HPC & Apache Big Data Stack
 
Natural Language Processing & Semantic Models in an Imperfect World
Natural Language Processing & Semantic Modelsin an Imperfect WorldNatural Language Processing & Semantic Modelsin an Imperfect World
Natural Language Processing & Semantic Models in an Imperfect World
 
Exploration of a Data Landscape using a Collaborative Linked Data Framework.
Exploration of a Data Landscape using a Collaborative Linked Data Framework.Exploration of a Data Landscape using a Collaborative Linked Data Framework.
Exploration of a Data Landscape using a Collaborative Linked Data Framework.
 
Skills in Artificial Intelligence
Skills in Artificial IntelligenceSkills in Artificial Intelligence
Skills in Artificial Intelligence
 
TUW- 184.742 Emerging Dynamic Distributed Systems and Challenges for Advanced...
TUW- 184.742 Emerging Dynamic Distributed Systems and Challenges for Advanced...TUW- 184.742 Emerging Dynamic Distributed Systems and Challenges for Advanced...
TUW- 184.742 Emerging Dynamic Distributed Systems and Challenges for Advanced...
 
Coping with Data Variety in the Big Data Era: The Semantic Computing Approach
Coping with Data Variety in the Big Data Era: The Semantic Computing ApproachCoping with Data Variety in the Big Data Era: The Semantic Computing Approach
Coping with Data Variety in the Big Data Era: The Semantic Computing Approach
 
AI and Machine Learning for .net developers
AI and Machine Learning for .net developersAI and Machine Learning for .net developers
AI and Machine Learning for .net developers
 
Compositional AI: Fusion of AI/ML Services
Compositional AI: Fusion of AI/ML ServicesCompositional AI: Fusion of AI/ML Services
Compositional AI: Fusion of AI/ML Services
 
Introduction to Apache Drill - Big Data Bellevue Meetup 20131023
Introduction to Apache Drill - Big Data Bellevue Meetup 20131023Introduction to Apache Drill - Big Data Bellevue Meetup 20131023
Introduction to Apache Drill - Big Data Bellevue Meetup 20131023
 
A vision on collaborative computation of things for personalized analyses
A vision on collaborative computation of things for personalized analysesA vision on collaborative computation of things for personalized analyses
A vision on collaborative computation of things for personalized analyses
 
Syntactic Mediation in Grid and Web Service Architectures
Syntactic Mediation in Grid and Web Service ArchitecturesSyntactic Mediation in Grid and Web Service Architectures
Syntactic Mediation in Grid and Web Service Architectures
 
Semantic Technolgy
Semantic TechnolgySemantic Technolgy
Semantic Technolgy
 
stackconf 2022: Introduction to Vector Search with Weaviate
stackconf 2022: Introduction to Vector Search with Weaviatestackconf 2022: Introduction to Vector Search with Weaviate
stackconf 2022: Introduction to Vector Search with Weaviate
 

From NEURON to NULON

  • 1. KNOWLEDGE GRAPH THE NEURORGANON APROACH By Athanassios I. Hatzis, PhD Tuesday18th of December 2012
  • 2. From Neural Networks To Concept Networks NEURON The fundamental unit of neural networks NULON and IR NULON - The Neurorganon Upper Level Ontology The fundamental upper-ontology for the construction of concept networks IR Information Resource/Reference The fundamental unit of construction in NULON
  • 3. The Problem User Perspective From The World Wide Web of Documents To The Giant Global Graph of Concepts From Document sharing to Content sharing From Document collections to Linked Information From Hierarchical Structure to Graph Structure From Document searching to Graph queries From Document Publishing to Content Publishing From Anonymous Files to Authoritative Content From Social Networks to Decentralized Communities
  • 4. The Problem Machine Perspective View Model (Presentation) Human Understanding Graph Model Concept Modeling The Gap Data Model (Representation) Machine Storage/Access/Retrieval
  • 5. Five Layer Neurorganon Architecture Data Layer Meta-Data Layer Conceptual Analysis Layer Presentation (DL) (ML) Layer (CL) (AL) Layer (PL) Data Sources Acquisition Inference Engine Engine Graph Web Engine Engine Indexing Statistics Engine Engine Graph Database
  • 6. Implementation Web Engine (PL) Statistical Engine (AL) Web Templates Analytical modeling Indexing Engine (ML) Information graphics Interoperability Inference Engine (AL) Tagging Reasoning Engine Acquisition Engine (ML) Search Engine Extraction Transformation Graph Engine (CL) Integration Logical Layer Data Sources (DL) Conceptual Layer Documents Database Engine (DL) Relational DBs Graph Database Structured Data