Inheritance in Semantic Networks
Formal vs. Informal Semantic Networks
Hierarchical Relations
Modelling Process
Objects vs. Attributs
Inferred Relations
Ordering Relations and Attributes
Extensions and Roles
Meta-Properties
The document describes a modeling procedure for business process modeling notation (BPMN) that involves four phases:
1) Blackboxing to analyze the overall building block and identify key artifacts.
2) Structuring to determine the main steps, classify artifacts, and add checkpoints.
3) Re-construction to synthesize an initial process skeleton and collect test scenarios.
4) Instrumentation to enrich the process with more automated activities, business logic, and finalize test scenarios.
The goal is to iteratively model business processes in a collaborative way between business and IT to produce executable diagrams.
Modelling Software Requirements: Important diagrams and templates (lecture sl...Dagmar Monett
Online lecture at the School of Computer Science, University of Hertfordshire, Hatfield, UK, as part of the 11th Europe Week from 2nd to 6th March 2015.
This document outlines a scenario planning exercise for an MBA program. It includes:
- An introduction to scenario planning and its objectives of experiencing the process and anticipating future trends.
- A schedule for the scenario planning session, including an introduction, group work analyzing an UPS case study, and group presentations.
- An overview of scenario planning methodology involving defining uncertainties, building scenarios, assessing implications and identifying early signals.
- Instructions for a short scenario planning group exercise, guiding participants through the key stages of defining the issue, uncertainties, scenarios and options for their organization.
Scatter diagrams, strong and weak correlation, positive and negative correlation, lines of best fit, extrapolation and interpolation. Aimed at UK level 2 students on Access and GCSE Maths courses.
A sales forecast is a projection of expected customer demand for products or services over a specific time horizon and under certain assumptions. It is an essential tool for business planning, marketing, and management decision making that can help achieve sales goals, drive revenue, and reduce costs. Sales forecasts are influenced by both external factors like the economy and competition, and internal factors like prices and new product lines. Common sales forecasting methods include qualitative approaches like executive opinions and surveys, and quantitative approaches like time series analysis, regression analysis, and market testing.
Computer models and simulations are used to predict how systems will behave without having to create physical systems. They use mathematical formulas and past data to mimic real-life situations. While not perfectly accurate, models allow testing of systems like cars, weather patterns, bridges and businesses in a safe, cost-effective manner. Examples given include using models to design safer cars, forecast weather, test bridge designs, predict business profits, and train pilots via realistic flight simulators.
The document describes a modeling procedure for business process modeling notation (BPMN) that involves four phases:
1) Blackboxing to analyze the overall building block and identify key artifacts.
2) Structuring to determine the main steps, classify artifacts, and add checkpoints.
3) Re-construction to synthesize an initial process skeleton and collect test scenarios.
4) Instrumentation to enrich the process with more automated activities, business logic, and finalize test scenarios.
The goal is to iteratively model business processes in a collaborative way between business and IT to produce executable diagrams.
Modelling Software Requirements: Important diagrams and templates (lecture sl...Dagmar Monett
Online lecture at the School of Computer Science, University of Hertfordshire, Hatfield, UK, as part of the 11th Europe Week from 2nd to 6th March 2015.
This document outlines a scenario planning exercise for an MBA program. It includes:
- An introduction to scenario planning and its objectives of experiencing the process and anticipating future trends.
- A schedule for the scenario planning session, including an introduction, group work analyzing an UPS case study, and group presentations.
- An overview of scenario planning methodology involving defining uncertainties, building scenarios, assessing implications and identifying early signals.
- Instructions for a short scenario planning group exercise, guiding participants through the key stages of defining the issue, uncertainties, scenarios and options for their organization.
Scatter diagrams, strong and weak correlation, positive and negative correlation, lines of best fit, extrapolation and interpolation. Aimed at UK level 2 students on Access and GCSE Maths courses.
A sales forecast is a projection of expected customer demand for products or services over a specific time horizon and under certain assumptions. It is an essential tool for business planning, marketing, and management decision making that can help achieve sales goals, drive revenue, and reduce costs. Sales forecasts are influenced by both external factors like the economy and competition, and internal factors like prices and new product lines. Common sales forecasting methods include qualitative approaches like executive opinions and surveys, and quantitative approaches like time series analysis, regression analysis, and market testing.
Computer models and simulations are used to predict how systems will behave without having to create physical systems. They use mathematical formulas and past data to mimic real-life situations. While not perfectly accurate, models allow testing of systems like cars, weather patterns, bridges and businesses in a safe, cost-effective manner. Examples given include using models to design safer cars, forecast weather, test bridge designs, predict business profits, and train pilots via realistic flight simulators.
1. Data Formats and Recommendations
2. Basic Principle of the Import-Export Tool Explained
3. Opening of and Overview over Import-Export Tool
4. Mapping of Data Sources
3.1 Mapping of Objects
3.2 Mapping of Types
3.3 Mapping of Multiple Values
5. Import Behaviour
6. XML Import
7. Export
1. Use
2. Structure
3. The conditions in detail
3.1 Property Conditions and Scheme
3.2 Identification of objects
3.3 Types of inquiry structures to the network
The document discusses distributed online learning techniques used at Sift Science for fraud detection. Sift Science collects time series event data from customers to build machine learning models with over 1,000 features. Models are updated continuously based on new data and labels. Sift Science uses a distributed database (HBase) to store sparse feature sets and model parameters and update them incrementally in real-time. Caching and batching techniques achieve high performance while maintaining consistency. The system handles billions of events and scores hundreds of millions of users daily for online fraud detection.
L3: architecture and services (english)medialeg gmbh
The core of any i-views installation is the semantic graph database.
It consists of three files which reside in a folder carrying the name of the database.
This folder is nested in another folder named volumes.
Useful Techniques in Artificial IntelligenceIla Group
The document discusses artificial intelligence techniques presented by Dr. Will Browne at Cranfield University. It provides examples of applications of AI techniques in various fields such as finance, industry, engineering and control. It then describes common AI techniques such as expert systems, case-based reasoning, genetic algorithms, neural networks, fuzzy logic and cellular automata. The document emphasizes exploring appropriate techniques for problems and avoiding issues like lack of transparency, garbage in-garbage out, and difficulties generalizing from training data.
Scaling up digital learning support for smart workforce development in cluste...Ralf Klamma
4th Research Forum on Small and Medium Sized Enterprises, Chur, Switzerland, February 9-10, 2015
Ralf Klamma & Tobias Ley
RWTH Aachen University, Germany & Tallinn University, Estonia
klamma@dbis.rwth-aachen.de & tley@tlu.ee
This is a 2 hours overview on the deep learning status as for Q1 2017.
Starting with some basic concepts, continue to basic networks topologies , tools, HW/Accelerators and finally Intel's take on the the different fronts.
C19013010 the tutorial to build shared ai services session 2Bill Liu
This document provides an agenda and overview for a tutorial on building shared AI services. The session will cover AI engineering platforms, data pipelines, traditional AI roles and their challenges, skills required for AI engineers, and benchmarking machine learning and deep learning approaches. It includes a live demo of building an end-to-end AI pipeline with Kafka, NiFi, Spark Streaming and Keras on Spark.
Nervana was just acquired by Intel for their capabilities and platform focused on Deep Learning and Artificial Intelligence. This is an overview deck of what they do.
Keras Tutorial For Beginners | Creating Deep Learning Models Using Keras In P...Edureka!
** AI & Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka Tutorial on "Keras Tutorial" (Deep Learning Blog Series: https://goo.gl/4zxMfU) provides you a quick and insightful tutorial on the working of Keras along with an interesting use-case! We will be checking out the following topics:
Agenda:
What is Keras?
Who makes Keras?
Who uses Keras?
What Makes Keras special?
Working principle of Keras
Keras Models
Understanding Execution
Implementing a Neural Network
Use-Case with Keras
Coding in Colaboratory
Session in a minute
Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This document is a resume for Shashi Shekar.B summarizing his objective, education, experience, skills, and personal details. His objective is to find a challenging job that provides career growth. He has a Bachelor's degree in Computer Science from Visveshrpura Science College in Bangalore. Currently he works as a Technical Support Engineer at Hewlett-Packard supporting Qualcomm processes. He has over 10 years of experience in technical support roles and seeks to leverage his skills in hardware, software, operating systems, and networking.
This document discusses scripting in i-views. It covers:
1. Use cases for scripting including triggers, reports, REST API, data mappings, object lists, and scripts as attributes.
2. The structure of the JavaScript API and how it represents semantic graph database components.
3. Editing and debugging scripts using the integrated editor.
4. Limitations of scripting and examples/caveats around choices, intervals, queries, and locators.
5. Homework assignments involving REST scripts, structured queries, and outputting composer data.
• Access and edit the semantic graph database via REST-Services
• Specify what can be seen and how it is displayed and edited via view configurations
• Make use of pre-defined resources
• Compose the back-end part for your own web-frontends
L11: panels and view configurations (advanced)medialeg gmbh
Web Applications with panels and view configurations
1. Overview of the configuration elements of a web application
1.1. Panels
1.2. Actions
1.3. View configurations
2. Preconditions to build a web application
3. Proceeding to build a web application
4. Panels
5. Basic knowledge: View configurations
1. Introduction and overview
2. Building a simple query
3. Specific queries
1. Trigram query
2. Query with parametrized hit quality
3. Folder query
4. Queries with regular expressions
4. Full text search
5. Indexing
This document discusses how to build a basic application frontend in i-views that visualizes connected data from a database. It involves creating an object schema with types for companies, people, and projects linked by relations. Sample data is imported and a structured query is defined to find active projects for a selected company. The view is configured with two side-by-side panels - one listing companies, and one listing related projects. When a company is clicked, the projects panel updates to show those for that company using the configured query and view mappings. Homework suggests recreating a similar scenario and reviewing past lessons for clarification.
1. Overview of the configuration elements of a web application
1.2. Panels
1.3. Actions
1.4. View configurations
2. Preconditions to build a web application
3. Proceeding to build a web application
4. Panels
5. Basic knowledge: View configurations
This document covers advanced structured query concepts in i-views, including utility queries, local macros, registered macros, and scripting possibilities. Utility queries allow connecting queries with logical operations like negation and filtering. Local macros can replace long condition branches that are reused in a query to make it more readable. Registered macros make reusable condition paths available across multiple queries. Scripting enables enhanced value comparisons and accessing structured queries from JavaScript. The homework assigns practicing these concepts by building queries using local macros, registered macros with identifiers, and attribute conditions with scripted comparisons.
Medialeg is a company specialised in Social Media and Online Marketing. Learn how to use Facebook, Twitter and other online tools to market your goods and services. Have a look at attractionsguide.ch to see how it is implemented.
1. Data Formats and Recommendations
2. Basic Principle of the Import-Export Tool Explained
3. Opening of and Overview over Import-Export Tool
4. Mapping of Data Sources
3.1 Mapping of Objects
3.2 Mapping of Types
3.3 Mapping of Multiple Values
5. Import Behaviour
6. XML Import
7. Export
1. Use
2. Structure
3. The conditions in detail
3.1 Property Conditions and Scheme
3.2 Identification of objects
3.3 Types of inquiry structures to the network
The document discusses distributed online learning techniques used at Sift Science for fraud detection. Sift Science collects time series event data from customers to build machine learning models with over 1,000 features. Models are updated continuously based on new data and labels. Sift Science uses a distributed database (HBase) to store sparse feature sets and model parameters and update them incrementally in real-time. Caching and batching techniques achieve high performance while maintaining consistency. The system handles billions of events and scores hundreds of millions of users daily for online fraud detection.
L3: architecture and services (english)medialeg gmbh
The core of any i-views installation is the semantic graph database.
It consists of three files which reside in a folder carrying the name of the database.
This folder is nested in another folder named volumes.
Useful Techniques in Artificial IntelligenceIla Group
The document discusses artificial intelligence techniques presented by Dr. Will Browne at Cranfield University. It provides examples of applications of AI techniques in various fields such as finance, industry, engineering and control. It then describes common AI techniques such as expert systems, case-based reasoning, genetic algorithms, neural networks, fuzzy logic and cellular automata. The document emphasizes exploring appropriate techniques for problems and avoiding issues like lack of transparency, garbage in-garbage out, and difficulties generalizing from training data.
Scaling up digital learning support for smart workforce development in cluste...Ralf Klamma
4th Research Forum on Small and Medium Sized Enterprises, Chur, Switzerland, February 9-10, 2015
Ralf Klamma & Tobias Ley
RWTH Aachen University, Germany & Tallinn University, Estonia
klamma@dbis.rwth-aachen.de & tley@tlu.ee
This is a 2 hours overview on the deep learning status as for Q1 2017.
Starting with some basic concepts, continue to basic networks topologies , tools, HW/Accelerators and finally Intel's take on the the different fronts.
C19013010 the tutorial to build shared ai services session 2Bill Liu
This document provides an agenda and overview for a tutorial on building shared AI services. The session will cover AI engineering platforms, data pipelines, traditional AI roles and their challenges, skills required for AI engineers, and benchmarking machine learning and deep learning approaches. It includes a live demo of building an end-to-end AI pipeline with Kafka, NiFi, Spark Streaming and Keras on Spark.
Nervana was just acquired by Intel for their capabilities and platform focused on Deep Learning and Artificial Intelligence. This is an overview deck of what they do.
Keras Tutorial For Beginners | Creating Deep Learning Models Using Keras In P...Edureka!
** AI & Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka Tutorial on "Keras Tutorial" (Deep Learning Blog Series: https://goo.gl/4zxMfU) provides you a quick and insightful tutorial on the working of Keras along with an interesting use-case! We will be checking out the following topics:
Agenda:
What is Keras?
Who makes Keras?
Who uses Keras?
What Makes Keras special?
Working principle of Keras
Keras Models
Understanding Execution
Implementing a Neural Network
Use-Case with Keras
Coding in Colaboratory
Session in a minute
Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This document is a resume for Shashi Shekar.B summarizing his objective, education, experience, skills, and personal details. His objective is to find a challenging job that provides career growth. He has a Bachelor's degree in Computer Science from Visveshrpura Science College in Bangalore. Currently he works as a Technical Support Engineer at Hewlett-Packard supporting Qualcomm processes. He has over 10 years of experience in technical support roles and seeks to leverage his skills in hardware, software, operating systems, and networking.
This document discusses scripting in i-views. It covers:
1. Use cases for scripting including triggers, reports, REST API, data mappings, object lists, and scripts as attributes.
2. The structure of the JavaScript API and how it represents semantic graph database components.
3. Editing and debugging scripts using the integrated editor.
4. Limitations of scripting and examples/caveats around choices, intervals, queries, and locators.
5. Homework assignments involving REST scripts, structured queries, and outputting composer data.
• Access and edit the semantic graph database via REST-Services
• Specify what can be seen and how it is displayed and edited via view configurations
• Make use of pre-defined resources
• Compose the back-end part for your own web-frontends
L11: panels and view configurations (advanced)medialeg gmbh
Web Applications with panels and view configurations
1. Overview of the configuration elements of a web application
1.1. Panels
1.2. Actions
1.3. View configurations
2. Preconditions to build a web application
3. Proceeding to build a web application
4. Panels
5. Basic knowledge: View configurations
1. Introduction and overview
2. Building a simple query
3. Specific queries
1. Trigram query
2. Query with parametrized hit quality
3. Folder query
4. Queries with regular expressions
4. Full text search
5. Indexing
This document discusses how to build a basic application frontend in i-views that visualizes connected data from a database. It involves creating an object schema with types for companies, people, and projects linked by relations. Sample data is imported and a structured query is defined to find active projects for a selected company. The view is configured with two side-by-side panels - one listing companies, and one listing related projects. When a company is clicked, the projects panel updates to show those for that company using the configured query and view mappings. Homework suggests recreating a similar scenario and reviewing past lessons for clarification.
1. Overview of the configuration elements of a web application
1.2. Panels
1.3. Actions
1.4. View configurations
2. Preconditions to build a web application
3. Proceeding to build a web application
4. Panels
5. Basic knowledge: View configurations
This document covers advanced structured query concepts in i-views, including utility queries, local macros, registered macros, and scripting possibilities. Utility queries allow connecting queries with logical operations like negation and filtering. Local macros can replace long condition branches that are reused in a query to make it more readable. Registered macros make reusable condition paths available across multiple queries. Scripting enables enhanced value comparisons and accessing structured queries from JavaScript. The homework assigns practicing these concepts by building queries using local macros, registered macros with identifiers, and attribute conditions with scripted comparisons.
Medialeg is a company specialised in Social Media and Online Marketing. Learn how to use Facebook, Twitter and other online tools to market your goods and services. Have a look at attractionsguide.ch to see how it is implemented.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
3. Advanced ModellingAdvanced Modelling
3
If you have problems with the sound:
- Check the audio settings in menu item „Communicate“
- Join alternatively by telephone or by dial-in number
from landline phone (for free):
• 000-800-100-8171 for India
• 0800-894627 for Switzerland
from mobile phone
• +41 43456 9564 only for Switzerland
Event-Number: : 844 317 586
Event-Password: hello
5. Advanced ModellingAdvanced Modelling
5
Recap: Object-Type Hierarchies - Inheritance
• Properties are defined for types
• Properties can be inherited by connecting supertypes with subtypes
has sub-type
Also known as:
Top / subset
Top / sub type
"Is a"-relation
The attributes and
relations should be
positioned as far up
in the hierarchy as it
is necessary.
location time
6. Advanced ModellingAdvanced Modelling
6
Recap: Object-Type Hierarchies - Inheritance
• Properties are defined for types
• Properties can be inherited by connecting supertypes with subtypes
has sub-type
has sub-type
Also known as:
Top / subset
Top / sub type
"Is a"-relation
The attributes and
relations should be
positioned as far up
in the hierarchy as it
is necessary.
location time
location time
location time location time
location time
location time
location time
11. Advanced ModellingAdvanced Modelling
11
Informal vs. Formal Semantic Networks
• Noisy data
• Extended queries
• Thematically relevant information
• Personalised work environment
• Deductions and Recommendations
Informal model
Few Types and Relationtypes
Simple Inferencing
Rigid, formal model
Many Types and Relationtypes
Extensive Inferecing
16. Advanced ModellingAdvanced Modelling
16
Formal Semantic Networks
• Object-type hierarchy is well orderd
and categorised
• Object-type hierarchy levels are of
the same granularity
• Objects belong to the matching
object-type
19. Advanced ModellingAdvanced Modelling
19
Part-Of or Super-/Subtype-Of Hierachy
Should geographical objects
be presented through a part-
of hierarchy or a type
hierarchy?
General rule: object types
should not be modelled as
subtypes if their individual
objects are already part of
other object type‘s individual
objects.
20. Advanced ModellingAdvanced Modelling
20
Part-Of or Super-/Subtype-Of Hierachy
If we can assume a realistic model of geographic structures, i.e. a continent
consists of countries, which in turn includes its cities, this structure would
be translated into a part-of hierarchy with cities that are part of countries
that are part of continents.
Another general rule: If the
sentence „Any city is a country is a
continent“ is true, you should use
a type hierarchy. In this case, „Any
city is part of a country is part of a
continent“ is more appropriate
though, therefore we use a part-of
hierarchy.
21. Advanced ModellingAdvanced Modelling
21
Part-Of or Super-/Subtype Hierarchy
In this case, attributes are the main reason for
choosing a type hierarchy between these objects.
Possible Reasons for another layer:
• Number of subtypes
• “perceived distance/ togetherness” between the
sibling nodes
22. Advanced ModellingAdvanced Modelling
22
Part-Of or Super-/Subtype Hierarchy
Advantages of a new layer:
• Objects with a certain relatedness are closer to
each other (like Hpyertension and Arteriosclerosis)
Disadvantage of a new layer:
• Complicated, artificial inserted Objects (like
Disease of the respiratory system)
26. Advanced ModellingAdvanced Modelling
26
Bottom-Up or Top-Down
Start with either
• A model (object and relation types)
• Specific objects
• Queries and rules for assembling information
Most of the time it will be a combination
• Iterative approach
• Quick construction of a prototype
• On the fly changes of the model
• Exploration of the semantic network will join the setup from the beginning
27. Advanced ModellingAdvanced Modelling
27
Keep in Mind while Modelling/ Designing your Network
• The semantic model is supposed to support the search process and automatic combination of
information
• The semantic model should help you to avoid redundancies
• The semantic model is to present facts as precise as possible
• The semantic model should be easy to understand by externals
29. Advanced ModellingAdvanced Modelling
29
Relations vs. Attributes - Recommendations on Usage
Use Attributes when:
• Great variety of information and barely any
repetition in values
Use Objects when:
• Many objects share the same „property“/
information
• The information is itself a complex
information with its own properties
Term x
Term x
Synonym
Term y
Term z Synonym
32. Advanced ModellingAdvanced Modelling
32
Inferred Relation
• At least two connected relations can
create a new inferred relation
• It builts a shortcut between objects,
well used paths don’t have to be
defined new every time
• Example: the song Back to Black from
the album Back to Black is connected
to three moods. These moods could
also be connected to the album itself.
An inferred relation defines this
connection.
34. Advanced ModellingAdvanced Modelling
34
Relations-/Attribute Hierarchy
• Like types, relations can be defined in a hierarchy
• The domain and target domain won‘t be inherited by the
subtypes
• It is possible to create abstract relations
• This hierarchy can be used for
• A better overview
• Which relations are possible between certain types?
• Implications
• Which can be used in structured queries
• Instead of using three different relations (with different domains
and target domains), a grouping relation can combine all three of
them (e.g. Role Is Present on Song)
36. Advanced ModellingAdvanced Modelling
36
Recap: Extensions and Roles
• Objects can only be of one type.
• You have to use extensions with their own relations and attributes if you
want to express additional features.
• Examplary use cases:
• Representation of temporary properties
(position in a company; status of a project)
• Role based permission system
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Recap: Extensions
• Extensions for time-limited roles (position
in a company; Project status)
• Example: Objects of type person will be
extended through:
• Roll bassist
• Roll producer
• Roll composer
• ...
• Changes for the role of the person must
be made only on the role object, not on
all people associated.
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Recap: Extensions
• A person can receive more than one
role at the same time
• Thanks to the extension, new relations
and attributes are available for the
extended object that were not
previously approved by the schema
(for example : Jenny Conlee
"participates in" Allright as
accordionist)
• This connection can not be illustrated
through "normal", separate relations
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Exercises
1. Revisit your Semantic Model from the lesson 1 exercise and think about ways to improve the
semantic network. Explain the changes you conducted and why.
2. Many organisations have more than one site, which can be identified by their street names, street
numbers, zip codes, cities and countries. Additionally, there is quite a high number of organisations
that are supposed to be represented by the model over a long timeframe. Choose a fitting way to
model this situation.
3. An organization employs 2 employees: Tim as am manager and Tom as an assembly-line worker. Two
years ago Tim used to be an assembly-line worker, too (before he got promoted to the manager-
position). Please model the scenario.