Waarom en hoe semantische netwerken. legt uit dat iedereen die nooit van semantische netwerken, ontologiëen, thesaurus, taxonomieén heeft gehoord toch van nature al vertrouwd is met het begrip semantische netwerken. Settembrini Publishers helpt u met het opzetten en onderhouden van dergelijke netwerken.
This document summarizes rules for forming comparative adjectives in English in 3 or fewer sentences. One-syllable adjectives form the comparative by adding -er. Two-syllable adjectives usually take more before them to form the comparative, except for those ending in -y which change to -ier. A few adjectives like good, bad, far, little, and many are irregular and have unique forms for the comparative.
This is the presentation Victor Montori (KER UNIT, Healthcare Delivery Research Program, Mayo Clinic) gave at the Normalization Process Theory symposium at King's Fund, London, UK on October 22, 2010.
Presentaties over welke effecten de komst van Internet en Sociale Media heeft op ons hele leven: privé, zakelijk, maatschappelijk. En ik vrees dat ook deze ingrijpende sociale revolutie zoals alle revoluties noodzakelijker wijze eindigen in chaos en revolutionaire tijden. Dat brengt zelden iets goeds
Planificación y objetivos en una acción de emailmarketingNoé Soriano
El documento describe un ejemplo de acción de email marketing para una agencia que desea comunicar a sus clientes sobre las nuevas penalizaciones de Google y ofrecer sus servicios de diseño web. La agencia ya tiene un departamento de diseño y un proveedor de correo electrónico, por lo que puede diseñar y enviar un boletín informativo a sus clientes de manera rentable para captar nuevos pedidos.
The session layer is responsible for dialog control, synchronization, activity management, and exception handling between communicating processes. Dialog control determines whose turn it is to transmit data using a data token. Synchronization allows processes to add checkpoints to data streams using sequence numbers. Activity management identifies where data starts and ends by dividing streams into activities. Exception handling reports errors. Common session layer protocols are PPTP, RPC, and RTCP.
The presentation layer is responsible for translation, encryption, and compression. Translation converts data to bit streams and between encoding formats using common standards like ASCII and EBCDIC. Encryption ensures privacy by transforming data cryptographically before transmission using techniques like cryptography. Compression represents data using the fewest bits possible
Semantic Networks are a useful tool for businesses to order, maintain and input data about there business process and to publishing these data on the Internet, APPs and webshops.
This document discusses how to form superlative adjectives in English. It explains that one-syllable adjectives form the superlative by adding -est. Two-syllable adjectives usually take the most. Some irregular adjectives have completely different superlative forms, such as good becoming best and bad becoming worst. The superlative is used to compare three or more nouns, while the comparative compares two.
The document discusses heap memory management. It describes the heap as the portion of memory used for indefinitely stored data. A memory manager subsystem allocates and deallocates space in the heap. It keeps track of free space and serves as the interface between programs and the operating system. When allocating memory, the manager either uses available contiguous space or increases heap size from the OS. Deallocated space is returned to the free pool but memory is not returned to the OS when heap usage drops.
This document summarizes rules for forming comparative adjectives in English in 3 or fewer sentences. One-syllable adjectives form the comparative by adding -er. Two-syllable adjectives usually take more before them to form the comparative, except for those ending in -y which change to -ier. A few adjectives like good, bad, far, little, and many are irregular and have unique forms for the comparative.
This is the presentation Victor Montori (KER UNIT, Healthcare Delivery Research Program, Mayo Clinic) gave at the Normalization Process Theory symposium at King's Fund, London, UK on October 22, 2010.
Presentaties over welke effecten de komst van Internet en Sociale Media heeft op ons hele leven: privé, zakelijk, maatschappelijk. En ik vrees dat ook deze ingrijpende sociale revolutie zoals alle revoluties noodzakelijker wijze eindigen in chaos en revolutionaire tijden. Dat brengt zelden iets goeds
Planificación y objetivos en una acción de emailmarketingNoé Soriano
El documento describe un ejemplo de acción de email marketing para una agencia que desea comunicar a sus clientes sobre las nuevas penalizaciones de Google y ofrecer sus servicios de diseño web. La agencia ya tiene un departamento de diseño y un proveedor de correo electrónico, por lo que puede diseñar y enviar un boletín informativo a sus clientes de manera rentable para captar nuevos pedidos.
The session layer is responsible for dialog control, synchronization, activity management, and exception handling between communicating processes. Dialog control determines whose turn it is to transmit data using a data token. Synchronization allows processes to add checkpoints to data streams using sequence numbers. Activity management identifies where data starts and ends by dividing streams into activities. Exception handling reports errors. Common session layer protocols are PPTP, RPC, and RTCP.
The presentation layer is responsible for translation, encryption, and compression. Translation converts data to bit streams and between encoding formats using common standards like ASCII and EBCDIC. Encryption ensures privacy by transforming data cryptographically before transmission using techniques like cryptography. Compression represents data using the fewest bits possible
Semantic Networks are a useful tool for businesses to order, maintain and input data about there business process and to publishing these data on the Internet, APPs and webshops.
This document discusses how to form superlative adjectives in English. It explains that one-syllable adjectives form the superlative by adding -est. Two-syllable adjectives usually take the most. Some irregular adjectives have completely different superlative forms, such as good becoming best and bad becoming worst. The superlative is used to compare three or more nouns, while the comparative compares two.
The document discusses heap memory management. It describes the heap as the portion of memory used for indefinitely stored data. A memory manager subsystem allocates and deallocates space in the heap. It keeps track of free space and serves as the interface between programs and the operating system. When allocating memory, the manager either uses available contiguous space or increases heap size from the OS. Deallocated space is returned to the free pool but memory is not returned to the OS when heap usage drops.
Here are the answers:
1. A rhyme is when words at the end of lines sound similar. Example: Night time by Lee Bennet Hopkins
3. Rhythm is a pattern of stressed and unstressed syllables in poetry. Example: Humpty Dumpty sat on a wall
5. Alliteration is when words are used in succession and begin with the same consonant sound. Example: Sheila Shorter sought a suitor;
Semantic networks are a knowledge representation technique where concepts are represented as nodes in a graph, and relationships between concepts are represented as links between nodes. There are different types of semantic networks, including definitional networks that emphasize subclass relationships, assertional networks for making propositions, and executable networks that can change based on operations. Common semantic relations include IS-A for subclasses, INSTANCE for examples, and HAS-PART for components. While semantic networks provide a natural representation of relationships, they have disadvantages like lack of standard link names and difficulty representing some logical constructs.
Settembrini Publishers is partnering with Unifiedroot S&M to design semantic networks for top-level domain name publishing services. Semantic networks organize information by linking related concepts and data, giving meaning to the relationships. They correspond to how humans naturally think about organizing information and are a flexible way to represent knowledge.
The document discusses how businesses must adapt to the changing social media landscape. It notes that in today's world, customers can be advocates or critics and that information is no longer controlled by companies. It also highlights challenges companies face with unclear social media objectives, underperforming initiatives, and lack of coordination across departments. The document argues that businesses need to become more social, connected and agile by planning for social business initiatives both internally and externally. This will help companies address issues like real-time crisis management, social marketing, and embracing greater responsibility.
The document discusses concepts related to main memory management in operating systems. It covers how programs are loaded into memory to execute, the use of base and limit registers to define logical address spaces, and different methods of binding instructions and data to physical memory addresses. It also describes logical versus physical address spaces, the role of the memory management unit in mapping virtual to physical addresses, dynamic loading and linking of code, and swapping of processes in and out of main memory. Finally, it discusses issues like fragmentation that can occur with contiguous memory allocation and approaches for dynamic storage allocation and compaction.
A sewing machine is a machine used to stitch fabric and other materials together with thread. Sewing machines were invented during the first Industrial Revolution to decrease the amount of manual sewing work performed in clothing companies. Since the invention of the first working sewing machine, generally considered to have been the work of Englishman Thomas Saint in 1790, the sewing machine has greatly improved the efficiency and productivity of the cloth.
In 1790, the English inventor Thomas Saint invented the first sewing machine design, but he did not successfully advertise or market his invention. His machine was meant to be used on leather and canvas material.
In 1874, a sewing machine manufacturer, William Newton Wilson, found Saint's drawings in the London Patent Office, made adjustments to the looper, and built a working machine, currently owned by the London Science Museum.
In 1804, a sewing machine was built by the Englishmen Thomas Stone and James Henderson, and a machine for embroidering was constructed by John Duncan in Scotland.An Austrian tailor, Josef Madersperger, began developing his first sewing machine in 1807. He presented his first working machine in 1814.
This document discusses weak slot-and-filler knowledge representation structures. It describes how slots represent attributes and fillers represent values. Semantic networks are provided as an example where nodes represent objects/values and links represent relationships. Property inheritance allows subclasses to inherit attributes from more general superclasses. Frames are also discussed as a type of weak structure where each frame contains slots and associated values describing an entity. The document notes challenges with tangled hierarchies and provides examples of how to resolve conflicts through inferential distance in the property inheritance algorithm.
The document discusses basic blocks and flow graphs in program representation. It defines basic blocks as straight-line code segments with a single entry and exit point. To construct the representation:
1. The program is partitioned into basic blocks
2. A flow graph is created where basic blocks are nodes and edges show control flow between blocks
The flow graph explicitly represents all execution paths between basic blocks. Loops in the flow graph are identified by having a single loop entry node with all paths from the start going through it, and all nodes inside the loop reaching the entry.
The document discusses different knowledge representation schemes used in artificial intelligence systems. It describes semantic networks, frames, propositional logic, first-order predicate logic, and rule-based systems. For each technique, it provides facts about how knowledge is represented and examples to illustrate their use. The goal of knowledge representation is to encode knowledge in a way that allows inferencing and learning of new knowledge from the facts stored in the knowledge base.
Los rumiantes son herbívoros cuyo estómago está dividido en cuatro cámaras que les permiten fermentar la celulosa gracias a las bacterias presentes. La rumia es el proceso de regurgitación y remasticación del alimento que ayuda a su digestión. Las vacas digieren la celulosa gracias a las bacterias celulolíticas presentes en el rumen que producen enzimas llamadas celulasas capaces de hidrolizar la celulosa.
Knowledge Representation in Artificial intelligence Yasir Khan
This document discusses different methods of knowledge representation in artificial intelligence, including logical representations, semantic networks, production rules, and frames. Logical representations use formal logics like propositional logic and first-order predicate logic to represent facts and relationships. Semantic networks represent knowledge graphically as nodes and edges to model concepts and their relationships. Production rules represent knowledge as condition-action pairs to model problem-solving. Frames represent stereotyped situations as templates with slots to model attributes and behaviors. Choosing the right knowledge representation method is important for building successful AI systems.
The document discusses the knapsack problem, which involves selecting a subset of items that fit within a knapsack of limited capacity to maximize the total value. There are two versions - the 0-1 knapsack problem where items can only be selected entirely or not at all, and the fractional knapsack problem where items can be partially selected. Solutions include brute force, greedy algorithms, and dynamic programming. Dynamic programming builds up the optimal solution by considering all sub-problems.
The document discusses different types of knowledge that may need to be represented in AI systems, including objects, events, performance, and meta-knowledge. It also discusses representing knowledge at two levels: the knowledge level containing facts, and the symbol level containing representations of objects defined in terms of symbols. Common ways of representing knowledge mentioned include using English, logic, relations, semantic networks, frames, and rules. The document also discusses using knowledge for applications like learning, reasoning, and different approaches to machine learning such as skill refinement, knowledge acquisition, taking advice, problem solving, induction, discovery, and analogy.
Zara is a clothing retailer that uses modern technology in its marketing research and supply chain to quickly deliver fashionable designs at lower prices. It collects frequent customer feedback and uses IT to closely monitor trends. This allows Zara to make production decisions quickly and produce small quantities of many styles. As a result, Zara is able to deliver new fashion designs about twice a month while competitors take 3-5 months. This rapid turnover keeps customers engaged with frequent store visits and purchases.
This document discusses Zara's supply chain and how it contributes to the company's success. It provides details on Zara's vertically integrated supply chain model, which allows it to bring designs to stores in just 2-3 weeks compared to the industry average of 6-9 months. Key aspects of Zara's supply chain include local sourcing, fast production times, mass customization, and using IT to share information. This vertical integration model helps Zara increase revenue through more fashionable and scarce products, while decreasing costs through factors like lower transportation and inventory costs.
Language is acquired naturally, with meaning taking priority over structure, and reinforced through real-world experiences. As with first language acquisition, second language learners progress from single words to combining words based on meaning before identifying sentence elements, and can rearrange elements to form questions. Motivation and anxiety levels impact the language acquisition process, so teachers should provide instruction at a student's current proficiency level plus one additional level.
Artificial intelligence and knowledge representationLikan Patra
Artificial intelligence uses algorithms and knowledge representation to solve problems in a manner inspired by human intelligence. Knowledge representation involves using formal symbolic logic and structures like semantic networks and frames to represent knowledge. Different representation techniques exist for propositions, predicates, rules and nonmonotonic reasoning. Challenges for AI include acquiring knowledge autonomously, representing human experiences, and fully transferring human knowledge through communication.
Knowledge representation and Predicate logicAmey Kerkar
1. The document discusses knowledge representation and predicate logic.
2. It explains that knowledge representation involves representing facts through internal representations that can then be manipulated to derive new knowledge. Predicate logic allows representing objects and relationships between them using predicates, quantifiers, and logical connectives.
3. Several examples are provided to demonstrate representing simple facts about individuals as predicates and using quantifiers like "forall" and "there exists" to represent generalized statements.
Here are the answers:
1. A rhyme is when words at the end of lines sound similar. Example: Night time by Lee Bennet Hopkins
3. Rhythm is a pattern of stressed and unstressed syllables in poetry. Example: Humpty Dumpty sat on a wall
5. Alliteration is when words are used in succession and begin with the same consonant sound. Example: Sheila Shorter sought a suitor;
Semantic networks are a knowledge representation technique where concepts are represented as nodes in a graph, and relationships between concepts are represented as links between nodes. There are different types of semantic networks, including definitional networks that emphasize subclass relationships, assertional networks for making propositions, and executable networks that can change based on operations. Common semantic relations include IS-A for subclasses, INSTANCE for examples, and HAS-PART for components. While semantic networks provide a natural representation of relationships, they have disadvantages like lack of standard link names and difficulty representing some logical constructs.
Settembrini Publishers is partnering with Unifiedroot S&M to design semantic networks for top-level domain name publishing services. Semantic networks organize information by linking related concepts and data, giving meaning to the relationships. They correspond to how humans naturally think about organizing information and are a flexible way to represent knowledge.
The document discusses how businesses must adapt to the changing social media landscape. It notes that in today's world, customers can be advocates or critics and that information is no longer controlled by companies. It also highlights challenges companies face with unclear social media objectives, underperforming initiatives, and lack of coordination across departments. The document argues that businesses need to become more social, connected and agile by planning for social business initiatives both internally and externally. This will help companies address issues like real-time crisis management, social marketing, and embracing greater responsibility.
The document discusses concepts related to main memory management in operating systems. It covers how programs are loaded into memory to execute, the use of base and limit registers to define logical address spaces, and different methods of binding instructions and data to physical memory addresses. It also describes logical versus physical address spaces, the role of the memory management unit in mapping virtual to physical addresses, dynamic loading and linking of code, and swapping of processes in and out of main memory. Finally, it discusses issues like fragmentation that can occur with contiguous memory allocation and approaches for dynamic storage allocation and compaction.
A sewing machine is a machine used to stitch fabric and other materials together with thread. Sewing machines were invented during the first Industrial Revolution to decrease the amount of manual sewing work performed in clothing companies. Since the invention of the first working sewing machine, generally considered to have been the work of Englishman Thomas Saint in 1790, the sewing machine has greatly improved the efficiency and productivity of the cloth.
In 1790, the English inventor Thomas Saint invented the first sewing machine design, but he did not successfully advertise or market his invention. His machine was meant to be used on leather and canvas material.
In 1874, a sewing machine manufacturer, William Newton Wilson, found Saint's drawings in the London Patent Office, made adjustments to the looper, and built a working machine, currently owned by the London Science Museum.
In 1804, a sewing machine was built by the Englishmen Thomas Stone and James Henderson, and a machine for embroidering was constructed by John Duncan in Scotland.An Austrian tailor, Josef Madersperger, began developing his first sewing machine in 1807. He presented his first working machine in 1814.
This document discusses weak slot-and-filler knowledge representation structures. It describes how slots represent attributes and fillers represent values. Semantic networks are provided as an example where nodes represent objects/values and links represent relationships. Property inheritance allows subclasses to inherit attributes from more general superclasses. Frames are also discussed as a type of weak structure where each frame contains slots and associated values describing an entity. The document notes challenges with tangled hierarchies and provides examples of how to resolve conflicts through inferential distance in the property inheritance algorithm.
The document discusses basic blocks and flow graphs in program representation. It defines basic blocks as straight-line code segments with a single entry and exit point. To construct the representation:
1. The program is partitioned into basic blocks
2. A flow graph is created where basic blocks are nodes and edges show control flow between blocks
The flow graph explicitly represents all execution paths between basic blocks. Loops in the flow graph are identified by having a single loop entry node with all paths from the start going through it, and all nodes inside the loop reaching the entry.
The document discusses different knowledge representation schemes used in artificial intelligence systems. It describes semantic networks, frames, propositional logic, first-order predicate logic, and rule-based systems. For each technique, it provides facts about how knowledge is represented and examples to illustrate their use. The goal of knowledge representation is to encode knowledge in a way that allows inferencing and learning of new knowledge from the facts stored in the knowledge base.
Los rumiantes son herbívoros cuyo estómago está dividido en cuatro cámaras que les permiten fermentar la celulosa gracias a las bacterias presentes. La rumia es el proceso de regurgitación y remasticación del alimento que ayuda a su digestión. Las vacas digieren la celulosa gracias a las bacterias celulolíticas presentes en el rumen que producen enzimas llamadas celulasas capaces de hidrolizar la celulosa.
Knowledge Representation in Artificial intelligence Yasir Khan
This document discusses different methods of knowledge representation in artificial intelligence, including logical representations, semantic networks, production rules, and frames. Logical representations use formal logics like propositional logic and first-order predicate logic to represent facts and relationships. Semantic networks represent knowledge graphically as nodes and edges to model concepts and their relationships. Production rules represent knowledge as condition-action pairs to model problem-solving. Frames represent stereotyped situations as templates with slots to model attributes and behaviors. Choosing the right knowledge representation method is important for building successful AI systems.
The document discusses the knapsack problem, which involves selecting a subset of items that fit within a knapsack of limited capacity to maximize the total value. There are two versions - the 0-1 knapsack problem where items can only be selected entirely or not at all, and the fractional knapsack problem where items can be partially selected. Solutions include brute force, greedy algorithms, and dynamic programming. Dynamic programming builds up the optimal solution by considering all sub-problems.
The document discusses different types of knowledge that may need to be represented in AI systems, including objects, events, performance, and meta-knowledge. It also discusses representing knowledge at two levels: the knowledge level containing facts, and the symbol level containing representations of objects defined in terms of symbols. Common ways of representing knowledge mentioned include using English, logic, relations, semantic networks, frames, and rules. The document also discusses using knowledge for applications like learning, reasoning, and different approaches to machine learning such as skill refinement, knowledge acquisition, taking advice, problem solving, induction, discovery, and analogy.
Zara is a clothing retailer that uses modern technology in its marketing research and supply chain to quickly deliver fashionable designs at lower prices. It collects frequent customer feedback and uses IT to closely monitor trends. This allows Zara to make production decisions quickly and produce small quantities of many styles. As a result, Zara is able to deliver new fashion designs about twice a month while competitors take 3-5 months. This rapid turnover keeps customers engaged with frequent store visits and purchases.
This document discusses Zara's supply chain and how it contributes to the company's success. It provides details on Zara's vertically integrated supply chain model, which allows it to bring designs to stores in just 2-3 weeks compared to the industry average of 6-9 months. Key aspects of Zara's supply chain include local sourcing, fast production times, mass customization, and using IT to share information. This vertical integration model helps Zara increase revenue through more fashionable and scarce products, while decreasing costs through factors like lower transportation and inventory costs.
Language is acquired naturally, with meaning taking priority over structure, and reinforced through real-world experiences. As with first language acquisition, second language learners progress from single words to combining words based on meaning before identifying sentence elements, and can rearrange elements to form questions. Motivation and anxiety levels impact the language acquisition process, so teachers should provide instruction at a student's current proficiency level plus one additional level.
Artificial intelligence and knowledge representationLikan Patra
Artificial intelligence uses algorithms and knowledge representation to solve problems in a manner inspired by human intelligence. Knowledge representation involves using formal symbolic logic and structures like semantic networks and frames to represent knowledge. Different representation techniques exist for propositions, predicates, rules and nonmonotonic reasoning. Challenges for AI include acquiring knowledge autonomously, representing human experiences, and fully transferring human knowledge through communication.
Knowledge representation and Predicate logicAmey Kerkar
1. The document discusses knowledge representation and predicate logic.
2. It explains that knowledge representation involves representing facts through internal representations that can then be manipulated to derive new knowledge. Predicate logic allows representing objects and relationships between them using predicates, quantifiers, and logical connectives.
3. Several examples are provided to demonstrate representing simple facts about individuals as predicates and using quantifiers like "forall" and "there exists" to represent generalized statements.
1. K
E B
L
W
X
A H
P
Semantisch Netwerken (NL)
Settembrini Publishers
Dick H. Ahles
December 2012
Settembrini Publishers is the official global Partner for Unifiedroot S&M
to design Semantic Networks for the Top Level Domain Names TLD Publishing Services
2. . Ordenen van informatie 2
• er zijn vele methoden om informatie te ordenen, op te slaan
en terug te vinden
• en natuurlijke manier om informatie te ordenen en te
verrijken is door het verwijzen naar soortgelijke of andere
relevante informatie en begrippen
• dat is de reden dat het internet
voor informatie opvragen zo
populair is: de hyperlinks maken
het gemakkelijk om van de ene
webpagina met informatie door
te springen naar een volgende …
D.Ahles - Semantische Netwerken - 0812012
3. . Netwerken van informatie 3
• als men kiest de beschikbare informatie te ordenen en aan te
bieden met allerhande verwijzingen naar andere informatie
en we slaan deze informatie ook zo op dat spreken van een
data netwerk of in database termen van een netwerk-
database
• in de taalkunde of de wereld van de documentalisten noemt
men dergelijke netwerken met hun verwijzingen semantische
netwerken, thesauri of ontologiën en worden dan vooral
gebruikt voor indexering van documenten
K
E B
L
W
X
A H
P
D.Ahles - Semantische Netwerken - 0812012
4. . Wat is een Semantisch Netwerk? 4
• DEFENITIE
verzameling van (namen van) objecten die naar elkaar
verwijzen
Een semantisch netwerk heeft de volgende kenmerken:
• verwijzingen hebben specifieke betekenissen;
semantisch = betekenis
• relaties geven daarmee ook betekenis aan het object
• relaties met een specifieke betekenis hebben een richting: het
“verwijst” van object1 naar object2; de omgekeerde - object2 naar
object1 – heeft de relatie meestal dan een andere betekenis.
• om betekenis van een objecten verder te bepalen zijn objecten in
een semantisch netwerk vaak gecategoriseerd.
D.Ahles - Semantische Netwerken - 0812012
5. . Voorbeeld 5
• “Leidseplein” uit de categorie “straatnamen” heeft een
relatie “ligt in” met het object “Amsterdam” van de categorie
“plaatsnamen”. Deze categorie en relatie gegeven daarmee
betekenis aan het “object” Leidseplein.
• Maar “Leidseplein” kan ook een relatie “grenst aan” hebben
met de objecten “Leidsestraat”, of “Marnixstraat”.
• En het object “Stadsschouwburg Amsterdam”, heeft weer
een relatie “ligt aan” “Leidseplein”;
• evenzo heeft “GVB tramlijn 1” een relatie “heeft halte”met
datzelfde “Leidseplein”.
• En uiteraard heeft dezelfde relatie vanuit het object
“Amsterdam” maar nu met de betekenis van “omvat”
“Leidseplein”.
D.Ahles - Semantische Netwerken - 0812012
6. . Voorbeeld Semantisch netwerk rond “Leidseplein” 6
Stads-
Staat aan schouwburg Halte van
Amsterdam Lijn 1
Leidse
plein
Ligt in Grenst aan
Grenst aan Marnixstraat Leidsestraat
D.Ahles - Semantische Netwerken - 0812012
7. . Formeel 7
• als je semantische netwerken formeel wilt opzetten voldoen
ze aan de volgende kenmerken:
1. namen van de objecten zijn uniek en gecontroleerd; de lijst
van objecten wordt een ontologie genoemd
2. schrijfvarianten en andere synoniemen worden als zodanig
aangeduid en verwijzen naar de “hoofdterm”
3. objecten worden geclassificeerd; de classificatie is
hiërarchisch geordend. Classificatie heet een taxonomie
4. Ontologie (lijst van objecten) met benoemde relaties tussen
de namen van objecten wordt ook wel Thesaurus
genoemd
D.Ahles - Semantische Netwerken - 0812012
8. . Wat maakt een semantisch netwerk zo interessant 8
• belangrijkste reden: het sluit aan bij een natuurlijke manier
van ordenen en denken
• daarnaast het is een hele flexibele manier van ordenen: je
kunt semantische netwerken gemakkelijk uitbreiden naar
behoefte, én
• je kunt bijna alle andere datastructuren gemakkelijk
converteren naar een semantisch netwerk
K
E B
L W
X
A P H
D.Ahles - Semantische Netwerken - 0812012
9. . U kende nog geen semantische netwerken!? ?9
• misschien had u nog nooit gehoord van het begrip
semantische netwerken of van Ontologieën
K
• geen nood want u bent eigenlijk L X
E B
W
al gewend van nature te denken A
`
` P H
in semantische netwerken
• voor u is een ontologie meestal niet
meer of minder dan:
– lijst van producten die u verkoopt
– lijst van contacten of klanten in uw database
– en heeft u uw contacten, klanten en producten al geclassificeerd;
privé, zakelijk, klanten, leveranciers, productsoorten
D.Ahles - Semantische Netwerken - 0812012
10. . Wat heb je nodig voor semantische netwerken? 10
• voor ieder semantisch netwerk moet je de
classificatie, hoofdtermen en de relaties vooraf definiëren
• keuze van classificatie en relatie typen hangt af van wat je
met het semantische netwerken wilt bereiken en wie er
gebruik van gaat maken.
• tevens moet je bepalen hoe je de namen in het netwerk
gebruikt voor het indexeren van de documenten waarnaar je
wilt verwijzen
• verder heb je natuurlijk een systeem nodig waarmee je
semantische netwerken kunt bouwen en onderhouden
D.Ahles - Semantische Netwerken - 0812012
11. . Settembrini Publishers 11
• bij Settembrini Publishers is veel (praktische) ervaring aanwezig om
semantische netwerken voor u te ontwerpen en te onderhouden
• voor informatie d.ahles@settembrini.nl
• Dick Ahles is uw consultant
Settembrini Publishers is the global Partner for
Unifiedroot S&M to design Semantic Networks for the
Top Level Domain Names TLD Publishing Services
www.unifiedroot.com
D.Ahles - Semantische Netwerken - 0812012