Ontologies provide a shared understanding of a domain by formally defining concepts, properties, and relationships. An ontology introduces vocabulary relevant to a domain and specifies the meaning of terms. Ontologies are machine-readable and enable overcoming differences in terminology across complex, distributed applications. Examples include gene ontologies, pharmaceutical drug ontologies, and customer profile ontologies. Semantic technologies use ontologies to provide semantic search, integration, reasoning, and analysis capabilities.
ESWC SS 2012 - Tuesday Tutorial Elena Simperl: Creating and Using Ontologieseswcsummerschool
Here are the steps I would suggest for aligning the ontologies:
1. Representatives present their ontology and explain key concepts and relationships.
2. Editor records all concepts and relationships on a whiteboard in a concept map format without evaluation.
3. Representatives discuss each concept and relationship to reach agreement on meaning and resolve any conflicts or ambiguities.
4. Editor incorporates agreed upon concepts and relationships into a single ontology, resolving any structural issues.
5. Representatives review the aligned ontology and provide feedback.
6. Editor incorporates final changes to produce the aligned ontology for use by all groups.
The goal is to understand each perspective, identify areas of overlap and conflict, and work together iteratively
Franz Et Al. Using ASP to Simulate the Interplay of Taxonomic and Nomenclatur...taxonbytes
Answer Set Programming (ASP) is a declarative, stable model approach to logic programming with an under-realized potential for representing and reasoning over biological information. ASP is particularly suited to address reasoning challenges with complex starting conditions and rule sets. One such challenge is the interplay of taxonomic and nomenclatural change in biological taxonomy that often results when a taxonomy is revised based on a previously published perspective. Depending on the nature of the taxonomic changes to be undertaken, one or more Code-mandated principles will apply to regulate specific and concomitant name changes. In the case of the International Code of Zoological Nomenclature, two principles of significance include the Principles of Priority and Typification. Although the relationship between the number of taxonomic and nomenclatural adjustments under a given transition scenario is not linear, the application of the name-changing rules is usually unambiguous and therefore amenable to logic representation. Here we explore the modeling of the taxonomy/nomenclature interplay in ASP with a simple, abstract nine-taxon use case that contains four terminal species of which two are type-bearers for their respective genera. Four distinct one-taxon transfer scenarios are simulated through a transition system approach, requiring 1-7 concomitant nomenclatural changes depending (1) on the priority relationships among the terminal taxa being repositioned and (2) the type-bearing name dependencies of their higher-level parents. ASP can simulate these rules faithfully and thus reason over situations that range from a one-to-one match of taxonomic and nomenclatural changes to situations where they two kinds of change become increasingly disconnected (e.g., transfer of non-type genera among tribes without name change, or "transfer" [in reverse direction] of a single priority-carrying name/taxon into a larger yet junior entity with numerous required name changes). Our results, though very preliminary, illustrate how ASP logic approach may be utilized to perform optimizations at the taxonomy/nomenclature intersection, and generally represent a novel step towards translating Code-mandated naming rules into logic, with potential benefits for virtual taxonomic domains.
The document discusses stepwise methodologies for building ontologies. It outlines common steps such as identifying the purpose and scope, capturing concepts and relationships, coding the ontology formally, integrating existing ontologies, evaluation, and documentation. It emphasizes starting with a middle-out approach to capture definitions and discusses reaching consensus among those involved in building the ontology. Modularization of ontologies into reusable components is also presented as an important aspect of the methodology.
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information RetrievalMauro Dragoni
The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed
The document discusses ontologies, including:
1) It defines ontologies as formal specifications of concepts and relationships that can exist for an agent or community. Ontologies allow knowledge to be shared and reused.
2) Ontologies can be used to facilitate knowledge management, enable learning about a domain, and enable intelligent search and query expansion.
3) The document provides guidance on developing ontologies, including researching the domain, using existing resources, defining classes and properties, and choosing an ontology language.
SWSN UNIT-3.pptx we can information about swsn professionalgowthamnaidu0986
Ontology engineering involves constructing ontologies through various methods. It begins with defining the scope and evaluating existing ontologies for reuse. Terms are enumerated and organized in a taxonomy with defined properties, facets, and instances. The ontology is checked for anomalies and refined iteratively. Popular tools for ontology development include Protege and WebOnto. Methods like Meth ontology and On-To-Knowledge methodology provide processes for building ontologies from scratch or reusing existing ones. Ontology sharing requires mapping between ontologies to allow interoperability, and libraries exist for storing and accessing ontologies.
Ontologies provide a shared understanding of a domain by formally defining concepts, properties, and relationships. An ontology introduces vocabulary relevant to a domain and specifies the meaning of terms. Ontologies are machine-readable and enable overcoming differences in terminology across complex, distributed applications. Examples include gene ontologies, pharmaceutical drug ontologies, and customer profile ontologies. Semantic technologies use ontologies to provide semantic search, integration, reasoning, and analysis capabilities.
ESWC SS 2012 - Tuesday Tutorial Elena Simperl: Creating and Using Ontologieseswcsummerschool
Here are the steps I would suggest for aligning the ontologies:
1. Representatives present their ontology and explain key concepts and relationships.
2. Editor records all concepts and relationships on a whiteboard in a concept map format without evaluation.
3. Representatives discuss each concept and relationship to reach agreement on meaning and resolve any conflicts or ambiguities.
4. Editor incorporates agreed upon concepts and relationships into a single ontology, resolving any structural issues.
5. Representatives review the aligned ontology and provide feedback.
6. Editor incorporates final changes to produce the aligned ontology for use by all groups.
The goal is to understand each perspective, identify areas of overlap and conflict, and work together iteratively
Franz Et Al. Using ASP to Simulate the Interplay of Taxonomic and Nomenclatur...taxonbytes
Answer Set Programming (ASP) is a declarative, stable model approach to logic programming with an under-realized potential for representing and reasoning over biological information. ASP is particularly suited to address reasoning challenges with complex starting conditions and rule sets. One such challenge is the interplay of taxonomic and nomenclatural change in biological taxonomy that often results when a taxonomy is revised based on a previously published perspective. Depending on the nature of the taxonomic changes to be undertaken, one or more Code-mandated principles will apply to regulate specific and concomitant name changes. In the case of the International Code of Zoological Nomenclature, two principles of significance include the Principles of Priority and Typification. Although the relationship between the number of taxonomic and nomenclatural adjustments under a given transition scenario is not linear, the application of the name-changing rules is usually unambiguous and therefore amenable to logic representation. Here we explore the modeling of the taxonomy/nomenclature interplay in ASP with a simple, abstract nine-taxon use case that contains four terminal species of which two are type-bearers for their respective genera. Four distinct one-taxon transfer scenarios are simulated through a transition system approach, requiring 1-7 concomitant nomenclatural changes depending (1) on the priority relationships among the terminal taxa being repositioned and (2) the type-bearing name dependencies of their higher-level parents. ASP can simulate these rules faithfully and thus reason over situations that range from a one-to-one match of taxonomic and nomenclatural changes to situations where they two kinds of change become increasingly disconnected (e.g., transfer of non-type genera among tribes without name change, or "transfer" [in reverse direction] of a single priority-carrying name/taxon into a larger yet junior entity with numerous required name changes). Our results, though very preliminary, illustrate how ASP logic approach may be utilized to perform optimizations at the taxonomy/nomenclature intersection, and generally represent a novel step towards translating Code-mandated naming rules into logic, with potential benefits for virtual taxonomic domains.
The document discusses stepwise methodologies for building ontologies. It outlines common steps such as identifying the purpose and scope, capturing concepts and relationships, coding the ontology formally, integrating existing ontologies, evaluation, and documentation. It emphasizes starting with a middle-out approach to capture definitions and discusses reaching consensus among those involved in building the ontology. Modularization of ontologies into reusable components is also presented as an important aspect of the methodology.
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information RetrievalMauro Dragoni
The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed
The document discusses ontologies, including:
1) It defines ontologies as formal specifications of concepts and relationships that can exist for an agent or community. Ontologies allow knowledge to be shared and reused.
2) Ontologies can be used to facilitate knowledge management, enable learning about a domain, and enable intelligent search and query expansion.
3) The document provides guidance on developing ontologies, including researching the domain, using existing resources, defining classes and properties, and choosing an ontology language.
SWSN UNIT-3.pptx we can information about swsn professionalgowthamnaidu0986
Ontology engineering involves constructing ontologies through various methods. It begins with defining the scope and evaluating existing ontologies for reuse. Terms are enumerated and organized in a taxonomy with defined properties, facets, and instances. The ontology is checked for anomalies and refined iteratively. Popular tools for ontology development include Protege and WebOnto. Methods like Meth ontology and On-To-Knowledge methodology provide processes for building ontologies from scratch or reusing existing ones. Ontology sharing requires mapping between ontologies to allow interoperability, and libraries exist for storing and accessing ontologies.
1) The document presents a new ontology-based question answering method using query templates for the dining domain.
2) A dining ontology is developed to represent concepts like cuisine, facilities, meals, and their relationships.
3) Query templates are generated from the dining ontology and stored to enable faster retrieval of answers from the ontology compared to using SPARQL queries. This improves reusability.
A special session about using DC metadata to describe scholarly research papers held during the DC-2006 conference in Manzanillo, Mexico in October 2006.
Extending models for controlled vocabularies to classification systems: model...Marcia Zeng
Mitchell, Joan S., Marcia Lei Zeng, and Maja Zumer. Presented at the International UDC Seminar 2011, Classification & Ontology, The Hague, The Netherlands, Sept. 19-20, 2011.
The document describes Earthster Core Ontology (ECO), a domain ontology for Life Cycle Assessment (LCA). ECO aims to provide a vocabulary for core LCA concepts to publish LCA data on the web in a semantically interoperable way. It defines concepts like Process, Quantified Effect, and Elementary Flow. ECO is still under development with feedback from the LCA community. Its goals are to extend existing LCA data structures, link data sources, and allow for flexible extension over time as the field evolves.
Presentation on the Chemical Analysis Metadata Platform (ChAMP) as a new project to characterize and organize metadata about chemical analysis methods. The project will develop an ontology, controlled vocabularies, and design rules
Semantic Web in Action: Ontology-driven information search, integration and a...Amit Sheth
Amit Sheth's Keynote talk given at: “Semantic Web in Action: Ontology-driven information search, integration and analysis,” Net Object Days 2003 and MATES03, Erfurt, Germany, September 23, 2003. http://knoesis.org
Note: slides 51-55 have audio.
The document summarizes a presentation on developing an application profile for the metadata schema for ePrints institutional repositories. It discusses the background and rationale for developing a richer metadata profile than Dublin Core to allow for aggregation of metadata from repositories. It outlines the functional requirements identified, including supporting complex objects, versions, and additional search/browse fields. It then describes the entity-relationship model developed, which is based on the FRBR model to describe the relationships between scholarly works, expressions, formats, and copies.
study or concern about what kinds of things exist
what entities there are in the universe.
the ontology derives from the Greek onto (being) and logia (written or spoken). It is a branch of metaphysics , the study of first principles or the root of things.
Semantic Web: Technolgies and Applications for Real-WorldAmit Sheth
Amit Sheth and Susie Stephens, "Semantic Web: Technolgies and Applications for Real-World," Tutorial at 2007 World Wide Web Conference, Banff, Canada.
Tutorial discusses technologies and deployed real-world applications through 2007.
Tutorial description at: http://www2007.org/tutorial-T11.php
The paper trail:steps towards a reference model for the metadata ecologyR. John Robertson
The paper trail: steps towards a reference model for the metadata ecology, presentation at ~CoLIS5 workshop. Presentation with Jane Barton. http://mwi.cdlr.strath.ac.uk/Colisworkshop.htm
Archiving- from June 2005.
please note this presentation is currently all rights reserved until i contact the other author.
The document discusses three case studies related to making organizational taxonomies and resources more interoperable:
1) Integrating metadata across three Victorian government departments by aggregating, rationalizing, and harmonizing their schemas.
2) Repatriating cultural resources from the Quinkan people by using Dublin Core metadata with local extensions to provide a single access point for internal and external users.
3) The AccessForAll project, which uses metadata to match educational resources to individual learner needs and preferences to ensure equal accessibility. Standards are discussed as a way to balance local specificity and global interoperability.
Metadata for Terminology / KOS ResourcesMarcia Zeng
1. Why do we need metadata for terminology resources? 2. What do we need to know about a terminology resource? 3. Is there a standardized set of metadata elements for terminology resources?-- a presentation at the "New Dimensions in Knowledge Organization Systems", a Joint NKOS/ CENDI Workshop, World Bank, Washington, DC. September 11, 2008 http://nkos.slis.kent.edu/2008workshop/NKOS-CENDI2008.htm
The document discusses the CSDMS Standard Names, which are naming conventions developed by the Community Surface Dynamics Modeling System (CSDMS) modeling framework to facilitate automatic coupling of models and data sets from different contributors. The naming conventions follow an object-oriented approach where each standard variable name is composed of an object name and quantity name joined by double underscores. This allows framework software to retrieve numerical values for variables based on their standardized names. The naming conventions were designed according to criteria such as avoiding ambiguity, using widely understood terminology, and supporting mathematical operations and assumptions. They address challenges of automatic semantic mediation when coupling diverse resources that use different naming systems.
ONTOLOGY VISUALIZATION PROTÉGÉ TOOLS – A REVIEWijait
The document discusses ontology visualization tools in Protégé. It reviews four main visualization methods used in Protégé tools: indented list, node-link and tree, zoomable, and focus+context. It then examines specific Protégé tools that use each method, including their key features and limitations. The tools discussed are Protégé Class Browser (indented list), Protégé OntoViz and OntoSphere (node-link and tree), Jambalaya (zoomable), and Protégé TGVizTab (focus+context). The document aims to categorize the characteristics of existing Protégé visualization tools to assist in method selection and promote future research.
ONTOLOGY VISUALIZATION PROTÉGÉ TOOLS – A REVIEW ijait
The document discusses ontology visualization tools in Protégé. It reviews four main visualization methods used in Protégé tools: indented list, node-link and tree, zoomable, and focus+context. It then examines specific Protégé tools that use each method, including their key features and limitations. The tools assessed are Protégé Class Browser (indented list), Protégé OntoViz and OntoSphere (node-link and tree), Jambalaya (zoomable), and Protégé TGVizTab (focus+context). The document concludes by summarizing and comparing the visualization characteristics of these Protégé tools.
The document provides an overview of ontology and its various aspects. It discusses the origin of the term ontology, which derives from Greek words meaning "being" and "science," so ontology is the study of being. It distinguishes between scientific and philosophical ontologies. Social ontology examines social entities. Perspectives on ontology include philosophy, library and information science, artificial intelligence, linguistics, and the semantic web. The goal of ontology is to encode knowledge to make it understandable to both people and machines. It provides motivations for developing ontologies such as enabling information integration and knowledge management. The document also discusses ontology languages, uniqueness of ontologies, purposes of ontologies, and provides references.
OODBMS Concepts - National University of Singapore.pdfssuserd5e338
This document discusses object-oriented database management systems (OODBMS). It covers basic OO concepts like objects, classes, attributes, methods, encapsulation, inheritance and polymorphism. It describes the two approaches to OODBMS - object-oriented databases and object-relational databases. It discusses some issues with the OO data model like inheritance conflicts. It also provides examples of queries in an object-relational query language and compares the OO data model to other data models.
The document introduces ontology and describes what it is from both philosophical and computer science perspectives. An ontology in computers consists of a vocabulary to describe a domain, specifications of the meaning of terms, and constraints capturing additional knowledge about the domain. It then provides an example ontology and discusses applications of ontologies such as for the semantic web. It also discusses important considerations for building ontologies such as collaboration, versioning, and ease of use.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
1) The document presents a new ontology-based question answering method using query templates for the dining domain.
2) A dining ontology is developed to represent concepts like cuisine, facilities, meals, and their relationships.
3) Query templates are generated from the dining ontology and stored to enable faster retrieval of answers from the ontology compared to using SPARQL queries. This improves reusability.
A special session about using DC metadata to describe scholarly research papers held during the DC-2006 conference in Manzanillo, Mexico in October 2006.
Extending models for controlled vocabularies to classification systems: model...Marcia Zeng
Mitchell, Joan S., Marcia Lei Zeng, and Maja Zumer. Presented at the International UDC Seminar 2011, Classification & Ontology, The Hague, The Netherlands, Sept. 19-20, 2011.
The document describes Earthster Core Ontology (ECO), a domain ontology for Life Cycle Assessment (LCA). ECO aims to provide a vocabulary for core LCA concepts to publish LCA data on the web in a semantically interoperable way. It defines concepts like Process, Quantified Effect, and Elementary Flow. ECO is still under development with feedback from the LCA community. Its goals are to extend existing LCA data structures, link data sources, and allow for flexible extension over time as the field evolves.
Presentation on the Chemical Analysis Metadata Platform (ChAMP) as a new project to characterize and organize metadata about chemical analysis methods. The project will develop an ontology, controlled vocabularies, and design rules
Semantic Web in Action: Ontology-driven information search, integration and a...Amit Sheth
Amit Sheth's Keynote talk given at: “Semantic Web in Action: Ontology-driven information search, integration and analysis,” Net Object Days 2003 and MATES03, Erfurt, Germany, September 23, 2003. http://knoesis.org
Note: slides 51-55 have audio.
The document summarizes a presentation on developing an application profile for the metadata schema for ePrints institutional repositories. It discusses the background and rationale for developing a richer metadata profile than Dublin Core to allow for aggregation of metadata from repositories. It outlines the functional requirements identified, including supporting complex objects, versions, and additional search/browse fields. It then describes the entity-relationship model developed, which is based on the FRBR model to describe the relationships between scholarly works, expressions, formats, and copies.
study or concern about what kinds of things exist
what entities there are in the universe.
the ontology derives from the Greek onto (being) and logia (written or spoken). It is a branch of metaphysics , the study of first principles or the root of things.
Semantic Web: Technolgies and Applications for Real-WorldAmit Sheth
Amit Sheth and Susie Stephens, "Semantic Web: Technolgies and Applications for Real-World," Tutorial at 2007 World Wide Web Conference, Banff, Canada.
Tutorial discusses technologies and deployed real-world applications through 2007.
Tutorial description at: http://www2007.org/tutorial-T11.php
The paper trail:steps towards a reference model for the metadata ecologyR. John Robertson
The paper trail: steps towards a reference model for the metadata ecology, presentation at ~CoLIS5 workshop. Presentation with Jane Barton. http://mwi.cdlr.strath.ac.uk/Colisworkshop.htm
Archiving- from June 2005.
please note this presentation is currently all rights reserved until i contact the other author.
The document discusses three case studies related to making organizational taxonomies and resources more interoperable:
1) Integrating metadata across three Victorian government departments by aggregating, rationalizing, and harmonizing their schemas.
2) Repatriating cultural resources from the Quinkan people by using Dublin Core metadata with local extensions to provide a single access point for internal and external users.
3) The AccessForAll project, which uses metadata to match educational resources to individual learner needs and preferences to ensure equal accessibility. Standards are discussed as a way to balance local specificity and global interoperability.
Metadata for Terminology / KOS ResourcesMarcia Zeng
1. Why do we need metadata for terminology resources? 2. What do we need to know about a terminology resource? 3. Is there a standardized set of metadata elements for terminology resources?-- a presentation at the "New Dimensions in Knowledge Organization Systems", a Joint NKOS/ CENDI Workshop, World Bank, Washington, DC. September 11, 2008 http://nkos.slis.kent.edu/2008workshop/NKOS-CENDI2008.htm
The document discusses the CSDMS Standard Names, which are naming conventions developed by the Community Surface Dynamics Modeling System (CSDMS) modeling framework to facilitate automatic coupling of models and data sets from different contributors. The naming conventions follow an object-oriented approach where each standard variable name is composed of an object name and quantity name joined by double underscores. This allows framework software to retrieve numerical values for variables based on their standardized names. The naming conventions were designed according to criteria such as avoiding ambiguity, using widely understood terminology, and supporting mathematical operations and assumptions. They address challenges of automatic semantic mediation when coupling diverse resources that use different naming systems.
ONTOLOGY VISUALIZATION PROTÉGÉ TOOLS – A REVIEWijait
The document discusses ontology visualization tools in Protégé. It reviews four main visualization methods used in Protégé tools: indented list, node-link and tree, zoomable, and focus+context. It then examines specific Protégé tools that use each method, including their key features and limitations. The tools discussed are Protégé Class Browser (indented list), Protégé OntoViz and OntoSphere (node-link and tree), Jambalaya (zoomable), and Protégé TGVizTab (focus+context). The document aims to categorize the characteristics of existing Protégé visualization tools to assist in method selection and promote future research.
ONTOLOGY VISUALIZATION PROTÉGÉ TOOLS – A REVIEW ijait
The document discusses ontology visualization tools in Protégé. It reviews four main visualization methods used in Protégé tools: indented list, node-link and tree, zoomable, and focus+context. It then examines specific Protégé tools that use each method, including their key features and limitations. The tools assessed are Protégé Class Browser (indented list), Protégé OntoViz and OntoSphere (node-link and tree), Jambalaya (zoomable), and Protégé TGVizTab (focus+context). The document concludes by summarizing and comparing the visualization characteristics of these Protégé tools.
The document provides an overview of ontology and its various aspects. It discusses the origin of the term ontology, which derives from Greek words meaning "being" and "science," so ontology is the study of being. It distinguishes between scientific and philosophical ontologies. Social ontology examines social entities. Perspectives on ontology include philosophy, library and information science, artificial intelligence, linguistics, and the semantic web. The goal of ontology is to encode knowledge to make it understandable to both people and machines. It provides motivations for developing ontologies such as enabling information integration and knowledge management. The document also discusses ontology languages, uniqueness of ontologies, purposes of ontologies, and provides references.
OODBMS Concepts - National University of Singapore.pdfssuserd5e338
This document discusses object-oriented database management systems (OODBMS). It covers basic OO concepts like objects, classes, attributes, methods, encapsulation, inheritance and polymorphism. It describes the two approaches to OODBMS - object-oriented databases and object-relational databases. It discusses some issues with the OO data model like inheritance conflicts. It also provides examples of queries in an object-relational query language and compares the OO data model to other data models.
The document introduces ontology and describes what it is from both philosophical and computer science perspectives. An ontology in computers consists of a vocabulary to describe a domain, specifications of the meaning of terms, and constraints capturing additional knowledge about the domain. It then provides an example ontology and discusses applications of ontologies such as for the semantic web. It also discusses important considerations for building ontologies such as collaboration, versioning, and ease of use.
Similaire à Importance of Ontology in a Data Warehouse.pptx (20)
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
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!
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
2. Ontology
on·tol·o·gy
/änˈtäləjē/ noun
1. The branch of metaphysics dealing with the nature of being.
2. A set of concepts and categories in a subject area or domain that shows their properties and
the relations between them.
3. Ontology
A set of naming patterns, structures
& rules for each application.
4. Existing SDS Ontology
1. Application users have an
ontology
2. Application developers have
an ontology
3. On occasion they even overlap
5. Existing SDS Ontology
1. Application users have an
ontology
2. Application developers have
an ontology
3. On occasion they even overlap
6. Existing SDS Ontology
1. Application users have an
ontology
2. Application developers have
an ontology
3. On occasion they even overlap
Give examples: words that have specific meanings to our families, work colleagues, people having a common hobby. I belong to several organizations promote an understanding of the huge influence film noir has had - and still has - on movies. You always suspected I was a couple bubbles off of plumb. Now you know it.