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How Enterprise Architecture & Knowledge Graph Technologies Can Scale Business...
How Enterprise Architecture & Knowledge Graph Technologies Can Scale Business...Semantic Web Company
Organising data, for most of us, means Excel spreadsheets and folders upon folders. Knowledge graph technology, however, organises data in ways similar to the brain – through context and relations. By connecting your data, you (and also machines) are able to gain context within your knowledge, helping you to make informed decisions based on all of the information you already have.
So, how can enterprises benefit from this and scale?
PwC Sr. Research Fellow for Emerging Tech, Alan Morrison, and Sebastian Gabler, Head of Sales of Semantic Web Company tackle the importance of Enterprise Knowledge Graphs and how these technologies scale business efficiency.
Learn about:
• Application-centric development to data-centric approaches
• How enterprise architects learn how to benefit from knowledge graphs: use cases
• Learn which use cases fit well to which type of graph, and which technologies are involved
• Understand how RDF helps with data integration.
• What is AI-assisted entity linking?
• Understand data virtualisation vs. materialisationIntroduction to Knowledge Graphs and Semantic AI
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
Deep Text Analytics - How to extract hidden information and aboutness from text
- Deep Text Analytics (DTA) is an application of Semantic AI
- DTA fuses methods and algorithms taken from language modeling, corpus linguistics, machine learning, knowledge representation and the semantic web result into Deep Text Analytics methods
- Main areas of use cases for DTA are Information retrieval, NLU, Question answering, and Recommender Systems
Leveraging Knowledge Graphs in your Enterprise Knowledge Management System
Knowledge graphs and graph-based data in general are becoming increasingly important for addressing various data management challenges in industries such as financial services, life sciences, healthcare or energy.
At the core of this challenge is the comprehensive management of graph-based data, ranging from taxonomy to ontology management to the administration of comprehensive data graphs along with a defined governance framework. Various data sources are integrated and linked (semi) automatically using NLP and machine learning algorithms. Tools for securing high data quality and consistency are an integral part of such a platform.
PoolParty 7.0 can now handle a full range of enterprise data management tasks. Based on agile data integration, machine learning and text mining, or ontology-based data analysis, applications are developed that allow knowledge workers, marketers, analysts or researchers a comprehensive and in-depth view of previously unlinked data assets.
At the heart of the new release is the PoolParty GraphEditor, which complements the Taxonomy, Thesaurus, and Ontology Manager components that have been around for some time. All in all, data engineers and subject matter experts can now administrate and analyze enterprise-wide and heterogeneous data stocks with comfortable means, or link them with the help of artificial intelligence.
Recommended
How Enterprise Architecture & Knowledge Graph Technologies Can Scale Business...
How Enterprise Architecture & Knowledge Graph Technologies Can Scale Business...Semantic Web Company
Organising data, for most of us, means Excel spreadsheets and folders upon folders. Knowledge graph technology, however, organises data in ways similar to the brain – through context and relations. By connecting your data, you (and also machines) are able to gain context within your knowledge, helping you to make informed decisions based on all of the information you already have.
So, how can enterprises benefit from this and scale?
PwC Sr. Research Fellow for Emerging Tech, Alan Morrison, and Sebastian Gabler, Head of Sales of Semantic Web Company tackle the importance of Enterprise Knowledge Graphs and how these technologies scale business efficiency.
Learn about:
• Application-centric development to data-centric approaches
• How enterprise architects learn how to benefit from knowledge graphs: use cases
• Learn which use cases fit well to which type of graph, and which technologies are involved
• Understand how RDF helps with data integration.
• What is AI-assisted entity linking?
• Understand data virtualisation vs. materialisationIntroduction to Knowledge Graphs and Semantic AI
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
Deep Text Analytics - How to extract hidden information and aboutness from text
- Deep Text Analytics (DTA) is an application of Semantic AI
- DTA fuses methods and algorithms taken from language modeling, corpus linguistics, machine learning, knowledge representation and the semantic web result into Deep Text Analytics methods
- Main areas of use cases for DTA are Information retrieval, NLU, Question answering, and Recommender Systems
Leveraging Knowledge Graphs in your Enterprise Knowledge Management System
Knowledge graphs and graph-based data in general are becoming increasingly important for addressing various data management challenges in industries such as financial services, life sciences, healthcare or energy.
At the core of this challenge is the comprehensive management of graph-based data, ranging from taxonomy to ontology management to the administration of comprehensive data graphs along with a defined governance framework. Various data sources are integrated and linked (semi) automatically using NLP and machine learning algorithms. Tools for securing high data quality and consistency are an integral part of such a platform.
PoolParty 7.0 can now handle a full range of enterprise data management tasks. Based on agile data integration, machine learning and text mining, or ontology-based data analysis, applications are developed that allow knowledge workers, marketers, analysts or researchers a comprehensive and in-depth view of previously unlinked data assets.
At the heart of the new release is the PoolParty GraphEditor, which complements the Taxonomy, Thesaurus, and Ontology Manager components that have been around for some time. All in all, data engineers and subject matter experts can now administrate and analyze enterprise-wide and heterogeneous data stocks with comfortable means, or link them with the help of artificial intelligence.
Linking SharePoint Documents with Structured Data
Unified views of business-critical information across all customer-facing processes and HR-related tasks are most relevant for decision makers.
In this talk we present a SharePoint extension that supports the automatic linking of unstructured content like Word documents with structured information from other databases, such as statistical data. As a result, decision makers have knowledge portals based on linked data at their fingertips.
While the importance of managed metadata and Term Store is clear to most SharePoint architects, the significance of a semantic layer outside of the content silos has not yet been explored systematically.
We will present a four-layered content architecture and will take a close look on some of the aspects of the semantic layer and its integration with SharePoint:
- Keeping Term Store and the semantic layer in sync
- Automatic tagging of SharePoint content
- Use of graph databases to store tags
- Entity-centric search & analytics applications
Metadata is most often stored per data source, and therefore it is meaningless outside of the silo. In this presentation, we will give a live demo of a SharePoint extension that makes use of an explicit semantic layer based on standards. This approach builds the basis to start linking data across the silos in a most agile way.
The resulting knowledge graph can start on a small scale, to develop continuously and to grow with the requirements. In this presentation we will give an example to illustrate how initially disconnected HR-related data (CVs in SharePoint; statistical data from labour market; skills and competencies taxonomies; salary spreadsheets) gets linked automatically, and is then made available through an extensive search & analytics application.
The Fast Track to Knowledge Engineering
Slides based on a workshop held at SEMANTiCS 2018 in Vienna. Introduces a methodology for knowledge graph management based on Semantic Web standards, ranging from taxonomies over ontologies, mappings, graph and entity linking. Further topics covered: Semantic AI and machine learning, text mining, and semantic search.
Semantic AI
Semantic Artificial Intelligence is the fusion of various types of AI, incl. symbolic AI, reasoning, and machine learning techniques like deep learning. At the same time, Semantic AI has a strong focus on data management and data governance. With the 'wedding' of various AI techniques new promises are made, but also fundamental approaches like 'Explainable AI (XAI)', knowledge graphs, or Linked Data are more strongly focused.
BrightTALK - Semantic AI
Bringing Machine Learning and Knowledge Graphs Together
Six Core Aspects of Semantic AI:
- Hybrid Approach
- Data Quality
- Data as a Service
- Structured Data Meets Text
- No Black-box
- Towards Self-optimizing Machines
PoolParty Semantic Classifier
The PoolParty Semantic Classifier is a component of the Semantic Suite, which makes use of machine learning in combination with Knowledge Graphs.
We discuss the potential of the fusion of machine learning, neuronal networks, and knowledge graphs based on use cases and this concrete technology offering.
We introduce the term 'Semantic AI' that refers to the combined usage of various AI methods.
Leveraging Taxonomy Management with Machine Learning
Machines learn better with Semantics!
See how taxonomy management and the maintenance of knowledge graphs benefit from machine learning and corpus analysis, and how, in return, machine learning gets improved when using semantic knowledge models for further enrichment.
Taxonomies put in the right place
A quick introduction to taxonomies, and how they relate to ontologies and knowledge graph. See how they can serve as part of a semantic layer in your information architecture. Learn which use cases can be developed based on this.
PoolParty GraphSearch - The Fusion of Search, Recommendation and Analytics
See how Cognitive Search works when based on Semantic Knowledge Graphs.
We showcase the latest developments and new features of PoolParty GraphSearch:
- Navigate a semantic knowledge graph
- Ontology-based data access (OBDA)
- Search over various search spaces: Ontology-driven facets including hierarchies
- Sophisticated autocomplete including context information
- Custom views on entity-centric and document-centric search results
- Linked data: put various tagging services such as TRIT or PoolParty Extractor in series and benefit from comprehensive semantic enrichment
- Statistical charts to explain results from unified data repositories quickly
- Plug-in system for various recommendation and matchmaking algorithms
Semantics as the Basis of Advanced Cognitive Computing
This talk discusses how companies can apply semantic technologies to build cognitive applications. It examines the role of semantic technologies within the larger Artificial Intelligence (AI) technology ecosystem, with the aim of raising awareness of different solution approaches.
To succeed in a digital and increasingly self-service-oriented business environment, companies can no longer rely solely on IT professionals. Solutions like the PoolParty Semantic Suite utilize domain experts and business users to shape the cognitive intelligence of knowledge-driven applications.
Cognitive solutions essentially mimic how the human brain works. The search for cognitive solutions has challenged computer scientists for more than six decades. The research has matured to the extent that it has moved out of the laboratory and is now being applied in a range of knowledge-intensive industries.
There is no such thing as a single, all-encompassing “AI technology.” Rather, the large global professional technology community and software vendors are continuously developing a broad set of methods and tools for natural language processing and advanced data analytics. They are creating a growing library of machine learning algorithms to enhance the automated learning capabilities of computer systems. These emerging technologies need to be customized or combined with complementary solutions as semantic knowledge graphs, depending on the use case.
A hybrid approach to cognitive computing, employing both the statistical and knowledge base models, will have a critical influence on the development of applications. Highly automated data processing based on sophisticated machine-learning algorithms must give end user the option to independently modify the functioning of smart applications in order to overcome the disadvantages associated with ‘black-box’ approaches.
This talk will give an overview over state-of-the-art smart applications, which are becoming a fusion of search, recommendation, and question-answer machines. We will cover specific use cases in focused knowledge domains, and we will discuss how this approach allows for AI-enabled use cases and application scenarios that are currently highly prioritized by corporate and digital business players.
Structured Content Meets Taxonomy
In this engaging, 1-hour webinar (hosted by http://www.poolparty.biz and http://www.mekon.com), you will learn how to tailor information chunks to readers’ unique needs. We will talk about:
- Benefits and principles of granular structured content, and how to start preparing your own content for this new architecture.
- Best practices for linking structured content to standards-based taxonomies, and some pitfalls to avoid
- The underlying semantic architecture that you can work toward for a truly mature and scalable approach to linking content and data
- Key use cases that you can apply to your own organization
PoolParty 6.0 - Climbing the Semantic Ladder
See how you can configure your linked data eco-system based on PoolParty's semantic middleware configurator. Benefit from Shadow Concept Extraction by making implicit knowledge visible. Combine knowledge graphs with machine learning and integrate semantics into your enterprise information systems.
PoolParty Semantic Suite - Release 6.0 (Technical Overview)
Technical Deep Dive: Learn more about the most complete Semantic Middleware on the market. See how to integrate semantic services into your Enterprise Information Systems.
Taxonomies and Ontologies – The Yin and Yang of Knowledge Modelling
See how ontologies and taxonomies can play together to reach the ultimate goal, which is the cost-efficient creation and maintenance of an enterprise knowledge graph. The knowledge modelling methodology is supported by approaches taken from NLP, data science, and machine learning.
PROPEL . Austrian's Roadmap for Enterprise Linked Data
Slidedeck for the final presentation of the Roadmap and Report
Taxonomy Quality Assessment
This talk addresses two questions: “How can the quality of taxonomies be defined?” and “How can it be measured?” See how quality criteria vary depending on how a taxonomy is applied, such as automatic content classification in ecommerce or a knowledge graph for data integration in enterprises. Distinguish between formal quality, structural properties, content coverage, and network topology. Investigate the advantages of standards-based and machine-processable SKOS taxonomies to be able to measure the quality of taxonomies automatically, as well as several tools and techniques for quality assessment.
Taxonomy-Driven UX
Consistency is crucial to a good user experience. Designers go to great lengths to create and test consistent visual designs. The structural design of an information environment, which is of equal importance to a good user experience, is too often ignored. Blumauer presents a “four-layered content architecture” for making sense of any information environment by clearly distinguishing between the content, metadata, and semantic layers and the navigation logic. He discusses several use cases for a taxonomy-driven user experience such as personalization or dynamically created topic pages.
PoolParty Semantic Suite - Release 5.5
PoolParty Semantic Suite 5.5 has been released in August 2016. Further integrations like with Elasticsearch or Stardog strengthen PoolParty’s position as the leading semantic middleware at the cognitive computing market. Knowledge engineers and users benefit from an even more sophisticated combination of semantic computing and machine learning. The new features support context aware knowledge modelling and include an extended data quality management module.
PowerTagging for Sharepoint and Office 365
Knowledge extraction: Extract terms, phrases and named entities from SharePoint and O365 documents with high accuracy
Auto classification: Streamline your workflows with PoolParty’s reliable auto classifier
Consistent tagging: Semi-automatic tagging based on your taxonomies provides consistent metadata
Enterprise-wide tagging: Benefit from linked data and connect your SharePoint to other repositories
Concept based search: autocomplete from taxonomy
Automatic use of synonyms: get more precise results
Configurable search refiners: faceted search based on taxonomy hierarchy
Include fact box for search term in search results: benefit from additional context information
From SKOS over SKOS-XL to Custom Ontologies
This slidedeck is about PoolParty Semantic Suite (http://www.poolparty.biz/), especially about features included by releases 5.2 and 5.3.
See how taxonomy management based on SKOS can be extended with SKOS-XL, all based on W3C's Semantic Web standards. See how SKOS-XL can be combined with ontologies like FIBO.
PoolParty's built in reference corpus analysis based on powerful text mining helps to continuously extend taxonomies. Its built-in co-occurence analysis supports taxonomists with the identification of candidate concepts.
PoolParty Semantic Integrator can be used for deep data analytics tasks and semantic search. See how this can be integrated with various graph databases and search engines.
PoolParty Semantic Suite: Solutions for Sustainable Development: The Climate ...
PoolParty Semantic Suite: Solutions for Sustainable Development: The Climate ...Semantic Web Company
Presentation of the webinar: PoolParty for Sustainable Development - the Climate Tagger - taking place on 5 November 2015. Introduction Slides by Florian Bauer of REEEP.
More information and other presentations to be found here: http://bit.ly/1NpTcGT.
Recording of the webinar: https://www.youtube.com/watch?v=3GxtFfLL1ps.Essentials of Automations: Optimizing FME Workflows with Parameters
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Knowledge engineering: from people to machines and back
Keynote at the 21st European Semantic Web Conference
More Related Content
More from Semantic Web Company
Linking SharePoint Documents with Structured Data
Unified views of business-critical information across all customer-facing processes and HR-related tasks are most relevant for decision makers.
In this talk we present a SharePoint extension that supports the automatic linking of unstructured content like Word documents with structured information from other databases, such as statistical data. As a result, decision makers have knowledge portals based on linked data at their fingertips.
While the importance of managed metadata and Term Store is clear to most SharePoint architects, the significance of a semantic layer outside of the content silos has not yet been explored systematically.
We will present a four-layered content architecture and will take a close look on some of the aspects of the semantic layer and its integration with SharePoint:
- Keeping Term Store and the semantic layer in sync
- Automatic tagging of SharePoint content
- Use of graph databases to store tags
- Entity-centric search & analytics applications
Metadata is most often stored per data source, and therefore it is meaningless outside of the silo. In this presentation, we will give a live demo of a SharePoint extension that makes use of an explicit semantic layer based on standards. This approach builds the basis to start linking data across the silos in a most agile way.
The resulting knowledge graph can start on a small scale, to develop continuously and to grow with the requirements. In this presentation we will give an example to illustrate how initially disconnected HR-related data (CVs in SharePoint; statistical data from labour market; skills and competencies taxonomies; salary spreadsheets) gets linked automatically, and is then made available through an extensive search & analytics application.
The Fast Track to Knowledge Engineering
Slides based on a workshop held at SEMANTiCS 2018 in Vienna. Introduces a methodology for knowledge graph management based on Semantic Web standards, ranging from taxonomies over ontologies, mappings, graph and entity linking. Further topics covered: Semantic AI and machine learning, text mining, and semantic search.
Semantic AI
Semantic Artificial Intelligence is the fusion of various types of AI, incl. symbolic AI, reasoning, and machine learning techniques like deep learning. At the same time, Semantic AI has a strong focus on data management and data governance. With the 'wedding' of various AI techniques new promises are made, but also fundamental approaches like 'Explainable AI (XAI)', knowledge graphs, or Linked Data are more strongly focused.
BrightTALK - Semantic AI
Bringing Machine Learning and Knowledge Graphs Together
Six Core Aspects of Semantic AI:
- Hybrid Approach
- Data Quality
- Data as a Service
- Structured Data Meets Text
- No Black-box
- Towards Self-optimizing Machines
PoolParty Semantic Classifier
The PoolParty Semantic Classifier is a component of the Semantic Suite, which makes use of machine learning in combination with Knowledge Graphs.
We discuss the potential of the fusion of machine learning, neuronal networks, and knowledge graphs based on use cases and this concrete technology offering.
We introduce the term 'Semantic AI' that refers to the combined usage of various AI methods.
Leveraging Taxonomy Management with Machine Learning
Machines learn better with Semantics!
See how taxonomy management and the maintenance of knowledge graphs benefit from machine learning and corpus analysis, and how, in return, machine learning gets improved when using semantic knowledge models for further enrichment.
Taxonomies put in the right place
A quick introduction to taxonomies, and how they relate to ontologies and knowledge graph. See how they can serve as part of a semantic layer in your information architecture. Learn which use cases can be developed based on this.
PoolParty GraphSearch - The Fusion of Search, Recommendation and Analytics
See how Cognitive Search works when based on Semantic Knowledge Graphs.
We showcase the latest developments and new features of PoolParty GraphSearch:
- Navigate a semantic knowledge graph
- Ontology-based data access (OBDA)
- Search over various search spaces: Ontology-driven facets including hierarchies
- Sophisticated autocomplete including context information
- Custom views on entity-centric and document-centric search results
- Linked data: put various tagging services such as TRIT or PoolParty Extractor in series and benefit from comprehensive semantic enrichment
- Statistical charts to explain results from unified data repositories quickly
- Plug-in system for various recommendation and matchmaking algorithms
Semantics as the Basis of Advanced Cognitive Computing
This talk discusses how companies can apply semantic technologies to build cognitive applications. It examines the role of semantic technologies within the larger Artificial Intelligence (AI) technology ecosystem, with the aim of raising awareness of different solution approaches.
To succeed in a digital and increasingly self-service-oriented business environment, companies can no longer rely solely on IT professionals. Solutions like the PoolParty Semantic Suite utilize domain experts and business users to shape the cognitive intelligence of knowledge-driven applications.
Cognitive solutions essentially mimic how the human brain works. The search for cognitive solutions has challenged computer scientists for more than six decades. The research has matured to the extent that it has moved out of the laboratory and is now being applied in a range of knowledge-intensive industries.
There is no such thing as a single, all-encompassing “AI technology.” Rather, the large global professional technology community and software vendors are continuously developing a broad set of methods and tools for natural language processing and advanced data analytics. They are creating a growing library of machine learning algorithms to enhance the automated learning capabilities of computer systems. These emerging technologies need to be customized or combined with complementary solutions as semantic knowledge graphs, depending on the use case.
A hybrid approach to cognitive computing, employing both the statistical and knowledge base models, will have a critical influence on the development of applications. Highly automated data processing based on sophisticated machine-learning algorithms must give end user the option to independently modify the functioning of smart applications in order to overcome the disadvantages associated with ‘black-box’ approaches.
This talk will give an overview over state-of-the-art smart applications, which are becoming a fusion of search, recommendation, and question-answer machines. We will cover specific use cases in focused knowledge domains, and we will discuss how this approach allows for AI-enabled use cases and application scenarios that are currently highly prioritized by corporate and digital business players.
Structured Content Meets Taxonomy
In this engaging, 1-hour webinar (hosted by http://www.poolparty.biz and http://www.mekon.com), you will learn how to tailor information chunks to readers’ unique needs. We will talk about:
- Benefits and principles of granular structured content, and how to start preparing your own content for this new architecture.
- Best practices for linking structured content to standards-based taxonomies, and some pitfalls to avoid
- The underlying semantic architecture that you can work toward for a truly mature and scalable approach to linking content and data
- Key use cases that you can apply to your own organization
PoolParty 6.0 - Climbing the Semantic Ladder
See how you can configure your linked data eco-system based on PoolParty's semantic middleware configurator. Benefit from Shadow Concept Extraction by making implicit knowledge visible. Combine knowledge graphs with machine learning and integrate semantics into your enterprise information systems.
PoolParty Semantic Suite - Release 6.0 (Technical Overview)
Technical Deep Dive: Learn more about the most complete Semantic Middleware on the market. See how to integrate semantic services into your Enterprise Information Systems.
Taxonomies and Ontologies – The Yin and Yang of Knowledge Modelling
See how ontologies and taxonomies can play together to reach the ultimate goal, which is the cost-efficient creation and maintenance of an enterprise knowledge graph. The knowledge modelling methodology is supported by approaches taken from NLP, data science, and machine learning.
PROPEL . Austrian's Roadmap for Enterprise Linked Data
Slidedeck for the final presentation of the Roadmap and Report
Taxonomy Quality Assessment
This talk addresses two questions: “How can the quality of taxonomies be defined?” and “How can it be measured?” See how quality criteria vary depending on how a taxonomy is applied, such as automatic content classification in ecommerce or a knowledge graph for data integration in enterprises. Distinguish between formal quality, structural properties, content coverage, and network topology. Investigate the advantages of standards-based and machine-processable SKOS taxonomies to be able to measure the quality of taxonomies automatically, as well as several tools and techniques for quality assessment.
Taxonomy-Driven UX
Consistency is crucial to a good user experience. Designers go to great lengths to create and test consistent visual designs. The structural design of an information environment, which is of equal importance to a good user experience, is too often ignored. Blumauer presents a “four-layered content architecture” for making sense of any information environment by clearly distinguishing between the content, metadata, and semantic layers and the navigation logic. He discusses several use cases for a taxonomy-driven user experience such as personalization or dynamically created topic pages.
PoolParty Semantic Suite - Release 5.5
PoolParty Semantic Suite 5.5 has been released in August 2016. Further integrations like with Elasticsearch or Stardog strengthen PoolParty’s position as the leading semantic middleware at the cognitive computing market. Knowledge engineers and users benefit from an even more sophisticated combination of semantic computing and machine learning. The new features support context aware knowledge modelling and include an extended data quality management module.
PowerTagging for Sharepoint and Office 365
Knowledge extraction: Extract terms, phrases and named entities from SharePoint and O365 documents with high accuracy
Auto classification: Streamline your workflows with PoolParty’s reliable auto classifier
Consistent tagging: Semi-automatic tagging based on your taxonomies provides consistent metadata
Enterprise-wide tagging: Benefit from linked data and connect your SharePoint to other repositories
Concept based search: autocomplete from taxonomy
Automatic use of synonyms: get more precise results
Configurable search refiners: faceted search based on taxonomy hierarchy
Include fact box for search term in search results: benefit from additional context information
From SKOS over SKOS-XL to Custom Ontologies
This slidedeck is about PoolParty Semantic Suite (http://www.poolparty.biz/), especially about features included by releases 5.2 and 5.3.
See how taxonomy management based on SKOS can be extended with SKOS-XL, all based on W3C's Semantic Web standards. See how SKOS-XL can be combined with ontologies like FIBO.
PoolParty's built in reference corpus analysis based on powerful text mining helps to continuously extend taxonomies. Its built-in co-occurence analysis supports taxonomists with the identification of candidate concepts.
PoolParty Semantic Integrator can be used for deep data analytics tasks and semantic search. See how this can be integrated with various graph databases and search engines.
PoolParty Semantic Suite: Solutions for Sustainable Development: The Climate ...
PoolParty Semantic Suite: Solutions for Sustainable Development: The Climate ...Semantic Web Company
Presentation of the webinar: PoolParty for Sustainable Development - the Climate Tagger - taking place on 5 November 2015. Introduction Slides by Florian Bauer of REEEP.
More information and other presentations to be found here: http://bit.ly/1NpTcGT.
Recording of the webinar: https://www.youtube.com/watch?v=3GxtFfLL1ps.More from Semantic Web Company (20)
Leveraging Taxonomy Management with Machine Learning
Leveraging Taxonomy Management with Machine Learning
PoolParty GraphSearch - The Fusion of Search, Recommendation and Analytics
PoolParty GraphSearch - The Fusion of Search, Recommendation and Analytics
Semantics as the Basis of Advanced Cognitive Computing
Semantics as the Basis of Advanced Cognitive Computing
PoolParty Semantic Suite - Release 6.0 (Technical Overview)
PoolParty Semantic Suite - Release 6.0 (Technical Overview)
Taxonomies and Ontologies – The Yin and Yang of Knowledge Modelling
Taxonomies and Ontologies – The Yin and Yang of Knowledge Modelling
PROPEL . Austrian's Roadmap for Enterprise Linked Data
PROPEL . Austrian's Roadmap for Enterprise Linked Data
PoolParty Semantic Suite: Solutions for Sustainable Development: The Climate ...
PoolParty Semantic Suite: Solutions for Sustainable Development: The Climate ...
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Essentials of Automations: Optimizing FME Workflows with Parameters
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Knowledge engineering: from people to machines and back
Keynote at the 21st European Semantic Web Conference
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Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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JMeter webinar - integration with InfluxDB and Grafana
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
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2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
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Execution from the test manager
Orchestrator execution result
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Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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