This workshop presentation from Enterprise Knowledge team members Joe Hilger, Founder and COO, and Sara Nash, Technical Analyst, was delivered on June 8, 2020 as part of the Data Summit 2020 virtual conference. The 3-hour workshop provided an interdisciplinary group of participants with a definition of what a knowledge graph is, how it is implemented, and how it can be used to increase the value of your organization’s datas. This slide deck gives an overview of the KM concepts that are necessary for the implementation of knowledge graphs as a foundation for Enterprise Artificial Intelligence (AI). Hilger and Nash also outlined four use cases for knowledge graphs, including recommendation engines and natural language query on structured data.
- 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
The Enterprise Knowledge Graph is a disruptive platform that combines emerging Big Data and Graph technologies to reinvent knowledge management inside organizations. This platform aims to organize and distribute the organization’s knowledge, and making it centralized and universally accessible to every employee. The Enterprise Knowledge Graph is a central place to structure, simplify and connect the knowledge of an organization. By removing complexity, the knowledge graph brings more transparency, openness and simplicity into organizations. That leads to democratized communications and empowers individuals to share knowledge and to make decisions based on comprehensive knowledge. This platform can change the way we work, challenge the traditional hierarchical approach to get work done and help to unleash human potential!
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
- 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
The Enterprise Knowledge Graph is a disruptive platform that combines emerging Big Data and Graph technologies to reinvent knowledge management inside organizations. This platform aims to organize and distribute the organization’s knowledge, and making it centralized and universally accessible to every employee. The Enterprise Knowledge Graph is a central place to structure, simplify and connect the knowledge of an organization. By removing complexity, the knowledge graph brings more transparency, openness and simplicity into organizations. That leads to democratized communications and empowers individuals to share knowledge and to make decisions based on comprehensive knowledge. This platform can change the way we work, challenge the traditional hierarchical approach to get work done and help to unleash human potential!
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
by Lukas Masuch, Henning Muszynski and Benjamin Raethlein
The Enterprise Knowledge Graph is a disruptive platform that combines emerging Big Data and Graph technologies to reinvent knowledge management inside organizations. This platform aims to organize and distribute the organization’s knowledge, and making it centralized and universally accessible to every employee. The Enterprise Knowledge Graph is a central place to structure, simplify and connect the knowledge of an organization. By removing complexity, the knowledge graph brings more transparency, openness and simplicity into organizations. That leads to democratized communications and empowers individuals to share knowledge and to make decisions based on comprehensive knowledge. This platform can change the way we work, challenge the traditional hierarchical approach to get work done and help to unleash human potential!
Introduction to Knowledge Graphs for Information Architects.pdfHeather Hedden
There is a growing interest in knowledge graphs to organize information and make it findable in organizations with large amounts of data and content. Unlike other data technologies, a knowledge graph has a structure that is typically based on a taxonomy and ontology, and thus should involve information architects. Knowledge graphs also have more benefits than information findability, including discovery, analysis, and recommendation. Knowledge graphs bring together content and data.
An enterprise knowledge graph involves a change in thinking about information and its access. Instead of designing information architecture in individual applications, an intranet, or website, a knowledge graph extracts data and links to content that exists in multiple different applications and repositories, linking them in a web or graph-like structure by means of customized, semantic relationships.
Data-centric design and the knowledge graphAlan Morrison
The #knowledgegraph--smart data that can describe your business and its domains--is now eating software. We won't be able to scale AI or other emerging tech without knowledge graphs, because those techs all require a transformed data foundation, large-scale integration, and shared data infrastructure.
Key to knowledge graphs are #semantics, #graphdatabase technology and a Tinker Toy-style approach to adding the missing verbs (which provide connections and context) back into your data. A knowledge graph foundation provides a means of contextualizing business domains, your content and other data, for #AI at scale.
This is from a talk I gave at the Data Centric Design for SMART DATA & CONTENT Enthusiasts meetup on July 31, 2019 at PwC Chicago. Thanks to Mary Yurkovic and Matt Turner for a very fun event!.
Climbing the Ontology Mountain to Achieve a Successful Knowledge GraphEnterprise Knowledge
Tatiana Baquero Cakici, Senior KM Consultant, and Jennifer Doughty, Senior Solution Consultant from Enterprise Knowledge’s Data and Information Management (DIME) Division presented at the Taxonomy Boot Camp (KMWorld 2022) on November 17, 2022. KMWorld is the world’s leading knowledge management event that takes place every year in Washington, DC.
Their presentation “Climbing the Ontology Mountain to Achieve a Successful Knowledge Graph” focused on how ontologies have gained momentum as a strong foundation for resolving business challenges through semantic search solutions, recommendation engines, and AI strategies. Cakici and Doughty explained that taxonomists are now faced with the challenge of gaining knowledge and experience in designing and documenting complex solutions that involve the integration of taxonomies, ontologies, and knowledge graphs. They also emphasized that taxonomists are well poised to learn how to design user-centric ontologies, analyze and map data from various systems, and understand the technological architecture of knowledge graph solutions. After describing the key roles and responsibilities needed for a team to successfully implement Knowledge Graph projects, Cakici and Doughty shared practical ontology design considerations and best practices based on their own experience. Lastly, Cakici and Doughty reviewed the most common use cases for knowledge graphs and presented real world applications through a case study that illustrated ontology design and the value of knowledge graphs.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Knowledge Graphs are Worthless, Knowledge Graph Use Cases are PricelessEnterprise Knowledge
At Knowledge Graph Forum 2022, Lulit Tesfaye and Sara Nash, Senior Consultant discuss the importance of establishing valuable and actionable use cases for knowledge graph efforts. The discussion draws on lessons learned from several knowledge graph development efforts to define how to diagnose a bad use case and outlined their impact on initiatives - including strained relationships with stakeholders, time spent reworking priorities, and team turnover. They also share guidance on how to navigate these scenarios and provide a checklist to assess a strong use case.
The three layers of a knowledge graph and what it means for authoring, storag...Neo4j
In this talk, Katariina Kari will discuss a framework for building a Knowledge Graph, by distinguishing between concepts, categories, and data. All three are interconnected to each other, however, they differ in their order of magnitude and the way they come about. A distinction makes sense to understand who is responsible for which part of the knowledge graph. Also, each layer should be governed differently. This framework ultimately helps to create a division of labour inside the company and helps stakeholders to understand the knowledge graph better.
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
The breath and depth of Azure products that fall under the AI and ML umbrella can be difficult to follow. In this presentation I’ll first define exactly what AI, ML, and deep learning is, and then go over the various Microsoft AI and ML products and their use cases.
Welcome to my post on ‘Architecting Modern Data Platforms’, here I will be discussing how to design cutting edge data analytics platforms which meet the ever-evolving data & analytics needs for the business.
https://www.ankitrathi.com
What is data literacy? Which organizations, and which workers in those organizations, need to be data-literate? There are seemingly hundreds of definitions of data literacy, along with almost as many opinions about how to achieve it.
In a broader perspective, companies must consider whether data literacy is an isolated goal or one component of a broader learning strategy to address skill deficits. How does data literacy compare to other types of skills or “literacy” such as business acumen?
This session will position data literacy in the context of other worker skills as a framework for understanding how and where it fits and how to advocate for its importance.
A Work of Zhamak Dehghani
Principal consultant
ThoughtWorks
https://martinfowler.com/articles/data-monolith-to-mesh.html
https://fast.wistia.net/embed/iframe/vys2juvzc3?videoFoam
How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh
Many enterprises are investing in their next generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
In this session, Sergio covered the Lakehouse concept and how companies implement it, from data ingestion to insight. He showed how you could use Azure Data Services to speed up your Analytics project from ingesting, modelling and delivering insights to end users.
"SPARQL Cheat Sheet" is a short collection of slides intended to act as a guide to SPARQL developers. It includes the syntax and structure of SPARQL queries, common SPARQL prefixes and functions, and help with RDF datasets.
The "SPARQL Cheat Sheet" is intended to accompany the SPARQL By Example slides available at http://www.cambridgesemantics.com/2008/09/sparql-by-example/ .
Sara Mae O’Brien Scott and Tatiana Baquero Cakici, Senior Consultants at Enterprise Knowledge (EK), presented “AI Fast Track to Search-Focused AI Solutions” at the Information Architecture Conference (IAC24) that took place on April 11, 2024 in Seattle, WA.
In their presentation, O’Brien-Scott and Cakici focused on what Enterprise AI is, why it is important, and what it takes to empower organizations to get started on a search-based AI journey and stay on track. The presentation explored the complexities of enterprise search challenges and how IA principles can be leveraged to provide AI solutions through the use of a semantic layer. O’Brien-Scott and Cakici showcased a case study where a taxonomy, an ontology, and a knowledge graph were used to structure content at a healthcare workforce solutions organization, providing personalized content recommendations and increasing content findability.
In this session, participants gained insights about the following:
Most common types of AI categories and use cases;
Recommended steps to design and implement taxonomies and ontologies, ensuring they evolve effectively and support the organization’s search objectives;
Taxonomy and ontology design considerations and best practices;
Real-world AI applications that illustrated the value of taxonomies, ontologies, and knowledge graphs; and
Tools, roles, and skills to design and implement AI-powered search solutions.
This presentation from Joe Hilger, Founder and COO of Enterprise Knowledge was presented at the KM Showcase 2020 in Arlington, VA on March 5th. The presentation addresses why knowledge management is the foundation for successful artificial intelligence. Hilger provides reasoning and examples for why taxonomy, content strategy, governance, and KM leadership are foundational requirements for organization's pursuing recommender systems, chat bots, and much more. Lastly, he defines Knowledge Artificial Intelligence and provides a brief overview of knowledge graphs.
by Lukas Masuch, Henning Muszynski and Benjamin Raethlein
The Enterprise Knowledge Graph is a disruptive platform that combines emerging Big Data and Graph technologies to reinvent knowledge management inside organizations. This platform aims to organize and distribute the organization’s knowledge, and making it centralized and universally accessible to every employee. The Enterprise Knowledge Graph is a central place to structure, simplify and connect the knowledge of an organization. By removing complexity, the knowledge graph brings more transparency, openness and simplicity into organizations. That leads to democratized communications and empowers individuals to share knowledge and to make decisions based on comprehensive knowledge. This platform can change the way we work, challenge the traditional hierarchical approach to get work done and help to unleash human potential!
Introduction to Knowledge Graphs for Information Architects.pdfHeather Hedden
There is a growing interest in knowledge graphs to organize information and make it findable in organizations with large amounts of data and content. Unlike other data technologies, a knowledge graph has a structure that is typically based on a taxonomy and ontology, and thus should involve information architects. Knowledge graphs also have more benefits than information findability, including discovery, analysis, and recommendation. Knowledge graphs bring together content and data.
An enterprise knowledge graph involves a change in thinking about information and its access. Instead of designing information architecture in individual applications, an intranet, or website, a knowledge graph extracts data and links to content that exists in multiple different applications and repositories, linking them in a web or graph-like structure by means of customized, semantic relationships.
Data-centric design and the knowledge graphAlan Morrison
The #knowledgegraph--smart data that can describe your business and its domains--is now eating software. We won't be able to scale AI or other emerging tech without knowledge graphs, because those techs all require a transformed data foundation, large-scale integration, and shared data infrastructure.
Key to knowledge graphs are #semantics, #graphdatabase technology and a Tinker Toy-style approach to adding the missing verbs (which provide connections and context) back into your data. A knowledge graph foundation provides a means of contextualizing business domains, your content and other data, for #AI at scale.
This is from a talk I gave at the Data Centric Design for SMART DATA & CONTENT Enthusiasts meetup on July 31, 2019 at PwC Chicago. Thanks to Mary Yurkovic and Matt Turner for a very fun event!.
Climbing the Ontology Mountain to Achieve a Successful Knowledge GraphEnterprise Knowledge
Tatiana Baquero Cakici, Senior KM Consultant, and Jennifer Doughty, Senior Solution Consultant from Enterprise Knowledge’s Data and Information Management (DIME) Division presented at the Taxonomy Boot Camp (KMWorld 2022) on November 17, 2022. KMWorld is the world’s leading knowledge management event that takes place every year in Washington, DC.
Their presentation “Climbing the Ontology Mountain to Achieve a Successful Knowledge Graph” focused on how ontologies have gained momentum as a strong foundation for resolving business challenges through semantic search solutions, recommendation engines, and AI strategies. Cakici and Doughty explained that taxonomists are now faced with the challenge of gaining knowledge and experience in designing and documenting complex solutions that involve the integration of taxonomies, ontologies, and knowledge graphs. They also emphasized that taxonomists are well poised to learn how to design user-centric ontologies, analyze and map data from various systems, and understand the technological architecture of knowledge graph solutions. After describing the key roles and responsibilities needed for a team to successfully implement Knowledge Graph projects, Cakici and Doughty shared practical ontology design considerations and best practices based on their own experience. Lastly, Cakici and Doughty reviewed the most common use cases for knowledge graphs and presented real world applications through a case study that illustrated ontology design and the value of knowledge graphs.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Knowledge Graphs are Worthless, Knowledge Graph Use Cases are PricelessEnterprise Knowledge
At Knowledge Graph Forum 2022, Lulit Tesfaye and Sara Nash, Senior Consultant discuss the importance of establishing valuable and actionable use cases for knowledge graph efforts. The discussion draws on lessons learned from several knowledge graph development efforts to define how to diagnose a bad use case and outlined their impact on initiatives - including strained relationships with stakeholders, time spent reworking priorities, and team turnover. They also share guidance on how to navigate these scenarios and provide a checklist to assess a strong use case.
The three layers of a knowledge graph and what it means for authoring, storag...Neo4j
In this talk, Katariina Kari will discuss a framework for building a Knowledge Graph, by distinguishing between concepts, categories, and data. All three are interconnected to each other, however, they differ in their order of magnitude and the way they come about. A distinction makes sense to understand who is responsible for which part of the knowledge graph. Also, each layer should be governed differently. This framework ultimately helps to create a division of labour inside the company and helps stakeholders to understand the knowledge graph better.
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
The breath and depth of Azure products that fall under the AI and ML umbrella can be difficult to follow. In this presentation I’ll first define exactly what AI, ML, and deep learning is, and then go over the various Microsoft AI and ML products and their use cases.
Welcome to my post on ‘Architecting Modern Data Platforms’, here I will be discussing how to design cutting edge data analytics platforms which meet the ever-evolving data & analytics needs for the business.
https://www.ankitrathi.com
What is data literacy? Which organizations, and which workers in those organizations, need to be data-literate? There are seemingly hundreds of definitions of data literacy, along with almost as many opinions about how to achieve it.
In a broader perspective, companies must consider whether data literacy is an isolated goal or one component of a broader learning strategy to address skill deficits. How does data literacy compare to other types of skills or “literacy” such as business acumen?
This session will position data literacy in the context of other worker skills as a framework for understanding how and where it fits and how to advocate for its importance.
A Work of Zhamak Dehghani
Principal consultant
ThoughtWorks
https://martinfowler.com/articles/data-monolith-to-mesh.html
https://fast.wistia.net/embed/iframe/vys2juvzc3?videoFoam
How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh
Many enterprises are investing in their next generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
In this session, Sergio covered the Lakehouse concept and how companies implement it, from data ingestion to insight. He showed how you could use Azure Data Services to speed up your Analytics project from ingesting, modelling and delivering insights to end users.
"SPARQL Cheat Sheet" is a short collection of slides intended to act as a guide to SPARQL developers. It includes the syntax and structure of SPARQL queries, common SPARQL prefixes and functions, and help with RDF datasets.
The "SPARQL Cheat Sheet" is intended to accompany the SPARQL By Example slides available at http://www.cambridgesemantics.com/2008/09/sparql-by-example/ .
Sara Mae O’Brien Scott and Tatiana Baquero Cakici, Senior Consultants at Enterprise Knowledge (EK), presented “AI Fast Track to Search-Focused AI Solutions” at the Information Architecture Conference (IAC24) that took place on April 11, 2024 in Seattle, WA.
In their presentation, O’Brien-Scott and Cakici focused on what Enterprise AI is, why it is important, and what it takes to empower organizations to get started on a search-based AI journey and stay on track. The presentation explored the complexities of enterprise search challenges and how IA principles can be leveraged to provide AI solutions through the use of a semantic layer. O’Brien-Scott and Cakici showcased a case study where a taxonomy, an ontology, and a knowledge graph were used to structure content at a healthcare workforce solutions organization, providing personalized content recommendations and increasing content findability.
In this session, participants gained insights about the following:
Most common types of AI categories and use cases;
Recommended steps to design and implement taxonomies and ontologies, ensuring they evolve effectively and support the organization’s search objectives;
Taxonomy and ontology design considerations and best practices;
Real-world AI applications that illustrated the value of taxonomies, ontologies, and knowledge graphs; and
Tools, roles, and skills to design and implement AI-powered search solutions.
This presentation from Joe Hilger, Founder and COO of Enterprise Knowledge was presented at the KM Showcase 2020 in Arlington, VA on March 5th. The presentation addresses why knowledge management is the foundation for successful artificial intelligence. Hilger provides reasoning and examples for why taxonomy, content strategy, governance, and KM leadership are foundational requirements for organization's pursuing recommender systems, chat bots, and much more. Lastly, he defines Knowledge Artificial Intelligence and provides a brief overview of knowledge graphs.
Translating AI from Concept to Reality: Five Keys to Implementing AI for Know...Enterprise Knowledge
Lulit Tesfaye explains how foundational knowledge management and knowledge engineering approaches can play a key role in ensuring enterprise Artificial Intelligence (AI) initiatives start right, quickly demonstrate business value, and “stick” within the organization. The presentation includes real world case studies and examples of how organizations are approaching their data and AI transformations through knowledge maturity models to translate organizational information and data into actionable and clickable solutions. Originally delivered at data.world Summit, Spring 2022.
Heather Hedden, Senior Consultant at Enterprise Knowledge, presented “Enterprise Knowledge Graphs: The Importance of Semantics” on May 9, 2024, at the annual Data Summit in Boston.
In her presentation, Hedden describes the components of an enterprise knowledge graph and provides further insight into the semantic layer – or knowledge model – component, which includes an ontology and controlled vocabularies, such as taxonomies, for controlled metadata. While data experts tend to focus on the graph database components (RDF triple store or a label property graph), Hedden emphasizes they should not overlook the importance of the semantic layer.
Data centric business and knowledge graph trendsAlan Morrison
The deck for my kickoff keynote at the Data-Centric Architecture Forum, February 3, 2020. Includes related data, content, and architecture definitions and fundamental explanations, knowledge graph trends, market outlook, transformation case studies and benefits of large-scale, cross-boundary integration/interoperation.
Top ten data and analysis technology trends in 2021Ruchi Jain
Data and analytics leaders should make mission-critical investments to accelerate their ability to predict, transform, and respond based on these ten trends.
Best Data Science Hybrid Course in Pune
Data Science, in its simpler terms, is about generating critical business value from the data through various creative ways. It can also be defined as a mix of data research, algorithms, and technology to solve complex analytical issues. Data is being generated by Companies at an exponential pace. The usable Data form can be different for different sections of people working in an organization.
Data Science Classes help us to explore the data to a granular form and find the needed insights. Data Science is about being analytical or inquisitive wherein asking new questions, doing further explorations, and continuing learning is a part of the job for Data Scientists.
According to Harward Business Review, Data Scientist is the Sexiest Job of the 21st Century.
According to Forbes, IBM Predicts Demand For Data Scientists Will Soar 28% By 2020
GET FRONTLINE DATA SCIENCE TRAINING IN PUNE AT 3RI TECHNOLOGIES
Data Science is a trending niche, for it promises notable mileage for the business economy! It is rather ironic that data which was considered a burden to manage and store only about a few decades ago is now viewed as a resource; courtesy of course to data scientists. They have brought about a paradigmatic change through their skills which allow them to derive the value from raw data. It is important to mention that ‘Raw Data’ is clueless to most laymen, including the high echelons in business management; but when processed through Data Science Tools, it renders value that is precious and immense for the decision-makers and salesmen. They are all riding on the Professionalism of the Data Scientists and this generates the demand of the latter! 3RI Technologies is the leading institution offering Data Science Classes in Pune and fresh graduates as well as Working Professionals can enroll for it.
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Today, Data Science is a much-talked subject and its significance is being deliberated among the business managers who are eager to hire a brilliant professional onboard their firm. Data Science is a milieu space that is shared by the distinct yet related domains of statistics & applicative mathematics, computer programming frameworks and tools, data metrics, and analytics. Machine Learning & associated automation underpins all the above-listed fields, almost as a generic derivative; because it is through this channel that the good results are accrued in favor of the business clients. What are these good results? Let’s talk about them!
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Understanding the New World of Cognitive ComputingDATAVERSITY
Cognitive Computing is a rapidly developing technology that has reached practical application and implementation. So what is it? Do you need it? How can it benefit your business?
In this webinar a panel of experts in Cognitive Computing will discuss the technology, the current practical applications, and where this technology is going. The discussion will start with a review of a recent survey produced by DATAVERSITY on how Cognitive Computing is currently understood by your peers. The panel will also review many components of the technology including:
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Data analysts use tools for data analysis to examine information and work with their teams to generate ideas and business plans. You'll need proficiency with tools for data analytics and data visualisation, as well as math, statistics, communication, and working with data. Investigate this in-demand profession. Want to become a data scientist or data analyst? Learnbay provides the best data science courses in Pune
https://www.learnbay.co/data-science-course/data-science-course-in-pune/
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
Watch this webinar to learn about the benefits of using semantic and graph database technology to create a Data Catalog of all of an enterprise's data, regardless of source or format, as part of a modern IT or data management stack and an important step toward building an Enterprise Data Fabric.
A Comprehensive Guide to Data Science Technologies.pdfGeethaPratyusha
In the fast-paced realm of data science, staying ahead requires a deep understanding of the tools and technologies that drive insights from data. From programming languages to advanced frameworks, the world of data science technologies is vast and dynamic. In this blog, we embark on a comprehensive guide, navigating through the essential tools that empower data scientists to unravel the mysteries hidden within datasets and shape the future of information analysis. For those seeking a structured and immersive learning experience, complementing this tech-centric journey with a well-crafted data science course is the key to unlocking boundless opportunities in this evolving field.
Similar to Introduction to Knowledge Graphs: Data Summit 2020 (20)
Driving Behavioral Change for Information Management through Data-Driven Gree...Enterprise Knowledge
Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida.
In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization.
In this session, participants gained answers to the following questions:
- What is a Green Information Management (IM) Strategy, and why should you have one?
- How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication?
- How can an organization use insights into their data to influence employee behavior for IM?
- How can you reap additional benefits from content reduction that go beyond Green IM?
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfEnterprise Knowledge
Heather Hedden, Senior Consultant at Enterprise Knowledge, presented “The Role of Taxonomy and Ontology in Semantic Layers” at a webinar hosted by Progress Semaphore on April 16, 2024.
Taxonomies at their core enable effective tagging and retrieval of content, and combined with ontologies they extend to the management and understanding of related data. There are even greater benefits of taxonomies and ontologies to enhance your enterprise information architecture when applying them to a semantic layer. A survey by DBP-Institute found that enterprises using a semantic layer see their business outcomes improve by four times, while reducing their data and analytics costs. Extending taxonomies to a semantic layer can be a game-changing solution, allowing you to connect information silos, alleviate knowledge gaps, and derive new insights.
Hedden, who specializes in taxonomy design and implementation, presented how the value of taxonomies shouldn’t reside in silos but be integrated with ontologies into a semantic layer.
Learn about:
- The essence and purpose of taxonomies and ontologies in information and knowledge management;
- Advantages of semantic layers leveraging organizational taxonomies; and
- Components and approaches to creating a semantic layer, including the integration of taxonomies and ontologies
Designing IA for AI - Information Architecture Conference 2024Enterprise Knowledge
With the explosive popularity of ChatGPT, organizations are throwing massive budgets and executive attention at the implementation of AI technologies. Making these solutions work for the enterprise can deliver competitive advantage and open up new solutions and business opportunities that were never before possible. However, without the right Information Architecture (IA) foundations, these projects are bound to fail. In this presentation, Marino and Galdamez provided practical, actionable steps around IA that organizations can take in preparation for future AI solutions.
In this session, attendees:
- Reviewed key elements of IA and discovered how their successful design and implementation can lay the foundations for AI;
- Learned basic terminology surrounding AI, as well as different techniques and applications of AI in enterprise environments;
- Gained a deeper understanding of the feedback loops between IA and AI and the corresponding implications on user experience; and
- Received practical advice on IA design to facilitate its implementation and the success of AI efforts.
Heather Hedden, Senior Consultant at Enterprise Knowledge, presented "An Overview of Taxonomies and AI" on January 30th, 2024, in the inaugural webinar of the Artificial Intelligence webinar series: The promise and the perils,” hosted by the Knowledge & Information Management Group of CILIP, the library and information association of the UK. In her presentation, Heather explained, with examples, how both generative AI and other AI technologies support taxonomy development and use and how taxonomies can support AI applications.
Explore the presentation to learn:
Why both top-down and bottom-up methods are needed in taxonomy creation
What AI methods are used for auto-tagging and auto-classification with taxonomies
How AI methods can extract candidate terms for taxonomy creation
How generative AI can be used for certain bottom-up taxonomy development tasks
How AI can be used to analyze a taxonomy against a corpus of documents
How generative AI can be used in queries to analyze a taxonomy
What AI applications taxonomies can support
Nonprofit KM Journey to Success: Lessons and Learnings at Feeding AmericaEnterprise Knowledge
Sara Duane, Senior Consultant within EK’s Strategic Consulting practice, and EK client Tom Summerfelt, former Chief Research Officer at Feeding America, presented on November 7, 2023 at KMWorld. The talk, “Nonprofit KM Journey to Success: Lessons & Learnings at Feeding America” focused on best practices for designing and implementing KM strategies that directly align with nonprofit organizational goals.
Duane and Summerfelt used their first-hand experience developing a multi-year comprehensive KM Strategy for Feeding America to outline real-world considerations and examples of:
Unique KM challenges faced by organizations in the nonprofit space
Considerations for strategic priorities and KM roadmaps for nonprofits
How to describe the business impact of KM for nonprofits
EK presented with Kate Vilches, Knowledge Management Lead at Ulteig, on November 6, 2022 at the Taxonomy Boot Camp Conference, co-located with KMWorld, in Washington, D.C. The talk, “Taxonomy Roller Coasters: Techniques to Keep Stakeholders on the Ride,” focused on proven stakeholder management techniques during enterprise taxonomy development and launch activities.
Gray and Vilches used their firsthand experience to relate advice, share practical tools, and provide real-life examples to ensure successful stakeholder involvement, reinforcing three key themes for attendees:
How to select partners and build coalitions to ensure long term success;
Overview of the steps, stages, challenges, and thrills of defining and implementing an enterprise taxonomy; and
The importance and finesse of effective change management efforts to ensure that stakeholders begin and remain excited and involved throughout the project.
DGIQ - Case Studies_ Applications of Data Governance in the Enterprise (Final...Enterprise Knowledge
Thomas Mitrevski, Senior Data Management and Governance Consultant and
Lulit Tesfaye, Partner and Vice President of Knowledge and Data Services
presented “Case Studies: Applications of Data Governance in the Enterprise” on December 6th, 2023 at DGIQ in Washington D.C.
In this presentation, Thomas and Lulit detailed their experiences developing strategies for multiple enterprise-scale data initiatives and provided an understanding of common data governance and maturity needs. Thomas and Lulit based their talk on real-world examples and case studies and provided the audience with examples of achieving buy-in to invest in governance tools and processes, as well as the expected return on investment (ROI).
Check out the presentation below to learn:
How Leading Organizations are Benchmarking Their Data Governance Maturity
Why End-User Training was Imperative in Seeing Scaled Governance Program Adoption
Which Tools and Frameworks were Critical in Getting Started with Data Governance
How Organizations Achieved Success with Data Governance in Under 12 Weeks
What Successful Data Governance Implementation Roadmaps Really Look Like
Sara Nash and Urmi Majumder, Principal Consultants at Enterprise Knowledge, presented on April 19, 2023 at KM World in Washington D.C. on the topic of Scaling Knowledge Graph Architectures with AI.
In this presentation, Sara and Urmi defined a Knowledge Graph architecture and reviewed how AI can support the creation and growth of Knowledge Graphs. Drawing from their experience in designing enterprise Knowledge Graphs based on knowledge embedded in unstructured content, Sara and Urmi defined approaches for entity and relationship extraction depending on Enterprise AI maturity and highlighted other key considerations to incorporate AI capabilities into the development of a Knowledge Graph.
View presentation below in order to learn about how:
Assess entity and relationship extraction readiness according to EK’s Extraction Maturity Spectrum and Relationship Extraction Maturity Spectrum.
Utilize knowledge extraction from content to gather important insights into organizational data.
Extract knowledge with three approaches:
RedEx Rule, Auto-Classification Rule, Custom ML Model
Examine key factors such as how to leverage SMEs, iterate AI processes, define use cases, and invest in establishing robust AI models.
This presentation was delivered by EK CEO Zach Wahl at the 2023 Midwest KM Symposium in Kent State, Ohio. The presentation defines Knowledge Management and its value. It also covers key industry trends and outcomes.
Building for the Knowledge Management Archetypes at Your CompanyEnterprise Knowledge
Building for the KM Archetypes at Your Company
Taylor Paschal, Knowledge and Information Management Consultant at Enterprise Knowledge, and Jessica Malloy, Senior Knowledge Manager at Harvard Business Publishing presented on April 19, 2023 at the APQC Conference in Houston, Texas on the topic of Building for the KM Archetypes at Your Company. In this presentation, Jessica and Taylor define common types of personalities that are often present when building a KM program. Jessica and Taylor prompted attendees to think through the root causes of various behaviors and the approaches for taking these into account when driving KM forward in round table discussions supported by this worksheet (link). Attendees left with the ability to:
Describe the importance of focusing on the unique culture of an organization when building and iterating on a KM program
Recognize organizational archetypes and know how to adapt their KM program to them
Conduct a cultural assessment of their own organization to ensure their KM program is meeting them where they are
For KM practitioners, Agile frameworks have long been important for optimizing stakeholder value and satisfaction in KM initiatives. Over 20 years ago, a group of software developers revolutionized their field by introducing the Agile Manifesto to guide their industry in adopting Agile values, frameworks, and practices. However, until now, KM practitioners have lacked a formal framework demonstrating how to apply Agility to KM. In short, it is time to codify these Agile principles in a manner suited for the KM profession. Leveraging the original Agile Manifesto for inspiration, Andrew Politi and Megan Salerno introduced “The Agile KM Manifesto” at KM World 2022. The presentation is designed to initiate a conversation amongst KM practitioners across the industry about this initial version of the Agile KM Manifesto (the 'AKM'), and solicit feedback on future iterations.
Next, the presenters walked through three EK case studies demonstrating how the application of its principles could have saved significant time in those initiatives.
First, we described how a global non-profit approached EK to address duplicate and outdated content, and the lack of content creation standards.
Applicable AKM principle: "Content should only be available to users if it is new, essential, reliable, dynamic, and reusable. If these criteria are not met, the content must be cleaned-up or archived accordingly.”"
Next was a discussion of how national nuclear research laboratory struggled to share and discover knowledge from retiring employees and compartmentalized silos.
Applicable AKM principle: “Tacit knowledge and expertise should be proactively and formally captured and stored in the same manner as explicit knowledge.”
Finally, the presenters described how one of the largest multinational athletic apparel companies struggled to help geographically separated teams collectively and collaboratively reuse knowledge and create content across the globe, even functionally similar focus roles.
Applicable AKM principle: “All KM efforts must leverage a common language. Develop, socialize, and employ a common KM language so stakeholders don't speak past each other and can maintain consensus throughout your KM effort.”
Ultimately, this presentation served to introduce The AKM to the broader community, demonstrate its value, and solicit input from across the industry.
Road Maps & Roadblocks to Federal Electronic Records ManagementEnterprise Knowledge
Angela Pitts, Sr. Consultant at Enterprise Knowledge, and Dave Simmons, Sr. Records Officer at General Services Administration (GSA), presented a case study in federal electronic records management that detailed the success of the GSA's Enterprise Document Management Solution (EDMS). They detailed the strategies used to identify elements of organizational change management required to successfully transition standard functions of records management (RM)—capture, maintenance, disposal, transfer, assignment of metadata, and reporting—from manual, paper-based practices to more efficient and less costly electronic systems.
Records Management is a necessary component of successful Knowledge Management as it systematically manages valuable content created and owned by the business. With technological advancements, most agencies have seen the volume of document records increase exponentially because they are now frequently born and managed as digital content through the records lifecycle. Acknowledging the challenge of managing more content with fewer people, Angela and Dave explained how the design of GSA's lean and agile systems and workflows enabled the agency to reduce the resources and attention needed to manage content collections while maintaining legal compliance and quality standards.
Building an Innovative Learning Ecosystem at Scale with Graph TechnologiesEnterprise Knowledge
Todd Fahlberg of Enterprise Knowledge, and Amber Simpson, a Senior Manager at Walmart Academy, presented on November 9, 2022 at the KMWorld Conference in Washington, DC on the topic of Building an Innovative Learning Ecosystem at Scale with Graph Technologies. In this presentation, Todd and Amber share how they’re making it easier for Walmart’s learning organization to manage content used by 2.4 million global associates with a custom Digital Library. The presentation provides insight into the challenges they faced and the lessons they learned along the way, in addition to their approach to design and implement the Digital Library. Todd and Amber also detail how and why they used graph technologies to make certain their solution can continue to scale to meet the needs of Walmart’s massive workforce and evolving business needs.
Identifying Security Risks Using Auto-Tagging and Text AnalyticsEnterprise Knowledge
On Thursday, November 10, Joe Hilger and Sara Duane spoke at Text Analytics Forum about identifying secure and confidential information using auto-tagging. Information security continues to grow in importance in today's society. We hear stories all of the time about hackers accessing private information from companies and government agencies. Every organization struggles with employees who store confidential information on insecure network drives or cloud drives. Joe and Sara did a project with a federal research organization that used auto-tagging and text analytics to identify confidential information that needed to be moved to a secure location. During the presentation, we shared the approach we took to identify this information and how we made sure that the tagging and text analytics were accurate. Attendees learned best practices for designing a taxonomy for auto-tagging and tuning auto-tagging as well as ways to identify confidential information across the enterprise.
Zach Wahl and Sara Mae O'Brien-Scott spoke at the 2022 Taxonomy Boot Camp in Washington, D.C. on taxonomy's critical role in delivering what every end user now expects—a seamless and personalized experience. Personalization is harnessed by the most successful organizations to anchor their content experience by allowing users to connect with content based on key characteristics. O’Brien-Scott and Wahl provided an understanding of how taxonomy powers personalization by detailing real-world use cases and best practices for taxonomy design for personalization. They discussed the personalization maturity scale, including how taxonomy lays the groundwork for enabling cutting-edge solutions such as recommendation engines, automated content assembly, and omnichannel delivery. They also shared expected outcomes of personalization such as increased conversion rates, a decrease in employee turnover, and stronger user engagement.
JPL’s Institutional Knowledge Graph II: A Foundation for Constructing Enterpr...Enterprise Knowledge
Previously at KMWorld 2021, EK joined JPL to share the vision, approach, and delivery of the Institutional Knowledge Graph (IKG), a centrally maintained, ever-evolving knowledge graph identifying and describing JPL’s enterprise-wide concepts, such as people, organizations, projects, and facilities, and the relationships between them. Since August 2020, the IKG has offered a single source of enterprise information that other JPL applications can leverage to reduce redundancy and out-of-date or inaccurate data. In production for 2 years and now with several releases under its belt, the IKG is beginning to fulfill its promise as a foundational layer in the semantic pyramid for additional taxonomies and knowledge graphs to build upon.
At KM World 2022, Bess Schrader, Senior Solutions Consultant at EK, and Ann Bernath, Software Systems Engineer at JPL, shared a follow-up to the IKG journey including a description of the Enterprise Semantic Platform, a look at new taxonomies and knowledge graphs at JPL (enterprise-wide, others specific to engineering, technical, or science domains) and how they are beginning to leverage the IKG’s foundation of JPL concepts to enrich their dataset into a broader context. This presentation discussed different techniques to federate or synchronize multiple knowledge graphs and how these diverse integrations benefit not only the new datasets, but also the IKG as it continues to pursue its overarching dream--providing answers to questions such as, “Who did what when?”, “Who should you call?”, and “Where is the Robotics Lab?”
Learning 360: Crafting a Comprehensive View of Learning by Using a GraphEnterprise Knowledge
Chris Marino, a Principal Solution Consultant at Enterprise Knowledge (EK), was a featured speaker at this year's Data Architecture Online event organized by Dataversity. Marino presented his webinar "Learning 360: Crafting a Comprehensive View of Learning Content Using a Graph" on July 20, 2022. In his presentation, Marino took participants through the entire Graph development process, including planning, designing, and developing the new tool, highlighting benefits to the organization and lessons learned throughout the process.
Making KM Clickable: The Rapidly Changing State of Knowledge ManagementEnterprise Knowledge
Initially delivered for the Bangalore K-Community Zoom Meetup: “The Digital Edge: Tech Roadmaps and Impacts on KM on June 15th, this deck covers the key takeaways from the leading Knowledge Management book, 'Making Knowledge Management Clickable,' by Zach Wahl and Joe Hilger of Enterprise Knowledge. The presentation covers definitions and value of KM, offers best practices on KM systems, details key types of KM technologies, and discusses some of the common types of KM solutions such as KM Portals and Knowledge Graphs.
How to Quickly Prototype a Scalable Graph Architecture: A Framework for Rapid...Enterprise Knowledge
Sara Nash and Thomas Mitrevski discuss the toolkit to scope and execute knowledge graph prototypes successfully in a matter of weeks. The framework discussed includes the development of a foundational semantic model (e.g. taxonomies/ontologies) and resources and skill sets needed for successful initiatives so that knowledge graph products can scale, as well as the data architecture and tooling required (e.g., orchestration and storage) for enterprise-scale implementation. This presentation was originally delivered at KGC 2022 in Boston, MA.
This is the three-hour "Taxonomy 101" Presentation delivered at KMWorld 2021 (Virtual, KMWorld Connect). The presentation details taxonomy and ontology definitions, business value, and design methodologies. It also covers the concept of Knowledge Graphs in detail. Special attention is given to the differences between taxonomy and ontologies (both from a use and design perspective).
JMeter webinar - integration with InfluxDB and GrafanaRTTS
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
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
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.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
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/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
4. WHAT YOU WILL LEARN TODAY
@EKCONSULTING
How to build a business case for Knowledge Graphs and Enterprise AI
The foundations and technical infrastructure to make Knowledge Graphs a reality
Practical use cases for Knowledge Graphs: Recommendation Engine, Natural Language
Querying, Relationship Discovery, Data Management
Where to begin in Knowledge Graph development – developing an ontology
6. 90% of the data and information we have
today was created just in the past two
years.
Most organizations are built to organize and
manage data and information by type and
department or business function. 80% of
leaders say their systems don’t talk to each
other.
Over 85% of the content and information we
work with is unstructured.
CONFRONTING TODAY’S INFORMATION MANAGEMENT CHALLENGES
90%
AI is set to be the key source of
transformation, disruption, and competitive
advantage in today’s fast changing economy,
contributing to 45% of total economic gains
by 2030.
@EKCONSULTING
7. FOLKSONOMY
Free-text tags.
CONTROLLED LIST
List of pre-defined terms.
Improves consistency.
TAXONOMY
Pre-defined terms &
synonyms.
Hierarchical relationships.
Improves consistency.
Allows for parent/child
content relationships.
Capture related data.
Integration of structured and
unstructured information.
Linked data Store.
Architecture and data
models to enable machine
learning (ML) and other AI
capabilities. Drive efficient
and intelligent data and
information management
solutions.
ONTOLOGY
Predefined classes &
properties.
Expanded relationship types.
Increased expressiveness.
Semantics. Inference.
KNOWLEDGE ORGANIZATION CONTINUUM
@EKCONSULTING
KNOWLEDGE GRAPHS
8. tax·on·o·my (tāk-sōn-mē)
n. pl. tax·on·o·mies
1. The classification of organisms in an
ordered system that indicates natural
relationships.
2. The science, laws, or principles of
classification; systematics.
3. Division into ordered groups or categories:
"Scholars have been laboring to develop a
taxonomy of young killers" (Aric Press).
EK’s Definition of Taxonomy
Controlled vocabularies used to describe or characterize explicit
concepts of information, for purposes of capture, management,
and presentation.
BUSINESS TAXONOMY
@EKCONSULTING
9. A defined data model that describes structured
and unstructured information through:
• entities,
• their properties,
• and the way they relate to one another.
• Ontology is about things, not strings.
• Ontologies model your domain in a machine and
human understandable format.
• Ontologies provide context.
• Effective ontologies require a deep understanding
of the knowledge domain.
BUSINESS ONTOLOGY
@EKCONSULTING
10. § A knowledge graph is a specialized graph or
network of the things we want to describe and
how they are related
§ It is a semantic model since we want to
capture and generate meaning with the model
“The application of graph processing and
graph DBMSs will grow at 100 percent
annually through 2022 to continuously
accelerate data preparation and enable more
complex and adaptive data science.”
– Gartner’s Top 10 Data and Analytics
Technology Trends for 2019
Google’s knowledge graph is a popular
use case
KNOWLEDGE GRAPH
@EKCONSULTING
12. § Consists of triples
§ concept → relationship → concept
§ A linked data store that organizes structured
and unstructured information through:
§ entities,
§ their properties,
§ and relationships.
§ Also known as:
§ Linked Data Store (LD Store)
§ Triple Store
§ “Knowledge Graph”
Subject Predicate Object
Project A hasTitle Title A
Person B isPMOn Project A
Document C isAbout Topic D
Document C isAbout Topic F
Person B IsExpertIn Topic D
… … …
GRAPH DATABASE
@EKCONSULTING
13. Content & Data
Sources
Subject Predicate Object
Person B isPMOn Project A
Document C isAbout Topic D
Document C isAbout Topic F
Person B IsExpertIn Topic D
Business Ontology
Triple Store/Graph Database
Enterprise Knowledge Graph
Person B
Project A
Document C
Person F
Topic D
Topic E
Business Taxonomy
HOW IT ALL FITS TOGETHER
@EKCONSULTING
16. ARTIFICIAL
INTELLIGENCE (AI)
IN ACTION
AI FOR DATA AND INFORMATION MANAGEMENT
ENTAILS LEVERAGING MACHINE CAPABILITIES TO
DISCOVER AND DELIVER ORGANIZATIONAL
KNOWLEDGE AND INFORMATION IN A WAY THAT
CLOSELY ALIGNS WITH HOW WE LOOK FOR AND
PROCESS INFORMATION.
@EKCONSULTING
17. @EKCONSULTING
DECONSTRUCTING AI: DRIVERS
BUSINESS AGILITY AGING INFRASTRUCTUREDATA DYNAMISM
Volume and dynamism of
organizational data/content
(structured and unstructured)
Growing digitalization, aging
of systems and disparate
sources
User experience, knowledge
loss, bad info/data, data team
efficiency
18. DECONSTRUCTING AI: MACHINE LEARNING
Inferred
Relationships
Automatically discover
implicit facts in your
data
Clustering
Detect fraud, identify
risk factors, categorize
customer behavior
Auto-
Classification
Automatically route
incoming requests
to appropriate
channels
Machine Learning
Image & Shape
Recognition
Digital Asset
Management, product
identification, security,
intelligence
Predictive Analytics
Customer retention, risk modeling,
predictive maintenance
Recommendation
Engine
Discover new content and
information based on
context at the point of need
Natural Language
Processing
Simplify user experience,
bring data closer to
business users
@EKCONSULTING
19. Aggregation, Reasoning, and
Optimization
Graphs allow for aggregation of information from
multiple disparate solutions, which allows
users to find information that exists in multiple
locations, and optimizes data management
and governance.
ENTERPRISE KNOWLEDGE GRAPHS & AI
Understanding Context
Relationships between information give us a
better understanding of how things fit
together, adding knowledge to data.
Structured and Unstructured
Information
Allows for the organization and integration of
structured and unstructured information so that
users can search for data and content at the
same time.
Intuitive Interactions
Graphs store information in the way people
speak and process information, while
simultaneously making it machine readable
and therefore ready for human centered
applications, such as natural language search.
Discover Hidden Facts & Patterns
Inferencing allows for large scale analysis and
identification of related topics and things.
@EKCONSULTING
22. SLIDE WITH CIRCLE PHOTO
The Business Challenge
A global development bank needed a better
way to disseminate information and
expertise to all of their staff so that they
could complete projects more efficiently,
without rework and knowledge loss.
Their information and expertise were
contained in thousands of unstructured
documents and publications that needed to
be better organized and made accessible.
The Solution
ü EK developed a semantic hub, leveraging a knowledge graph that
collects organizational content, user context, and project activities.
ü The information powered a recommendation engine that suggests
relevant articles and information when an email or a calendar invite is sent
on a given topic or during searches on that topic, which will eventually
power a chatbot as part of a larger AI Strategy.
ü These outputs were then published on the bank’s website to help
improve knowledge retention and to showcase expertise via Google
recognition and search optimization for future reference.
Outcomes
Using knowledge graphs based on this linked data strategy enabled the bank
to connect all of their knowledge assets in a meaningful way to:
§ Increase the relevancy and personalization of search.
§ Enable employees to discover content across unstructured content types,
such as webinars, classes, or other learning materials based on factors
such as location, interest, role, seniority level, etc.
§ Further facilitate connections between people who share similar interests,
expertise, or location.
@EKCONSULTING
USE CASE #1: RECOMMENDATION ENGINE
24. Because of a Knowledge Graph…
ü Ability to support future business questions and
needs that are currently unknown
ü Greater flexibility to quickly modify and improve
data flows aligned to business needs
ü Flexibility to add new data sources without
making extensive changes to data architectures
and schemas resulting in rapid iteration and
quick adaptation to changing requirements
ü Architecture allows to quickly iterate and grow
new products and services for its users
@EKCONSULTING
Recommendation Engine
25. USE CASE #2: NATURAL LANGUAGE QUERYING ON
STRUCTURED DATA
26. SLIDE WITH CIRCLE PHOTO
The Business Challenge
One of the largest supply chains needed to
provide its business users a way to obtain quick
answers based on very large and varied data
sets.
The data sets were stored in a large RDBMS data
warehouse with little to no context, making it
difficult to understand its value, which information
to use, and what questions it could answer.
The goal was to bring meaningful information
and facts closer to the business to make
funding and investment decisions.
The Solution
ü By extracting topics, places, people, etc. from a given file, EK developed
an ontology to describe the key types of things business users were
interested in and how they relate to each other. EK mapped the various
data sets to the ontology and leveraged semantic Natural Language
Processing (NLP) capabilities to recognize user intent, link concepts,
and dynamically generate the data queries that provide the response.
Outcomes
In doing so, non-technical users were able to uncover the answers to
critical business questions such as:
§ Which of your products or services are most profitable and perform
better?
§ What investments are successful, and when are they successful?
§ How much of a given product did we deliver in a given timeframe?
§ Who were my most profitable customers last year?
§ How can we align products and services with the right experts,
locations, delivery method, and timing?
@EKCONSULTING
USE CASE #2: NATURAL LANGUAGE QUERYING
27. FVC & LVC
Data Virtual
Graph
Mapping
Graph Search
Knowledge Graph IDE
Configure
Graph
Mapping
Query Graph Data
Connects to
Graph DB
Virtualizes
Relational Data
Data SME
Taxonomy &
Ontology Manager
SPARQL
Knowledge
Graph
Business User
Front End UI
Relational
NoSQL
Metadata
External
Internal
Chatbot
Q&A
Semantic
Enterprise
Search
NLP
@EKCONSULTING
USE CASE #2: NATURAL LANGUAGE QUERYING
28. Because of a Knowledge Graph…
@EKCONSULTING
ü Rapid alignment of data elements with natural
language structure of English questions to
identify user intent
ü Flexible mapping of disparate data source
schemas into a single, unified data model that is
“whiteboardable”- accessible to both technical
and nontechnical users
ü Clear definition of key information entities and
their relationships to each other to unleash the
value of data contexts and meaning
Natural Language Querying on Structured Data
29. USE CASE #3: RELATIONSHIP DISCOVERY THROUGH
UNSTRUCTURED DATA
30. The Business Challenge
A federally funded research and development
center (FFRDC) has an extensive “project
library” where they store technical documents,
certifications, and reports related to various
engineering projects.
These documents often don’t have much
associated metadata and are very difficult
to search. When employees start working on
new projects, it’s hard to tell, from the project
libraries, what was done on previous
projects and who did the work.
@EKCONSULTING
The Solution
ü Using an existing business taxonomy developed by the
FFRDC, EK led the development of an enterprise
knowledge graph, connecting documents to projects, topics,
and individuals through auto-tagging
ü EK also developed a semantic search platform, enabling
document searches based on context.
Outcome
Using the enterprise knowledge graph, the FFRDC could then
use the semantic search application to
§ Browse documents by person, project, and topic
§ Analyze relationships between people and projects directly
USE CASE #3: RELATIONSHIP DISCOVERY
THROUGH UNSTRUCTURED DATA
31. ▪ Enhanced Auto-Tagging
▪ History of Documents
▪ Implicit Auto-Tagging
▪ Associate Taxonomy Terms
▪ Classification
▪ Group Content based on Tags
Taxonomy Content
Tag
Co-occurrence
@EKCONSULTING
USE CASE #3: RELATIONSHIP DISCOVERY
THROUGH UNSTRUCTURED DATA
32. v
PROJECTS
PEOPLE
TOPICS
showing 53 results for PROJECT X...
Project X
John Doe (25)
Emily Smith (14)
Robert Jones (5)
Topic A (19)
Topic B (11)
Topic C (3)
Related People
Related Topics
@EKCONSULTING
USE CASE #3: RELATIONSHIP DISCOVERY
THROUGH UNSTRUCTURED DATA
33. Because of a Knowledge Graph…
@EKCONSULTING
ü Ability to support future business questions and
needs that are currently unknown
ü Greater flexibility to quickly modify and improve
data flows aligned to business needs
ü Flexibility to add new data sources without
making extensive changes to data architectures
and schemas
ü Architecture allows to quickly iterate and grow
new products and services for its users
RELATIONSHIP DISCOVERY THROUGH UNSTRUCTURED DATA
35. The Business Challenge
The data scientists and economists at the
Federal Agency were having trouble
connecting siloed data sources to easily
access, interpret and track all the data and
history in order to provide meaningful context
to the Board.
This Agency needed a solution that
enhanced and modernized their metadata
management practices through improved
access and visibility across their data
resources while maintaining the
appropriate security.
@EKCONSULTING
Solution
ü EK led the development of an advanced, semantic metadata
modeling prototype, leveraging a knowledge graph to provide
key contextual and descriptive information that helped map
relationships across the Agency’s regulatory data sources.
ü EK also developed an intuitive front-end user interface that
enabled end-users and data SMEs to explore and access the data
in the model. The model made it easy to find and connect to the
relevant data the business user needs to view key information at a
glance.
Outcome
Data analysts and researchers can now:
§ Access to the Agency’s data resources in a single tool that makes
data stored in multiple locations available without moving or copying
the data.
§ Spend less time tracking or processing data for non-technical users
who can now directly access and explore the data for decision
making.
USE CASE #4: DATA MANAGEMENT
36. Because of a Knowledge Graph…
@EKCONSULTING
ü Achieve powerful alignment between the application
UI and knowledge graph structure allowing the
graph to define the templates that the UI populated
with key data from the graph
ü Encourage the users to explore the information by
traversing relationships that made navigating the
data easy and intuitive
ü Arrange the information from both unstructured
documentation and structured data sources into a
single, structured format
ü Optimize data quality by allowing the analysis of
network effects, through patterns
DATA MANAGEMENT
37. WE’LL BE ANSWERING QUESTIONS NOW
Q A&
THANKS FOR LISTENING
Q & A SESSION