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.
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.
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.
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
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.
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.
This document discusses the importance of aligning taxonomy and metadata with search engine optimization efforts. It provides an overview of taxonomy and its goals in organizing content and improving findability. The document contrasts the internal perspective of taxonomy with the external perspective of SEO and emphasizes that the two should be integrated. It provides examples of how terminology, metadata, and site structure should align between taxonomy and SEO to improve search visibility and deliver qualified visitors to a website.
KM SHOWCASE 2020 - "Lessons Learned Building a Knowledge Graph" - Chris MarinoKM Institute
This document provides an overview of building a knowledge graph at the Inter-American Development Bank. It discusses how the Bank implemented a knowledge graph to automatically extract entities and concepts from content to create semantic data and recommendations. The solution involved developing taxonomies and ontologies, ingesting content, and using an extractor like PoolParty to tag documents and connect them to concepts in the knowledge graph. Key lessons included creating an organic taxonomy, leveraging extraction scores, using applicable sections of taxonomies, and developing a repeatable ingestion process to continually update the knowledge graph.
Graph databases provide the ability to quickly discover and integrate key relationships between enterprise data sets. Business use cases such as recommendation engines, social networks, enterprise knowledge graphs, and more provide valuable ways to leverage graph databases in your organization. This webinar will provide an overview of graph database technologies, and how they can be used for practical applications to drive business value.
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.
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.
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
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.
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.
This document discusses the importance of aligning taxonomy and metadata with search engine optimization efforts. It provides an overview of taxonomy and its goals in organizing content and improving findability. The document contrasts the internal perspective of taxonomy with the external perspective of SEO and emphasizes that the two should be integrated. It provides examples of how terminology, metadata, and site structure should align between taxonomy and SEO to improve search visibility and deliver qualified visitors to a website.
KM SHOWCASE 2020 - "Lessons Learned Building a Knowledge Graph" - Chris MarinoKM Institute
This document provides an overview of building a knowledge graph at the Inter-American Development Bank. It discusses how the Bank implemented a knowledge graph to automatically extract entities and concepts from content to create semantic data and recommendations. The solution involved developing taxonomies and ontologies, ingesting content, and using an extractor like PoolParty to tag documents and connect them to concepts in the knowledge graph. Key lessons included creating an organic taxonomy, leveraging extraction scores, using applicable sections of taxonomies, and developing a repeatable ingestion process to continually update the knowledge graph.
Graph databases provide the ability to quickly discover and integrate key relationships between enterprise data sets. Business use cases such as recommendation engines, social networks, enterprise knowledge graphs, and more provide valuable ways to leverage graph databases in your organization. This webinar will provide an overview of graph database technologies, and how they can be used for practical applications to drive business value.
This document discusses challenges in developing master data models across multiple domains. Some key challenges include conflicting data structures and semantics between different models, the expectation that each real-world entity should have only one master record even when represented in different domains, and the need to create horizontal views across domains to provide full visibility of entity data. The document argues that a governed, model-driven approach is needed to reduce duplication and inconsistencies when integrating multiple legacy models into a unified master data environment.
This presentation starts off by discussing powerful examples of The Power of Data and the benefits of Data Driven architectures. A Data Governance program is important for the success of Data Driven architectures. We then discuss the challenges of implementing a Data Governance framework on a Big Data Data Lake with open source software including DataPlane, Apache Atlas and Apache Ranger. And finally, we discuss the importance of the democratization of data and the switching to a speed of thought framework with Hive LLAP.
How do you structure your information systems to enable collaboration? Through careful planning, proper structure, and
aligned technology, serendipity can happen in large scale and massive organizational benefits can be achieved.
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.
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.
Knowledge Graph Discussion: Foundational Capability for Data Fabric, Data Int...Cambridge Semantics
Knowledge graphs are on the rise at businesses hungry for greater automation and intelligence with use cases spreading across industries, from fraud detection and chatbots, to risk analysis and recommendation engines. In this webinar we dive into key technical and business considerations, use cases and best practices in leveraging knowledge graphs for better knowledge management.
Augmentation, Collaboration, Governance: Defining the Future of Self-Service BIDenodo
Watch full webinar here: https://bit.ly/3zVJRRf
According to Dresner Advisory’s 2020 Self-Service Business Intelligence Market Study, 62% of the responding organizations say self-service BI is critical for their business. If we look deeper into the need for today’s self-service BI, it’s beyond some Executives and Business Users being enabled by IT for self-service dashboarding or report generation. Predictive analytics, self-service data preparation, collaborative data exploration are all different facets of new generation self-service BI. While democratization of data for self-service BI holds many benefits, strict data governance becomes increasingly important alongside.
In this session we will discuss:
- The latest trends and scopes of self-service BI
- The role of logical data fabric in self-service BI
- How Denodo enables self-service BI for a wide range of users - Customer case study on self-service BI
This document discusses database marketing and the importance of collecting and storing customer data. It covers what constitutes good data and why building a marketing database is beneficial. Some key points include:
- A marketing database allows a company to better understand customers and target communications more effectively across multiple products and services.
- Collecting the right data, integrating it from various sources, and making it accessible is important for success. Underestimating resources and lacking a clear use plan can lead to failure.
- Major profit drivers include customer profiling, research, CRM strategies, lifetime value analysis, and acquisition/retention modeling that the database enables.
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.
This talk was given at SEMANTiCS 2014 in Leipzig. It gives an overview how to develop an enterprise linked data strategy around controlled vocabularies based on SKOS. It discusses how knowledge graphs based on SKOS can extended step by step due to the needs of the organization.
Data centric business and knowledge graph trendsAlan Morrison
The document discusses data-centric architecture and knowledge graphs. It defines key terms like data, content, and knowledge graphs. It discusses how knowledge graphs are evolving to be multi-model and can combine different data structures. The document argues that a data-centric approach is needed to reduce data and application silos and enable greater data reuse. It provides examples of how knowledge graphs can help industries like banking, pharmaceuticals, and oil and gas better manage their data assets and digital twins. The market potential for knowledge graph technologies is large but there is still low awareness of how they can help organizations.
Modelation - how a strategic data mashup integrates with modern data architec...Denodo
Watch full webinar here: https://bit.ly/3C1Ex23
Data Clarity is an approach to building trust in your data and your people. Modelation™ is a method to use business information models to communicate, collaborate, integrate and automate!
To get the most value from your data, you need a hybrid approach – a Top down AND Bottom up! Practitioners need direction, visionaries need some "clarity" - Data Virtualization is an "Enabler" of a Business Model of Meanings (Universal Semantic Model).
Join Seven Verbs, Denodo and Data Governance Lead, David Bowen to see how Business Information Models can be pushed into a data pipeline. How an abstraction layer (Data Virtualization) is essential to a Semantic Model and how communication and collaboration are critical to its success.
Key Takeaways:
- A Business Model of Meanings (Universal Semantic Layer) is enabled with modern data architectures
- Business Information Models are a tool for improving "Data Clarity"
- Finding the value of your data is a team sport
- You can do it, we can help
Transforming Data Management and Time to Insight with Anzo Smart Data Lake®Cambridge Semantics
The document discusses how Anzo Smart Data Lake can help government agencies transform data management and increase time to insight. It provides an overview of Anzo and how it uses semantic knowledge graphs to link and harmonize diverse data sources for self-service data preparation, discovery, and analytics. Examples are given of how Anzo has helped organizations in intelligence and defense integrate data sources and gain better visibility into areas like contract performance. The presentation concludes by discussing how Anzo could help agencies drive business efficiency, enable more self-service for citizens using public data, and suggests next steps of proof of concept or proposal.
Linked Analytics Data Sets allow data from disparate sources to be linked together, making the data more useful for knowledge discovery and analytics. Linked data uses standard web technologies like HTTP, RDF and URIs to share machine-readable interlinked structured data on the web. Analytical data sets are tables that have been semi-denormalized by including descriptions along with ID codes to make the data easier for analysts to use without focusing on efficient storage. The conclusions from data analytics are usually trends, patterns and statistics that help organizations make effective decisions.
Denodo’s Data Catalog: Bridging the Gap between Data and Business (APAC)Denodo
Watch full webinar here: https://bit.ly/3nxGFam
Self service is a major goal of modern data strategists. Denodo’s data catalog is a key piece in Denodo’s portfolio to bridge the gap between the technical data infrastructure and business users. It provides documentation, search, governance and collaboration capabilities, and data exploration wizards. It’s the perfect companion for a virtual layer to fully empower those self service initiatives with minimal IT intervention. It provides business users with the tool to generate their own insights with proper security, governance and guardrails.
In this session you will learn about:
- The role of a virtual semantic layer in self service initiatives
- What are the key capabilities of Denodo’s new Data Catalog
- Best practices and advanced tips for a successful deployment
- How customers are using the Denodo’s Data Catalog to enable self-service initiatives
Intro to big data and applications - day 1Parviz Vakili
This document provides an overview and introduction to big data and its applications. It defines key concepts related to big data, including the five V's of big data (volume, velocity, variety, veracity, and value). It also discusses where big data comes from, different data types (structured, semi-structured, unstructured), and common applications of big data across different industries. Finally, it introduces concepts of data governance, data strategy, and how big data can support digital transformation.
Linked Data has become a broadly adopted approach for information management and data management not only by government organisations but also more and more by various industries.
Enterprise linked data tackles several challenges like the improvement of information retrieval tools or the integration of distributed data silos. Enterprises understand better and better why their information management should not be limited by organisational boundaries but should rather consider to integrate and link information from different spheres like the public internet, government organisations, professional information providers, customers and even suppliers.
On the other hand, enterprise IT architects still tend to pull down the shutters wherever possible. The continuation of the success of the Semantic Web doesn't seem to be limited by technical barriers anymore but rather by people's mindsets of intranets being strictly cut off from other information sources.
In this talk I will throw new light on the reasons why metadata is key for professional information management, and why W3C's semantic web standards are so important to reduce costs of data management through economies of scale. I will discuss from a multi-stakeholder perspective several use cases for the industrialization of semantic technologies and linked data.
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.
In their webinar "Big Data Fabric 2.0 Drives Data Democratization" Ben Szekley, Cambridge Semantics’ SVP of Field Operations, and guest speaker, Forrester’s Noel Yuhanna, author of the Forrester report: “Big Data Fabric 2.0 Drives Data Democratization”, explored why data-driven businesses are making a big data fabric part of their data strategy to minimize data complexity, integrate siloed data, deliver real-time trusted insights, and to create new business opportunities. These are the slides from that webinar.
Evolving Big Data Strategies: Bringing Data Lake and Data Mesh Vision to LifeSG Analytics
The new data technologies, along with legacy infrastructure, are driving market-driven innovations like personalized offers, real-time alerts, and predictive maintenance. However, these technical additions - ranging from data lakes to analytics platforms to stream processing and data mesh —have increased the complexity of data architectures. They are significantly hampering the ongoing ability of an organization to deliver new capabilities while ensuring the integrity of artificial intelligence (AI) models. https://us.sganalytics.com/blog/evolving-big-data-strategies-with-data-lakehouses-and-data-mesh/
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?
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.
More Related Content
Similar to Enterprise Knowledge Graphs - Data Summit 2024
This document discusses challenges in developing master data models across multiple domains. Some key challenges include conflicting data structures and semantics between different models, the expectation that each real-world entity should have only one master record even when represented in different domains, and the need to create horizontal views across domains to provide full visibility of entity data. The document argues that a governed, model-driven approach is needed to reduce duplication and inconsistencies when integrating multiple legacy models into a unified master data environment.
This presentation starts off by discussing powerful examples of The Power of Data and the benefits of Data Driven architectures. A Data Governance program is important for the success of Data Driven architectures. We then discuss the challenges of implementing a Data Governance framework on a Big Data Data Lake with open source software including DataPlane, Apache Atlas and Apache Ranger. And finally, we discuss the importance of the democratization of data and the switching to a speed of thought framework with Hive LLAP.
How do you structure your information systems to enable collaboration? Through careful planning, proper structure, and
aligned technology, serendipity can happen in large scale and massive organizational benefits can be achieved.
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.
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.
Knowledge Graph Discussion: Foundational Capability for Data Fabric, Data Int...Cambridge Semantics
Knowledge graphs are on the rise at businesses hungry for greater automation and intelligence with use cases spreading across industries, from fraud detection and chatbots, to risk analysis and recommendation engines. In this webinar we dive into key technical and business considerations, use cases and best practices in leveraging knowledge graphs for better knowledge management.
Augmentation, Collaboration, Governance: Defining the Future of Self-Service BIDenodo
Watch full webinar here: https://bit.ly/3zVJRRf
According to Dresner Advisory’s 2020 Self-Service Business Intelligence Market Study, 62% of the responding organizations say self-service BI is critical for their business. If we look deeper into the need for today’s self-service BI, it’s beyond some Executives and Business Users being enabled by IT for self-service dashboarding or report generation. Predictive analytics, self-service data preparation, collaborative data exploration are all different facets of new generation self-service BI. While democratization of data for self-service BI holds many benefits, strict data governance becomes increasingly important alongside.
In this session we will discuss:
- The latest trends and scopes of self-service BI
- The role of logical data fabric in self-service BI
- How Denodo enables self-service BI for a wide range of users - Customer case study on self-service BI
This document discusses database marketing and the importance of collecting and storing customer data. It covers what constitutes good data and why building a marketing database is beneficial. Some key points include:
- A marketing database allows a company to better understand customers and target communications more effectively across multiple products and services.
- Collecting the right data, integrating it from various sources, and making it accessible is important for success. Underestimating resources and lacking a clear use plan can lead to failure.
- Major profit drivers include customer profiling, research, CRM strategies, lifetime value analysis, and acquisition/retention modeling that the database enables.
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.
This talk was given at SEMANTiCS 2014 in Leipzig. It gives an overview how to develop an enterprise linked data strategy around controlled vocabularies based on SKOS. It discusses how knowledge graphs based on SKOS can extended step by step due to the needs of the organization.
Data centric business and knowledge graph trendsAlan Morrison
The document discusses data-centric architecture and knowledge graphs. It defines key terms like data, content, and knowledge graphs. It discusses how knowledge graphs are evolving to be multi-model and can combine different data structures. The document argues that a data-centric approach is needed to reduce data and application silos and enable greater data reuse. It provides examples of how knowledge graphs can help industries like banking, pharmaceuticals, and oil and gas better manage their data assets and digital twins. The market potential for knowledge graph technologies is large but there is still low awareness of how they can help organizations.
Modelation - how a strategic data mashup integrates with modern data architec...Denodo
Watch full webinar here: https://bit.ly/3C1Ex23
Data Clarity is an approach to building trust in your data and your people. Modelation™ is a method to use business information models to communicate, collaborate, integrate and automate!
To get the most value from your data, you need a hybrid approach – a Top down AND Bottom up! Practitioners need direction, visionaries need some "clarity" - Data Virtualization is an "Enabler" of a Business Model of Meanings (Universal Semantic Model).
Join Seven Verbs, Denodo and Data Governance Lead, David Bowen to see how Business Information Models can be pushed into a data pipeline. How an abstraction layer (Data Virtualization) is essential to a Semantic Model and how communication and collaboration are critical to its success.
Key Takeaways:
- A Business Model of Meanings (Universal Semantic Layer) is enabled with modern data architectures
- Business Information Models are a tool for improving "Data Clarity"
- Finding the value of your data is a team sport
- You can do it, we can help
Transforming Data Management and Time to Insight with Anzo Smart Data Lake®Cambridge Semantics
The document discusses how Anzo Smart Data Lake can help government agencies transform data management and increase time to insight. It provides an overview of Anzo and how it uses semantic knowledge graphs to link and harmonize diverse data sources for self-service data preparation, discovery, and analytics. Examples are given of how Anzo has helped organizations in intelligence and defense integrate data sources and gain better visibility into areas like contract performance. The presentation concludes by discussing how Anzo could help agencies drive business efficiency, enable more self-service for citizens using public data, and suggests next steps of proof of concept or proposal.
Linked Analytics Data Sets allow data from disparate sources to be linked together, making the data more useful for knowledge discovery and analytics. Linked data uses standard web technologies like HTTP, RDF and URIs to share machine-readable interlinked structured data on the web. Analytical data sets are tables that have been semi-denormalized by including descriptions along with ID codes to make the data easier for analysts to use without focusing on efficient storage. The conclusions from data analytics are usually trends, patterns and statistics that help organizations make effective decisions.
Denodo’s Data Catalog: Bridging the Gap between Data and Business (APAC)Denodo
Watch full webinar here: https://bit.ly/3nxGFam
Self service is a major goal of modern data strategists. Denodo’s data catalog is a key piece in Denodo’s portfolio to bridge the gap between the technical data infrastructure and business users. It provides documentation, search, governance and collaboration capabilities, and data exploration wizards. It’s the perfect companion for a virtual layer to fully empower those self service initiatives with minimal IT intervention. It provides business users with the tool to generate their own insights with proper security, governance and guardrails.
In this session you will learn about:
- The role of a virtual semantic layer in self service initiatives
- What are the key capabilities of Denodo’s new Data Catalog
- Best practices and advanced tips for a successful deployment
- How customers are using the Denodo’s Data Catalog to enable self-service initiatives
Intro to big data and applications - day 1Parviz Vakili
This document provides an overview and introduction to big data and its applications. It defines key concepts related to big data, including the five V's of big data (volume, velocity, variety, veracity, and value). It also discusses where big data comes from, different data types (structured, semi-structured, unstructured), and common applications of big data across different industries. Finally, it introduces concepts of data governance, data strategy, and how big data can support digital transformation.
Linked Data has become a broadly adopted approach for information management and data management not only by government organisations but also more and more by various industries.
Enterprise linked data tackles several challenges like the improvement of information retrieval tools or the integration of distributed data silos. Enterprises understand better and better why their information management should not be limited by organisational boundaries but should rather consider to integrate and link information from different spheres like the public internet, government organisations, professional information providers, customers and even suppliers.
On the other hand, enterprise IT architects still tend to pull down the shutters wherever possible. The continuation of the success of the Semantic Web doesn't seem to be limited by technical barriers anymore but rather by people's mindsets of intranets being strictly cut off from other information sources.
In this talk I will throw new light on the reasons why metadata is key for professional information management, and why W3C's semantic web standards are so important to reduce costs of data management through economies of scale. I will discuss from a multi-stakeholder perspective several use cases for the industrialization of semantic technologies and linked data.
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.
In their webinar "Big Data Fabric 2.0 Drives Data Democratization" Ben Szekley, Cambridge Semantics’ SVP of Field Operations, and guest speaker, Forrester’s Noel Yuhanna, author of the Forrester report: “Big Data Fabric 2.0 Drives Data Democratization”, explored why data-driven businesses are making a big data fabric part of their data strategy to minimize data complexity, integrate siloed data, deliver real-time trusted insights, and to create new business opportunities. These are the slides from that webinar.
Evolving Big Data Strategies: Bringing Data Lake and Data Mesh Vision to LifeSG Analytics
The new data technologies, along with legacy infrastructure, are driving market-driven innovations like personalized offers, real-time alerts, and predictive maintenance. However, these technical additions - ranging from data lakes to analytics platforms to stream processing and data mesh —have increased the complexity of data architectures. They are significantly hampering the ongoing ability of an organization to deliver new capabilities while ensuring the integrity of artificial intelligence (AI) models. https://us.sganalytics.com/blog/evolving-big-data-strategies-with-data-lakehouses-and-data-mesh/
Similar to Enterprise Knowledge Graphs - Data Summit 2024 (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?
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
This document contains summaries of case studies demonstrating how various organizations have successfully implemented data governance programs. One case study describes how a construction firm used a data governance assessment to benchmark their maturity and prioritize initiatives. Another case study highlights how end-user training was critical to adoption at an enterprise organization. A third case study examines which tools and frameworks, such as a data catalog, were important starting points for a financial organization's data governance efforts. The last case study outlines how a federal agency developed a long-term roadmap for their data governance program after an initial 12 week accelerator to demonstrate value from a data catalog solution.
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
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.
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.
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.
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?”
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.
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.
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).
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
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Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
Enterprise Knowledge Graphs - Data Summit 2024
1. Enterprise Knowledge Graphs:
The Importance of Semantics
Heather Hedden
Senior Consultant
Enterprise Knowledge, LLC
Data Summit
May 9, 2024
2. ENTERPRISE KNOWLEDGE
About the Speaker
Heather Hedden
Senior Consultant, Enterprise Knowledge
⬢ Leads the design and development of
taxonomies and ontologies for varied use cases
for diverse clients.
⬢ Taxonomist for over 28 years in various corporate
and consulting roles.
⬢ Instructor of taxonomy design & creation
workshops and courses.
⬢ Author of the book, The Accidental Taxonomist,
3rd edition (Information Today, Inc., 2022).
⬢ Blogs at accidental-taxonomist.blogspot.com
3. Enterprise Knowledge at a Glance
10AREAS OF EXPERTISE
KM STRATEGY & DESIGN TAXONOMY & ONTOLOGY DESIGN
TECHNOLOGY SOLUTIONS AGILE, DESIGN THINKING, & FACILITATION
CONTENT & BRAND STRATEGY KNOWLEDGE GRAPHS, DATA MODELING, & AI
ENTERPRISE SEARCH INTEGRATED CHANGE MANAGEMENT
ENTERPRISE LEARNING CONTENT MANAGEMENT
80+
EXPERT
CONSULTANTS
HEADQUARTERED IN WASHINGTON, DC,
USA
ESTABLISHED 2013 – OUR FOUNDERS AND PRINCIPALS HAVE BEEN PROVIDING
KNOWLEDGE MANAGEMENT CONSULTING TO GLOBAL CLIENTS FOR OVER 20 YEARS.
KMWORLD’S
100 COMPANIES THAT MATTER IN KM (2015, 2016, 2017, 2018,
2019, 2020, 2021, 2022, 2023, 2024)
TOP 50 TRAILBLAZERS IN AI (2020, 2021, 2022)
CIO REVIEW’S
20 MOST PROMISING KM SOLUTION PROVIDERS (2016)
INC MAGAZINE
#2,343 OF THE 5000 FASTEST GROWING COMPANIES (2021)
#2,574 OF THE 5000 FASTEST GROWING COMPANIES (2020)
#2,411 OF THE 5000 FASTEST GROWING COMPANIES (2019)
#1,289 OF THE 5000 FASTEST GROWING COMPANIES (2018)
INC MAGAZINE
BEST WORKPLACES (2018, 2019, 2021, 2022)
WASHINGTONIAN MAGAZINE’S
TOP 50 GREAT PLACES TO WORK (2017)
WASHINGTON BUSINESS JOURNAL’S
BEST PLACES TO WORK (2017, 2018, 2019, 2020)
ARLINGTON ECONOMIC DEVELOPMENT’S
FAST FOUR AWARD FASTEST GROWING COMPANY (2016)
VIRGINIA CHAMBER OF COMMERCE’S
FANTASTIC 50 AWARD – FASTEST GROWING COMPANY (2019,
2020)
AWARD-WINNING
CONSULTANCY
PRESENCE IN BRUSSELS, BELGIUM
STABLE CLIENT BASE
5. ENTERPRISE KNOWLEDGE
Why Enterprise Knowledge Graphs
⬢ In enterprises, structured data lives in multiple siloed data
repositories in separate data applications.
⬢ Combining them into a data lake or data warehouse,
mixed data does not fully share the same original structure.
⬢ A data lake or data warehouse also brings in unstructured data.
⬢ The combined data can be searched, but not comprehensively
analyzed, compared, multi-step queried, discovered, or inferenced.
⬢ Data users need to go beyond merely “finding” data to obtaining
insights and knowledge from the data.
6. ENTERPRISE KNOWLEDGE
Why Enterprise Knowledge Graphs
Problems:
● Data silos
● Heterogeneous data sources
● Mix of unstructured and structured data
● Same things with different names
● Localized meanings for the same thing
Solutions:
● Semantic links across data
● Shared data and content
● Unified vocabulary
● Unified application view
Causing:
● Inefficiencies
● Missed opportunities
● Poor decisions Provided by:
● Knowledge graphs
7. ENTERPRISE KNOWLEDGE
Why Enterprise Knowledge Graphs
Intuitive Interactions
Information in a machine
readable yet human
understandable way.
Discovery of Hidden
Facts and Patterns
Large scale analysis.
Aggregation
and Reasoning
Aggregation of information
from multiple disparate
solutions.
Understanding Context
Adding knowledge to
data through how things
fit together.
.
Knowledge graphs enable:
8. ENTERPRISE KNOWLEDGE
Knowledge Graph Defined
⬢ A model of a knowledge domain combined with instance data.
⬢ Represents unified information across a domain or an organization,
enriched with context and semantics.
⬢ Contains business objects and topics that are closely linked, classified,
and connected to existing data and documents.
⬢ A layer between the actual data/content and the querying layer.
⬢ Both machine-readable and human-readable through some form of
display.
⬢ Gets its name from knowledge base + graph database and optional
graph visualizations.
9. ENTERPRISE KNOWLEDGE
Knowledge Graph Defined
Different definitions from different perspectives:
(based on The Knowledge Graph Cookbook)
Data Architects:
Structured as an additional
virtual data layer, the KG lies
on top of existing databases
or datasets to link all your
data together at scale.
Data Engineers:
A KG provides a structure and
common interface for all of your
data and enables the creation of
smart multilateral relations
throughout your databases.
Knowledge Engineers:
A KG is a model of a
knowledge domain created by
subject matter experts with
the help of intelligent machine
learning algorithms.
Knowledge graph - “A knowledge base that uses a graph-structured data model or
topology to represent and operate on data.”
Knowledge base - “A technology used to store complex structured and unstructured
information used by a computer system.”
- Wikipedia
10. ENTERPRISE KNOWLEDGE
Semantic
Knowledge
Models
Semantic
Search Analytics Recommendation Chatbots
CMS
Data Lake
Data &
Content
Sources
Presentation
Applications
APIs
Shared
Drives
ETL
Extracted/
Virtualized
Data
Controlled
Metadata
Taxonomies Ontology
Data stored in a graph
database or search index
Knowledge
Graph
Data
Warehouse
Structured
Content
CRM
Knowledge Graph Defined - As a Layer
11. ENTERPRISE KNOWLEDGE
Knowledge Graph History
1. “Knowledge Graphs” project for mathematics by researchers of the University of Groningen and
University of Twente, Netherlands, 1982
2. Rise of topic-specific knowledge bases: e.g. Wordnet in 1985; Geonames in 2005
3. General graph-based knowledge repositories, DBpedia (based on linked data) in 2006, Freebase in
2007
4. Google introduced its Knowledge Graph (based on Freebase) to improve search results value in 2012.
5. Large data-heavy companies adopted knowledge graphs: Airbnb, Amazon, Apple, Bank of America,
6. Bloomberg, Facebook, Genentech, Goldman Sachs, JPMorgan Chase, LinkedIn, Microsoft, Uber,
Wells Fargo
7. Knowledge graphs became a topic at various conferences by 2019
8. Enterprise knowledge graphs become the focus
Google web searches on “knowledge graph”
worldwide, April 2016 - April 2024
12. ENTERPRISE KNOWLEDGE
Knowledge Graph Components
A knowledge graph comprises:
1. Extracted data stored or virtualized in either:
a. A graph database, of either:
i. RDF-based triple store
ii. Labeled property graph (LPG)
b. A search index (if not large)
2. Which are tagged/classified/annotated with metadata:
a. as concepts in controlled vocabularies (including taxonomies),
to label and organize the data
b. as attributes managed in an ontology to enrich the data
3. Which are semantically linked to each other with ontology-based
semantic relationships, to represent conceptual relationships
13. ENTERPRISE KNOWLEDGE
Knowledge Graph Components
Graph Database
Data & Content Sources
Business Ontology
Business Taxonomy
Enterprise
Knowledge Graph
extracted
tagged
linked
integrated
14. ENTERPRISE KNOWLEDGE
KG Components: Data From tabular/relational data
to a graph…
Metadata
Data
Class Relation to a class Relation to a class Attribute Attribute
Volkswagen
Automotive
Germany
belongs to
industry
headquartered
in
$293 b
680,000
has
revenue
has
employees
15. ENTERPRISE KNOWLEDGE
KG Components: Data in a Graph Database
Graph databases structure data in the form of graphs, comprising nodes (points,
vertices) and edges (lines, links), not as tables of rows and columns, as relational
database are.
Undirected
graph
Directed
graph
node
edge
Two kinds of graph databases: RDF Triple Stores and Labeled Property Graphs (LPGs)
16. ENTERPRISE KNOWLEDGE
KG Components: Data in a Graph Database
RDF Triple Store Labeled Property Graph
Standardization World Wide Web Consortium Different vendors
Designed for Linked Open Data, publishing and linking data
with formal semantics and no central control
Graph representation for analytics
Processing strengths Set analysis operations Graph traversal
Data management
strengths
Interoperability via global identifiers and a
standard
Data validation, data type support
Compact serialization
Shorter learning curve
Main use cases Data-driven architecture, data integrations,
metadata management, knowledge
representation
Graph analytics, path search,
network analysis
Additional options Inferencing Shortest path calculations
Formal semantics Yes No
17. ENTERPRISE KNOWLEDGE
KG Components: Data in a Graph Database
RDF Triple Store Graph Databases
⬢ Store data
⬢ Store links to content
⬢ Store metadata, controlled vocabularies, taxonomies, ontologies
Based on RDF: Resource Description Framework
⬢ A World Wide Web (W3C) recommendation www.w3.org/TR/rdf11-concepts
⬢ “A standard model for data interchange on the Web”
⬢ Requires the use of URIs to specify things and to specify relations
⬢ Models information as subject – predicate – object triples
18. ⬢ Taxonomies are
controlled, organized
sets of concepts.
⬢ Concepts are used to
tag/categorize content to
make finding and
retrieving specific
content easier.
⬢ This enables better
findability than search
alone.
⬢ The taxonomy is an
intermediary that links
users to the desired
content.
Taxonomy
focus on
organized
Taxonomy
focus on
controlled
KG Components: Taxonomies
19. A taxonomy is a knowledge
organization system (KOS)
that is…
1. Controlled:
A kind of controlled
vocabulary, based on
unambiguous concepts, not
just words
(things, not strings).
2. Organized:
Concepts are organized in a
structure of hierarchies,
categories, or facets to make
them easier to find and
understand.
Controlled Organized
KG Components: Taxonomies
20. ENTERPRISE KNOWLEDGE
KG Components: Taxonomies
What you can do with a taxonomy:
⬢ Consistent tagging: Enable comprehensive and accurate content retrieval
⬢ Normalization: Bring together different names, localizations, languages for concepts
⬢ Standard search: Find content about…. (search string matches taxonomy concepts)
⬢ Topic browse: Explore subjects arranged in a hierarchy and then content on the
subject
⬢ Faceted (filtering/refining) search: Find content meeting a combination of basic
criteria
⬢ Discovery: Find other content tagged with same concepts as tagged to found
content; explore broader, narrower, and (sometimes) related taxonomy topics
⬢ Content curation: Create feeds or alerts based on pre-set search terms
⬢ Metadata management: Support identification, comparison, mapping, analysis, etc.
21. ENTERPRISE KNOWLEDGE
KG Components: Taxonomies
Standard: SKOS (Simple Knowledge Organization System)
⬢ A data model (“standard”) to represent knowledge organization systems
⬢ A World Wide Web (W3C) recommendation (initial version 2004 - revised 2009)
⬢ “A common data model for sharing and linking knowledge organization systems via the
Web” www.w3.org/TR/skos-reference
⬢ To enable easy publication and use of such vocabularies as linked data
⬢ Based on RDF (Resource Description Framework), and encoded in XML, JSON, JSON-LD, etc.
⬢ Concepts and relations are resources with URIs
⬢ A KOS built on SKOS is machine-readable and interchangeable
⬢ Different KOS types (name authority, glossary, classification scheme, thesaurus, taxonomy)
can all be built in SKOS
22. ENTERPRISE KNOWLEDGE
KG Components: Taxonomies
SKOS Principles and Elements
⬢ A KOS is a group of concepts identified with URIs
⬢ Concepts can be grouped hierarchically into concept schemes
⬢ Concepts can be labeled with any number of lexical strings (labels) in any natural language
⬢ Concepts have one preferred label in any natural language, and any number of alternative labels
and hidden labels
⬢ Concepts can be linked to each other using hierarchical and associative semantic relations:
⬢ broader/narrower and related
⬢ Concepts of different concept schemes can be linked using various mapping relations
⬢ Concepts can be documented with notes:
⬢ scope note, definition, editorial note, and history note
⬢ Concepts can additionally be members of collections, which can be labeled or ordered
23. ENTERPRISE KNOWLEDGE
KG Components: Taxonomies
⬢ Centrally managed taxonomies (not a taxonomy built in a siloed application),
now tend to be built on the SKOS data-exchange model.
⬢ Since SKOS is based on RDF, SKOS taxonomies are easily managed in RDF
graph databases, and connect to the data, other taxonomies, and ontologies,
in addition to linking to content.
24. ENTERPRISE KNOWLEDGE
KG Components: Ontologies
Ontology
⬢ A model of a knowledge domain
⬢ Similar to (most of) a knowledge graph, but doesn’t include all actual instance data
⬢ A formal naming and definition of the types (classes), attribute properties, and
interrelationships of entities in a particular domain
⬢ Relations contain meaning, or are “semantic”
⬢ Properties are customized attributes of entities
⬢ Standards provided by W3C: Web Ontology Language (OWL) and RDF-Schema
⬢ A set of of precise descriptive statements about a particular domain
⬢ Statements are expressed as subject-predicate-object triples
⬢ Comprises classes, relations, and attributes, which are linked in statements of triples
Antibiotic
Bacterial
infection
treats
Subject Predicate Object
25. ENTERPRISE KNOWLEDGE
KG Components: Ontologies
Classes: Employee, Country, Organization
Relations: headquartered in < > home of
employed by < > employs
Attributes: Email address, Job title, HQ city, NAICS codes, Currency, Language
Ontology model example:
26. ENTERPRISE KNOWLEDGE
KG Components: Ontologies
RDF (Resource Description
Framework)
www.w3.org/TR/rdf11-concepts
“A standard model for data
interchange on the Web” modeled
in triples
RDFS (RDF-Schema)
www.w3org/2001/sw/wiki/RDFS
“A general-purpose language for
representing simple RDF
vocabularies on the Web”
- Goes beyond RDF to designate
classes and properties of RDF
resources, as ontology basics
OWL (Web Ontology Language)
www.w3.org/OWL
“A Semantic Web language
designed to represent rich and
complex knowledge about things,
groups of things, and relations
between things”
- An extension of RDFS
SPARQL (SPARQL Protocol and
RDF Query Language)
https://www.w3.org/TR/2008/REC-r
df-sparql-query-20080115/
Language to query and updated
RDF data
W3C Standards and Guidelines for for Ontologies
27. ENTERPRISE KNOWLEDGE
KG Components: Ontologies
OWL-Specific Ontology Components
⬢ Entities – subjects (domains) or objects (ranges) of triples - graph nodes
⬢ Classes
⬢ Named sets of concepts that share characteristics and relations
⬢ May group subclasses or individuals (instances of the class)
⬢ Individuals
⬢ Members or instances of a class (may be managed in a linked taxonomy)
⬢ Properties – predicates of triples, about individuals - graph edges
⬢ Object properties
⬢ Relations between individuals
⬢ May be directed, symmetric, or with an inverse
⬢ Datatype properties
⬢ Attributes or characteristics of individuals
⬢ The object of a datatype property is a value
⬢ Literals – values of attributes (metadata values)
28. ENTERPRISE KNOWLEDGE
KG Components: Ontologies + Taxonomies
An ontology is a semantic layer that links to and enhances other controlled vocabularies.
29. ENTERPRISE KNOWLEDGE
KG Components: Ontologies + Taxonomies
What you cannot do with a taxonomy alone,
but can with an added ontology:
⬢ Model complex interrelationships (e.g. in product approval or supply chain processes)
and also connect to content
⬢ Perform complex multi-part searches: e.g. find contacts in a specific location, who are
employed by companies which belong to certain industries
⬢ Search on more specific criteria that vary based on category (class)
⬢ Explore explicit relationships between concepts (not just broader, narrower, related)
⬢ Visualize concepts and semantic relationships
⬢ Perform reasoning and inferencing across data
⬢ Search across datasets, not just search for content
⬢ Connect across siloed content and data repositories across the enterprise
30. ENTERPRISE KNOWLEDGE
Building a Knowledge Graph
Steps to building a knowledge graph:
1. Identify use cases, or problems to be solved.
2. Inventory and organize relevant data and content.
3. Identify and map relationships across data: design and implement an ontology.
4. Incorporate sample data in a graph database.
5. Connect to the ontology/taxonomy, as a test proof of concept.
6. Connect to or build user applications and interfaces.
7. Automate and scale with data pipelines, auto-tagging, and AI.
31. ENTERPRISE KNOWLEDGE
Building a Knowledge Graph:
Sample Infrastructure
Graph Data
Storage &
Query
Data
Orchestration
& ETL
Ontology
Management
Web API to
Development Data
SQL Connection to
Production Data
RDF Extraction of
Production Data
Ontology
Model
Integrated
Ontology
Data
Integration Needs
Source Systems Core Tools End User Apps
Interactive
Data
Visualization
Ontology
Managers
ElasticSearch to
Data Lake
Source
System 1
Source
System 2
Source
System 3
Source
System 4
Querying
Portal
Front End
Application
32. ENTERPRISE KNOWLEDGE
Building a Knowledge Graph
Core software and technology needed:
⬢ Graph database management software
⬢ Taxonomy/ontology management software based on W3C standards
⬢ Search software (such as Solr or Elasticsearch)
⬢ Front-end (web) application
Also important:
⬢ Extract-Transform-Load (ETL) tool to extract data
⬢ Text mining/natural language processing/entity extraction tool
⬢ Machine-learning auto-classification tool
⬢ Capabilities (such as algorithms for weighting/scoring relations) specified in SPARQL
query language for RDF
33. ENTERPRISE KNOWLEDGE
Building a Knowledge Graph
Collaboration of roles:
Challenges/Requirements:
⬢ A specific business/use case, not just curiosity to try new technologies
⬢ Implementation expertise with software tools and guidance from consultants
⬢ Commitment from all stakeholders
⬢ Sufficient time, effort, and expertise to deal with a very complex project
⬢ Data quality
⬢ Knowledge engineers
⬢ Taxonomists
⬢ Ontologists
⬢ Content strategists
⬢ Solutions architects
⬢ Software engineers
⬢ Web developers
⬢ Information architects
⬢ Data engineers
⬢ Data scientists
⬢ Data analysts
⬢ Data architects
34. ENTERPRISE KNOWLEDGE
Knowledge Graph Applications
⬢ Semantic search
⬢ Recommendation
⬢ Compliance and risk prediction
⬢ Question answering engines
An organization typically builds its
own web-browser-based knowledge
graph application.
⬢ Chatbots
⬢ Insight engines
⬢ Expert finder
⬢ Customer 360
35. Enterprise Knowledge White Papers:
⬢ “How to Optimize Data Governance with Enterprise Knowledge Graphs” August 22, 2019
⬢ “Using Knowledge Graph Data Models to Solve Real Business Problems” June 10, 2019
Enterprise Knowledge Blog Articles:
⬢ “How a Knowledge Graph Supports AI: Technical Considerations” September 26, 2023
⬢ “How a Knowledge Graph Can Accelerate Data Mesh Transformation” July 11, 2023
⬢ “Elevating Your Point Solution to an Enterprise Knowledge Graph” November 16, 2022
⬢ “Digital Twins and Knowledge Graphs” May 5, 2022
⬢ “Where Does a Knowledge Graph Fit Within the Enterprise?” April 21, 2022
⬢ “Integrating Search and Knowledge Graphs” October 19, 2020
⬢ “How to Build a Knowledge Graph in Four Steps: The Roadmap From Metadata to AI”
September 9, 2019
Further Reading