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Folksonomies: a bottom-up social categorization system
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The first presentation about collaborative tagging i did
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Recommended
Taxonomy, ontology, folksonomies & SKOS.
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Janet Leu
Designing website and intranet taxonomies with a consideration to serving user experience. Presented at World IA Day 2020.
Taxonomies for Users
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Heather Hedden
Benefits of having a taxonomy for content management, information management, and knowledge management
Benefits of Taxonomies
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Heather Hedden
Presented by Access Innovations, Inc. president Marjorie M.K. Hlava at the 2013 Taxonomy Boot Camp, November 5, 2013.
Taxonomy Fundamentals Workshop 2013
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Presentation given at the National Federation of Advanced Information Services (NFAIS) conference: "Improving the User Search Experience" October 2010, in Philadelphia, PA
Taxonomies and Folksonomies
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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).
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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.
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See how ontologies and taxonomies can play together to reach the ultimate goal, which is the cost-efficient creation and maintenance of an enterprise knowledge graph. The knowledge modelling methodology is supported by approaches taken from NLP, data science, and machine learning.
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Organizations have been chasing the dream of data democratization, unlocking and accessing data at scale to serve their customers and business, for over a half a century from early days of data warehousing. They have been trying to reach this dream through multiple generations of architectures, such as data warehouse and data lake, through a cambrian explosion of tools and a large amount of investments to build their next data platform. Despite the intention and the investments the results have been middling. In this keynote, Zhamak shares her observations on the failure modes of a centralized paradigm of a data lake, and its predecessor data warehouse. She introduces Data Mesh, a paradigm shift in big data management that draws from modern distributed architecture: considering domains as the first class concern, applying self-sovereignty to distribute the ownership of data, applying platform thinking to create self-serve data infrastructure, and treating data as a product. This talk introduces the principles underpinning data mesh and Zhamak's recent learnings in creating a path to bring data mesh to life in your organization.
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This invited keynote at the Social Computing Track at WI-IAT21 gives an introduction to Knowledge Graphs and how they are built collaboratively by us. It gives also presents a brief analysis of the links in Wikidata.
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I gave this presentation at the Advanced Architecture Conference, Bill Inmon, 2011 in Evergreen, Colorado. This presentation covers a new breed of data warehousing called Operational Data Warehousing. These are the next steps in business intelligence towards self-service BI and enabling users to do more with their enterprise data warehouse solution. Specifically, it talks about how the Data Vault model fits in to this picture. If you would like to use the slides, please e-mail me first, I'd be happy to discuss it with you.
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This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems. Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/) Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Data Mesh Part 4 Monolith to Mesh
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Theresa regli bw-3
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Week 8 slides from the class "Social Web 2.0" I taught at the University of Washington's Masters in Communication program in 2007. Most of the content is still very relevant today. Topics: Social metadata, ratings, and social tagging.
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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.
Introducing the Agile KM Manifesto.pdf
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Enterprise Knowledge
- Learn to understand what knowledge graphs are for - Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies) - Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods. - Understand knowledge graphs as a basis for data integration in companies - Understand knowledge graphs as tools for data governance and data quality management - Implement and further develop knowledge graphs in companies - Query and visualize knowledge graphs (including SPARQL and SHACL crash course) - Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision - Develop digital assistants and question and answer systems based on semantic knowledge graphs - Understand how knowledge graphs can be combined with text mining and machine learning techniques - Apply knowledge graphs in practice: Case studies and demo applications
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See how ontologies and taxonomies can play together to reach the ultimate goal, which is the cost-efficient creation and maintenance of an enterprise knowledge graph. The knowledge modelling methodology is supported by approaches taken from NLP, data science, and machine learning.
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FAIRy stories: the FAIR Data principles in theory and in practice
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Organizations have been chasing the dream of data democratization, unlocking and accessing data at scale to serve their customers and business, for over a half a century from early days of data warehousing. They have been trying to reach this dream through multiple generations of architectures, such as data warehouse and data lake, through a cambrian explosion of tools and a large amount of investments to build their next data platform. Despite the intention and the investments the results have been middling. In this keynote, Zhamak shares her observations on the failure modes of a centralized paradigm of a data lake, and its predecessor data warehouse. She introduces Data Mesh, a paradigm shift in big data management that draws from modern distributed architecture: considering domains as the first class concern, applying self-sovereignty to distribute the ownership of data, applying platform thinking to create self-serve data infrastructure, and treating data as a product. This talk introduces the principles underpinning data mesh and Zhamak's recent learnings in creating a path to bring data mesh to life in your organization.
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This invited keynote at the Social Computing Track at WI-IAT21 gives an introduction to Knowledge Graphs and how they are built collaboratively by us. It gives also presents a brief analysis of the links in Wikidata.
Knowledge graphs on the Web
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Armin Haller
I gave this presentation at the Advanced Architecture Conference, Bill Inmon, 2011 in Evergreen, Colorado. This presentation covers a new breed of data warehousing called Operational Data Warehousing. These are the next steps in business intelligence towards self-service BI and enabling users to do more with their enterprise data warehouse solution. Specifically, it talks about how the Data Vault model fits in to this picture. If you would like to use the slides, please e-mail me first, I'd be happy to discuss it with you.
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Introduction to the what, when, why, where, and who of conducting website content inventories and audits, with tips on auditing for content quality, performance, and competitive advantage.
Introduction to Content Inventories and Audits
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Paula Ladenburg Land
Ontology has its roots as a field of philosophical study that is focused on the nature of existence. However, today's ontology (aka knowledge graph) can incorporate computable descriptions that can bring insight in a wide set of compelling applications including more precise knowledge capture, semantic data integration, sophisticated query answering, and powerful association mining - thereby delivering key value for health care and the life sciences. In this webinar, I will introduce the idea of computable ontologies and describe how they can be used with automated reasoners to perform classification, to reveal inconsistencies, and to precisely answer questions. Participants will learn about the tools of the trade to design, find, and reuse ontologies. Finally, I will discuss applications of ontologies in the fields of diagnosis and drug discovery. Bio: Dr. Michel Dumontier is an Associate Professor of Medicine (Biomedical Informatics) at Stanford University. His research focuses on the development of methods to integrate, mine, and make sense of large, complex, and heterogeneous biological and biomedical data. His current research interests include (1) using genetic, proteomic, and phenotypic data to find new uses for existing drugs, (2) elucidating the mechanism of single and multi-drug side effects, and (3) finding and optimizing combination drug therapies. Dr. Dumontier is the Stanford University Advisory Committee Representative for the World Wide Web Consortium, the co-Chair for the W3C Semantic Web for Health Care and the Life Sciences Interest Group, scientific advisor for the EBI-EMBL Chemistry Services Division, and the Scientific Director for Bio2RDF, an open source project to create Linked Data for the Life Sciences. He is also the founder and Editor-in-Chief for a Data Science, a new IOS Press journal featuring open access, open review, and semantic publishing.
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Taxonomy 101: Classifying DITA Tasks
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easyDITA
An introduction to the FAIR principles and a discussion of key issues that must be addressed to ensure data is findable, accessible, interoperable and re-usable. The session explored the role of the CDISC and DDI standards for addressing these issues. Presented by Gareth Knight at the ADMIT Network conference, organised by the Association for Data Management in the Tropics, in Antwerp, Belgium on December 1st 2015.
Preparing Data for Sharing: The FAIR Principles
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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.
Big Data Fabric 2.0 Drives Data Democratization
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This presentation, origninally presented at the Knowledge Management Institute's KM Symposium on March 27, 2014, addresses the concepts of business taxonomy value, taxonomy design methodology, and taxonomy design best practices. It is intended as an introductory deck for anyone seeking guidance on taxonomy design efforts.
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This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems. Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/) Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
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R Aunpad
Week 8 slides from the class "Social Web 2.0" I taught at the University of Washington's Masters in Communication program in 2007. Most of the content is still very relevant today. Topics: Social metadata, ratings, and social tagging.
Social Web 2.0 Class Week 8: Social Metadata, Ratings, Social Tagging
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FaceTag at IASummit 2007
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Taxonomy Development and Digital Projects
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FaceTag is a working prototype of a semantic collaborative tagging tool conceived for bookmarking information architecture resources. It aims to show how the widespread homogeneous and flat keywords' space created by users while tagging can be effectively mixed with a richer faceted classification scheme to improve the �information scent� and �berrypicking� capabilities of the system. The additional semantic structure is aggregated both implicitly observing user behaviour and explicitly introducing a compelling user experience that facilitates the end-user creation of relationships between tags. FaceTag current implementation is written in PHP / SQL and includes an open API which allows querying and integration from other applications.
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Co-researcher with Nieves Regino ** this Presentation shows, KOS, types and future insights which has political effect.
Knowledge organization
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Knowledge organization system
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Presentation given at Hypertext 2006 in Odense, Denmark on classifying tagging systems. <a href="http://alumni.media.mit.edu/~cameron/cv/pubs/2006-ht06-tagging-paper">Full paper available here</a>.
HT06, Position Paper, Tagging, Taxonomy, Flickr, Academic Article, ToRead, Pr...
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Henry stewart dam2010_taxonomicsearch_markohurst
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study or concern about what kinds of things exist what entities there are in the universe. the ontology derives from the Greek onto (being) and logia (written or spoken). It is a branch of metaphysics , the study of first principles or the root of things.
Ontology
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FaceTag: Integrating Bottom-up and Top-down Classification in a Social Taggin...
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Folksonomies: a bottom-up social categorization system
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