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Label Property Graphs (LPGs) are not ontological-based knowledge graphs because they lack the formal semantics and logical rigor that underpin ontologies
Label Property Graphs (LPGs) are not ontological-based knowledge graphs because they lack the formal semantics and logical rigor that underpin ontologies
Dear LinkedIn Fam, We need to have a conversation about something… Label Property Graphs (LPGs) are not ontological-based knowledge graphs because they lack…
Label Property Graphs (LPGs) are not ontological-based knowledge graphs because they lack the formal semantics and logical rigor that underpin ontologies
·linkedin.com·
Label Property Graphs (LPGs) are not ontological-based knowledge graphs because they lack the formal semantics and logical rigor that underpin ontologies
coming around to the idea of ontologies
coming around to the idea of ontologies
I'm coming around to the idea of ontologies. My experience with entity extraction with LLMs has been inconsistent at best. Even running the same request with… | 63 comments on LinkedIn
coming around to the idea of ontologies
·linkedin.com·
coming around to the idea of ontologies
WorldFAIR (D2.3) Cross-Domain Interoperability Framework (CDIF) (Report Synthesising Recommendations for Disciplines and Cross-Disciplinary Research Areas)
WorldFAIR (D2.3) Cross-Domain Interoperability Framework (CDIF) (Report Synthesising Recommendations for Disciplines and Cross-Disciplinary Research Areas)
The Cross-Domain Interoperability Framework (CDIF) is designed to support FAIR implementation by establishing a ‘lingua franca’, based on existing standards and technologies to support interoperability, in both human- and machine-actionable fashion. CDIF is a set of implementation recommendations, based on profiles of common, domain-neutral metadata standards which are aligned to work together to support core functions required by FAIR. This report presents a core set of five CDIF profiles, which address the most important functions for cross-domain FAIR implementation.  Discovery (discovery of data and metadata resources) Data access (specifically, machine-actionable descriptions of access conditions and permitted use) Controlled vocabularies (good practices for the publication of controlled vocabularies and semantic artefacts) Data integration (description of the structural and semantic aspects of data to make it integration-ready) Universals (the description of ‘universal’ elements, time, geography, and units of measurement). Each of these profiles is supported by specific recommendations, including the set of metadata fields in specific standards to use, and the method of implementation to be employed for machine-level interoperability. A further set of topics is examined, establishing the priorities for further work. These include: Provenance (the description of provenance and processing) Context (the description of ‘context’ in the form of dependencies between fields within the data and a description of the research setting) Perspectives on AI (discussing the impacts of AI and the role that metadata can play) Packaging (the creation of archival and dissemination packages)  Additional Data Formats (support for some of the data formats not fully supported in the initial release, such as NetCDF, Parquet, and HDF5).  In each of these topics, current discussions are documented, and considerations for further work are provided. Visit WorldFAIR online at http://worldfair-project.eu. WorldFAIR is funded by the EC HORIZON-WIDERA-2021-ERA-01-41 Coordination and Support Action under Grant Agreement No. 101058393.
·zenodo.org·
WorldFAIR (D2.3) Cross-Domain Interoperability Framework (CDIF) (Report Synthesising Recommendations for Disciplines and Cross-Disciplinary Research Areas)
What Do D&A Leaders Need to Know About Data Products?
What Do D&A Leaders Need to Know About Data Products?
Attention #Data and #Aalytics Leaders. Our team has fielded over 500 inquires on the subject of #DataProducts. In fact it is now one of the most popular topics… | 28 comments on LinkedIn
What Do D&A Leaders Need to Know About Data Products?
·linkedin.com·
What Do D&A Leaders Need to Know About Data Products?
Knowledge-driven data access means accessing data through an ontology used to represent its meaning at a conceptual level
Knowledge-driven data access means accessing data through an ontology used to represent its meaning at a conceptual level
🧠 Knowledge-driven data access means accessing data through an ontology used to represent its meaning at a conceptual level. 🔎 Connecting an enterprise…
Knowledge-driven data access means accessing data through an ontology used to represent its meaning at a conceptual level
·linkedin.com·
Knowledge-driven data access means accessing data through an ontology used to represent its meaning at a conceptual level
Consolidation in the Semantic Software Industry - Enterprise Knowledge
Consolidation in the Semantic Software Industry - Enterprise Knowledge
As a technology SME in the KM space, I am excited about the changes happening in the semantic software industry. Just two years ago, in my book, I provided a complete analysis of the leading providers of taxonomy and ontology management systems, as well as graph providers, auto-tagging systems, and more. While the software products I evaluated are still around, most of them have new owners.
·enterprise-knowledge.com·
Consolidation in the Semantic Software Industry - Enterprise Knowledge
RDF vs LPG: Friends or Foes?
RDF vs LPG: Friends or Foes?
RDF vs LPG: Friends or Foes? For over a decade, ever since #KnowledgeGraphs (KGs) gained prominence, there has been intense competition between #RDF (also…
RDF vs LPG: Friends or Foes?
·linkedin.com·
RDF vs LPG: Friends or Foes?
Knowledge Graph / Concept Model: Same or Different?
Knowledge Graph / Concept Model: Same or Different?
Knowledge Graph / Concept Model: Same or Different? In my understanding when people say 'knowledge graph' they are usually talking about something OWL/RDF-ish,… | 42 comments on LinkedIn
Knowledge Graph / Concept Model: Same or Different?
·linkedin.com·
Knowledge Graph / Concept Model: Same or Different?
How do you maintain an ontology over time?
How do you maintain an ontology over time?
How do you maintain an ontology over time? Today, I had a wonderful meeting with Kurt Cagle about ontologies, AI, and beyond. We spent some time on this… | 27 comments on LinkedIn
How do you maintain an ontology over time?
·linkedin.com·
How do you maintain an ontology over time?
Unlocking the Power of Generative AI: Why OWL Leads in Knowledge Representation and Semantic Layers
Unlocking the Power of Generative AI: Why OWL Leads in Knowledge Representation and Semantic Layers
Web Ontology Language (OWL) emerges as a superior choice for knowledge representation in generative AI, offering unparalleled expressiveness, reasoning capabilities, and semantic richness. By leveraging OWL-based knowledge graphs, AI systems can generate more accurate, context-aware, and nuanced outputs across diverse ...
·data.world·
Unlocking the Power of Generative AI: Why OWL Leads in Knowledge Representation and Semantic Layers
Taxonomies: Foundational to knowledge management
Taxonomies: Foundational to knowledge management
As the volume of digital content increases, the ability to manage it becomes more important. Taxonomy and metadata are vital to finding products, conducting scientific research, and keeping track of organizational information. They also enable a wide variety of analytics on unstructured data. We can see the results of a well-designed taxonomy, but behind the scenes, there's a lot more going on than meets the eye.
·kmworld.com·
Taxonomies: Foundational to knowledge management
Operationalizing the information architecture
Operationalizing the information architecture
✨ Operationalizing the information architecture 👇 There are three main ways to operationalize the information architecture, depending on how the data plane… | 14 comments on LinkedIn
Operationalizing the information architecture
·linkedin.com·
Operationalizing the information architecture
Where do you start when you want to build an ontology?
Where do you start when you want to build an ontology?
Where do you start when you want to build an ontology? Building an ontology sounds like a big, complex task, right? With all those high-level frameworks like… | 28 comments on LinkedIn
Where do you start when you want to build an ontology?
·linkedin.com·
Where do you start when you want to build an ontology?
Data Product Vocabulary (DPROD)
Data Product Vocabulary (DPROD)
The Data Product (DPROD) specification is a profile of the Data Catalog (DCAT) Vocabulary, designed to describe Data Products. This document defines the schema and provides examples for its use.
The Data Product (DPROD) specification is a profile of the Data Catalog (DCAT) Vocabulary, designed to describe Data Products. This document defines the schema and provides examples for its use. DPROD extends DCAT to enable publishers to describe Data Products and data services in a decentralized way. By using a standard model and vocabulary, DPROD facilitates the consumption and aggregation of metadata from multiple Data Marketplaces. This approach increases the discoverability of products and services, supports decentralized data publishing, and enables federated search across multiple sites using a uniform query mechanism and structure. The namespace for DPROD terms is https://ekgf.github.io/dprod/# The suggested prefix for the DPROD namespace is dprod DPROD follows two basic principles: Decentralize Data Ownership: To make data integration more efficient, tasks should be shared among multiple teams. DCAT helps by offering a standard way to publish datasets in a decentralized manner. Harmonize Data Schemas: Using shared schemas helps unify different data formats. For instance, the DPROD specification provides a common set of rules for defining a Data Product. You can extend this schema as needed. The DPROD specification builds on DCAT by connecting DCAT Data Services to DPROD Data Products using Input and output ports. These ports are used to publish and consume data from a Data Product. DPROD treats ports as dcat data services, so the data exchanged can be described using DCAT's highly expressive metadata around distributions and datasets. This approach also allows you to create your own descriptions for the data you are sharing. You can use a special property called conformsTo from DCAT to link to your own set of rules or guidelines for your data. The DPROD specification has four main aims: To provide unambiguous and sharable semantics to answer the question: 'What is a data product?' Be simple for anyone to use, but expressive enough to power large data marketplaces Allow organisations to reuse their existing data catalogues and dataset infrastructure To share common semantics across different Data Products and promote harmonisation
·ekgf.github.io·
Data Product Vocabulary (DPROD)