I would suggest that Google does not have its own intelligence. If I search for, say, ‘Arnold Schwarzenegger and Harvard’, Google will only suggest documents that contain BOTH Arnold Schwarzenegger and Harvard.

Knowledge graphs help in organizing unstructured data in a way that information can easily be extracted where explicit relations between multiple entities help in the process.

Knowledge Graphs (KGs) have become one of the most powerful tools for modeling the relations between entities in various fields, from biotech to e-comme...

This might seem like a trivial question at first, but it's not. Before we can evaluate whether a particular markup is actually helping Google understand the content of a web page, we need to make sure that Google can crawl and index its markup.

The new tool ecosystem for conceptual modeling with OntoUML. The full paper can be found in https://lnkd.in/evNpjBya #ontology #ontologies #conceptualmodeling...

This Tuesday in the LoGaG reading group, we will have Giorgos Bouritsas from Imperial College London and Andreas Loukas from École polytechnique fédérale...

Framework for zero-shot learning with knowledge graphs. - GitHub - BatsResearch/zsl-kg: Framework for zero-shot learning with knowledge graphs.

Keeping track of faculty research activities, using semantic data integration of public LOD sources combined into a single knowledge graph

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I'm relatively old school, semantically speaking: my first encounters with RDF was in the early 2000s, not long after Tim Berners-Lee's now-famous article in Scientific American introducing the Semantic Web to the world.

Types x Individuals, Instantiation x Specialization. These are among the most fundamental distinctions in Conceptual Modeling. Still, they seem to elude... 23 comments on LinkedIn

Leveraging more data sources using machine learning on graphs to enhance item suggestions for Decathlon members

Content management and its many challenges Content is inherently diverse, written for and understandable only by a specific audience, often multilingual, scattered across silos and different systems, and regularly ambiguous and therefore difficult to understand. Even humans, who are actually experts

Gartner has put “Knowledge Graphs” at the peak of inflated expectations. If you are a Knowledge Graph software vendor, this might be good news. Companies will be buying knowledge graphs without knowing what they are. I’m reminded of an old cartoon of an executive dictating into a dictation machine: “…and in closing, in the future […]

And the Quest for a New Business Model for Open Source Projects

Researchers are turning to the mathematics of higher-order interactions to better model the complex connections within their data.

🤖 A Python library for learning and evaluating knowledge graph embeddings - GitHub - pykeen/pykeen: 🤖 A Python library for learning and evaluating knowledge graph embeddings

Reply to "Why I Don't Use OWL Anymore" by Irene Polikoff - In recent years, many linked data experts have reflected on the use of SHACL in…

This course is aimed at digitization managers and data scientists who are responsible for datasets and knowledge management and helps you to improve the handling of information. Using various knowledge graph tools as examples, learn how to network your datasets and build a digital knowledge base in this course.

Enterprises often find difficulty in applying insights from AI because of a lack of context, this is where knowledge graphs come in.

Ensuring the success of big graph processing for the next decade and beyond.

Within financial services, a lot of firms are still stuck in the past, using decades old tech to compete on a whole new data-driven playing field. These firms face growing challenges from regulators, and as part-fossilized data dinosaurs, they are threatened with extinction. In this paper, Capco’s Mark Kitson explores how knowledge graphs could be the answer to this problem.

How combining three emerging technologies is driving innovation

Data may be the world’s most valuable resource, especially in financial services, which depends on harnessing insightful data. Structures are needed to make growing banks of data decipherable and useful, such as knowledge graphs—interlinked descriptions of relevant objects and their relationships and powerful aids in understanding data.

Graph transformer networks (GTN) are a variant of graph convolutional networks (GCN) that are targeted to heterogeneous graphs in which nodes and edges have associated type information that can be...

These days, building and activating the network is a starting point to ensure adherence to standards and principles, quality of service and continuous ...