Modeling Schema.org JSON-LD in TypeScript: A Story in Four Parts - DEV Community 👩💻👨💻
some TypeScript type system features, such as discriminated type unions, powerful type inference, nullability checking, and type intersections, present an opportunity to both model what Schema.org-conformant JSON-LD looks like, while also providing ergonomic completions to the developer
Modeling Schema.org Schema with TypeScript: The Power and Limitations of the TypeScript Type System | Eyas's Blog
Recently, I published schema-dts ( npm , GitHub ), an open source library that models JSON-LD Schema.org in TypeScript. A big reason I wanted to do this…
Today there are also strong user stories in the contexts of investigative work flows based on sets of suspects in graph-based analysis of crime, intelligence, fraud, churn, recommendations and other behavior / networking analytical areas.
Google applied the PageRank algorithm to rank pages to prioritize search results. The algorithm is applicable to networks in other domains, for example, interest rate swap transaction networks in banking, real estate transaction networks, and social networks to find the most influential members of t
prem or both. The discovery and integration layer in the fabric integrates and blends data, drawing on subsets of data from across the underlying data landscape as required.
Muhammad Saleem on Twitter: ""How Representative is a SPARQL Benchmark? An Analysis of RDF Triplestore Benchmarks" has been accepted as full paper to #www2019 #webconf2019. @DiceResearch @akswgroup @NgongaAxel"
"How Representative is a SPARQL Benchmark? An Analysis of RDF Triplestore Benchmarks" has been accepted as full paper to #www2019 #webconf2019. @DiceResearch @akswgroup @NgongaAxel— Muhammad Saleem (@saleem_muhamad) January 21, 2019
My list of 7 great 2018 advancements in Enterprise Knowledge Graphs (and 2019 recommendations) | LinkedIn
While the term “Knowledge Graph” is relatively new (Google 2012) the concept of “representing knowledge as a set of relations between entities - forming a “graph” has been around for much longer. 2019 marks, for example, the 20th anniversary of the publication of arguably the first open standard for
natanael.arndt.xyz: Decentralized Collaborative Knowledge Management using Git (Extended Abstract)
Collaboration of people and machines is a major aspect of the World Wide Web and as well of the Semantic Web. As a result of the collaboration process, structural and content interferences as wel…
Neo4j Graph Database 3.5: Everything You Need to Know [GA Release]
Discover what's new in the 3.5 release of the Neo4j graph database, including a Go language driver, full-text search and indexing, TLS encryption and more.
Never mind the logix: taming the semantic anarchy of mappings in ontologies | Monkeying around with OWL
Mappings between ontologies, or between an ontology and an ontology-like resource, are a necessary fact of life when working with ontologies. For example, GO provides mappings to external resources…
New Approaches for Structured Data:Evolution of Question Aswering
Google has moved from Search to Knowledge, and Focusing on Answering questions with knowledge graph entity information provides has led to answering queries w…
Next generation machine learning powered by graph analytics
meters, human capital is almost always the largest single component. To this end, if you want to use smart building technologies to save costs or increase margins, the main use cas
Nicolas Torzec on Twitter: "Q: which product taxonomies are used in the Shopping / Ad industries? Google's Product Taxonomy is a de facto standard but it lacks freshness, coverage and/or finesse in some areas. I'm also looking at product taxonomies from A
“Q: which product taxonomies are used in the Shopping / Ad industries? Google's Product Taxonomy is a de facto standard but it lacks freshness, coverage and/or finesse in some areas. I'm also looking at product taxonomies from Amazon, Ebay, Walmart, Target, Groupon. What else?”
Node2Vec — Graph Embedding Method - Towards Data Science
Graphs are common #data structures to represent #connecteddata. To use graphs with #deeplearning, we use graph embeddings, a low dimension representations which helps generalize input data. Node2Vec aims to preserve network neighborhoods #datascience #AI
Not Science Fiction - Kurt Cagle's View on Semantic Technologies | SEMANTiCS 2020 US
Kurt Cagle is one of the advisors behind the SEMANTICS 2020 Conference. He is a writer, data scientist and futurist focused on the intersection of computer technologies and society. He is also the founder of Semantical, LLC, a smart data company. In this interview, Kurt shares with us some of his expertise and his vision for the new paths being opened by Semantic Technologies.
I have submitted mine. Find it at https://t.co/tdWjN8NhdX"The RDF* and SPARQL* Approach to Annotate Statements in RDF and to Reconcile RDF and Property Graphs"— Olaf Hartig (@olafhartig) January 11, 2019