How Knowledge Graph and Attention Help? A Quantitative Analysis into Bag-level Relation Extraction
How Knowledge Graph and Attention Help? A Quantitative Analysis into Bag-level Relation Extraction https://t.co/zwBLmn1FtH pic.twitter.com/U9bJEw4qLv— Aaron Bradley (@aaranged) July 27, 2021
"Improving Inductive Link Prediction Using Hyper-Relational Facts" from hyper-relational knowledge graph such as #Wikidata.(Ali et al, 2021)paper: https://t.co/FfdhZJHxqzcode: https://t.co/uURI4qi2Jj.. pic.twitter.com/6HFbh59Na6— WikiResearch (@WikiResearch) July 27, 2021
Visual Query Builder: Building Basic Cypher Queries
Welcome to the launch of Visual Query Builder (VQB) on GraphXR! This is a preview of VQB on an existing Neo4j database supplied with Twitter data from the US presidential election, where we build basic Cypher queries using drag and drop, no-code visual building blocks.
Schedule a free live-training below to learn more today! 👩💻💡
https://meetings.hubspot.com/kineviz/vqb-building-cypher
Special thanks to Alice Benedict & the Kineviz team for creating this preview of VQB!
Vertex Matching Engine: Blazing fast and massively scalable nearest neighbor search
Some of the handiest tools in an ML engineer’s toolbelt are vector embeddings, a way of representing data in a dense vector space. An early example of the
A Survey of Knowledge Graph Embedding and Their Applications
Knowledge Graph embedding provides a versatile technique for representing knowledge. These techniques can be used in a variety of applications such as completion of knowledge graph to predict...
Hey RDF folks, what is the state of the art in making visualizations of RDFS/OWL vocabularies, shapes, of the kind that would make people familiar with UML feel comfortable?— Dan Brickley (@danbri) July 20, 2021
"WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset", collected by pairing Wikipedia articles with a subgraph from the Freebase knowledge graph.(Luyu Wang et al, 2021)paper: https://t.co/FKmpfBARe8data: https://t.co/hS1xbWj9aI pic.twitter.com/HOQxRoc5we— WikiResearch (@WikiResearch) July 21, 2021
ThingFO v1.2's Terms, Properties, Relationships and Axioms - Foundational Ontology for Things / Luis Olsina https://t.co/c91oOm2SAg pic.twitter.com/3YxDhtG1iN— Aaron Bradley (@aaranged) July 21, 2021
Information Integration using the Typed Graph Model / Fritz Laux & Malcolm Crowe https://t.co/b3cAQt9c5U pic.twitter.com/fGm1V309Ul— Aaron Bradley (@aaranged) July 21, 2021
Cf. Damion Dooley, @griffiemma, @plbuttigieg, FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration https://t.co/ARpniFL1FQ https://t.co/MXIfFbAeNc pic.twitter.com/qkHeMpcxQe— Aaron Bradley (@aaranged) July 21, 2021
Of Superheroes, Hypergraphs and the Intricacy of Roles
In my previous post in which I discussed names, I also led in with the fact that I am a writer. Significantly, I did not really talk much about that particular assertion, because is in fact comes with its own rabbit hole quite apart from that associated with names and naming.
Similar Cases Recommendation using Legal Knowledge Graphs https://t.co/I9lTyMwsMF (yet another cc: for you @EmekaOkoye :) pic.twitter.com/Sqrm5uGhFF— Aaron Bradley (@aaranged) July 13, 2021
"We demonstrate that this approach significantly reduces the cognitive load required to users for visualizing and interpreting a knowledge graph...." > Pattern-based Visualization of Knowledge Graphs @lguspree et al. https://t.co/S1DuVfNlxc pic.twitter.com/EEH9o8A8eA— Aaron Bradley (@aaranged) July 7, 2021
SeaNet -- Towards A Knowledge Graph Based Autonomic Management of Software Defined Networks https://t.co/XZSHPF44Hj pic.twitter.com/p9MOGGxhB1— Aaron Bradley (@aaranged) July 6, 2021
Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering
Visual Question Answering (VQA) is concerned with answering free-form questions about an image. Since it requires a deep semantic and linguistic understanding of the question and the ability to...