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Michael Bronstein on Twitter
Michael Bronstein on Twitter
Another exciting application for #graphml With @frederickmonti back in 2018 we have had our excursion into HEP working on neutrino detection with @uw_icecube https://t.co/roiByGczpi— Michael Bronstein (@mmbronstein) September 25, 2020
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Michael Bronstein on Twitter
Ontotext on Twitter
Ontotext on Twitter
The discussion on personal #knowledgegraphs is gaining traction. @kurt_cagle had it listed among other most common use cases for knowledge graph application: https://t.co/jLpqZOWJjrhttps://t.co/xxjnlAtti2— Ontotext (@ontotext) September 25, 2020
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Ontotext on Twitter
Teodora Petkova on Twitter
Teodora Petkova on Twitter
Knowledge Graphs at a glance by @giuseppe_futia in @TDataScience https://t.co/q5W3CLUXSj— Teodora Petkova (@TheodoraPetkova) September 28, 2020
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Teodora Petkova on Twitter
stephen mallette on Twitter
stephen mallette on Twitter
I'll be discussing "Graph Queries with Gremlin Language Variants" at the Category Theory and Applications group meetup on October 6: https://t.co/MG1HpNEiGd Be prepared to see Gremlin in many different forms! #graphdb pic.twitter.com/OIOsfLvWze— stephen mallette (@spmallette) September 28, 2020
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stephen mallette on Twitter
Alan Morrison on Twitter
Alan Morrison on Twitter
"Therefore, the job of data scientists is to decode the data and to find the knowledge encoded in the data—i.e., to find the model in the data, because the data is the model." - @KirkDBorne, @BoozAllen https://t.co/sZJzBkiA1p pic.twitter.com/NpKaDLLUuL— Kirk Borne (@KirkDBorne) September 28, 2020
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Alan Morrison on Twitter
WikiResearch on Twitter
WikiResearch on Twitter
😉Happy to share our work on multi-hop (open-domain) QA (https://t.co/7BhmCqX64m). TL;DR: you don't need the Wikipedia hyperlinks to achieve SoTA performance on HotpotQA(@qi2peng2 )! A shared RoBERTa encoder (for both Q and docs) is all you need to retrieve SP passages! 1/3 pic.twitter.com/B9JO85Vd1l— Wenhan Xiong (@xwhan_) September 29, 2020
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WikiResearch on Twitter
KM Delivery on Twitter
KM Delivery on Twitter
Beyond improving institutional memory, #knowledgegraphs open an organization's data to the growing sophistication of #artificialintelligence. https://t.co/e5WyplfL8i #semantictechnology #linkeddata #knowledgemanagement— Ontotext (@ontotext) September 30, 2020
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KM Delivery on Twitter
Learning SPARQL on Twitter
Learning SPARQL on Twitter
Interesting paper on which parts of SPARQL query language are easier or more difficult to understand: https://t.co/TgWNXFrCfv— Learning SPARQL (@LearningSPARQL) September 29, 2020
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Learning SPARQL on Twitter
LinuxBot on Twitter
LinuxBot on Twitter
⭕ Insightful Reading: "Semantic #KnowledgeGraphs Soar To The Fore Of #AI" v/@AITimeJournal #BigData #Analytics #DataScience #ML #IoT #IIoT #Python #RStats #TensorFlow #javascript #reactjs #CloudComputing #Linux #programming #coding #Industry40https://t.co/1GQw3IgRUa pic.twitter.com/aCnCY0NCJn— Bomoi Abdulmajid (@Bomoimajid) September 24, 2020
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LinuxBot on Twitter
Klaus Illmayer on Twitter
Klaus Illmayer on Twitter
Everything you always wanted to know but never dared to ask about #knowledgeGraphs:On Oct. 27, 2020, our new free online course "Knowledge Graphs" with @lysander07 and @em_alam will start on the @openHPI platform. Register now at https://t.co/UA9XxTOO3O pic.twitter.com/V6YGkV5Pu4— Harald Sack (@lysander07) September 12, 2020
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Klaus Illmayer on Twitter
Memgraph on Twitter
Memgraph on Twitter
In this blog post, we explore how the performance of Memgraph as evolved across version from v0.15.2 to v1.1.0 and discuss how we were able to reduce memory usage by as much as 50% and improve throughput towards near-linear scalability.https://t.co/R8XTh520CK— Memgraph (@memgraphdb) October 1, 2020
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Memgraph on Twitter
Kelvin Lawrence on Twitter
Kelvin Lawrence on Twitter
Lots of very good material here. Covers a lot of ground including Amazon Neptune, performance tuning, data modeling, common use cases and also some @apachetinkerpop Gremlin and @w3c RDF tutorials. https://t.co/3gdcKcNHiT— Kelvin Lawrence (@gfxman) October 1, 2020
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Kelvin Lawrence on Twitter
Denny Vrandečić on Twitter
Denny Vrandečić on Twitter
Ever had some weird dataset you wanted, like "a list of every US senator ever and their gender?" and thought "ugh, that's gonna be a pain to assemble?"Or "Wikipedia has info on X, but it's gonna be hard to get out?"Well, Have you heard of Wikidata?https://t.co/1L6pkFh43h pic.twitter.com/8KiZyKKlSX— Erin ✨💽 (@erincandescent) October 1, 2020
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Denny Vrandečić on Twitter
James Le on Twitter
James Le on Twitter
https://t.co/kSTvjW2oBv Such a thoughtful article on #knowledgegraphs! @TDataScience pic.twitter.com/BphubNoYwv— James Le (@le_james94) October 1, 2020
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James Le on Twitter
DBpedia on Twitter
DBpedia on Twitter
This is just super satisfying. A @GavinMGleason query to combine conflict data from @dbpedia and @SeshatDatabank I love:1. the explosion2. dragging empires around followed by their battles pic.twitter.com/4oxAT8ynAF— TerminusDB (@TerminusDB) October 1, 2020
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DBpedia on Twitter
Anthony J. Algmin on Twitter
Anthony J. Algmin on Twitter
This current issue of https://t.co/iSEn2yvLfR covers #datagovernance #dataestate #data's #gendergap #datacatalog #dataestate #knowledgegraphs #DMP ... more. New content from @RSeiner @MandySeiner @AJAlgmin Polikoff of @TopQuadrant Beechum and @HBKI71. https://t.co/EEuVW3g7SG pic.twitter.com/sIL7KvLiJp— TDAN (@TDAN_com) October 1, 2020
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Anthony J. Algmin on Twitter
Marco Neumann on Twitter
Marco Neumann on Twitter
In case you missed it, the recording of last week's #Lotico session on JSON-LD is now available. https://t.co/Pzlyjh2S89. #jsonld cc/@neumarcx— Gregg Kellogg (@Gkellogg) October 1, 2020
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Marco Neumann on Twitter