From 1503acfffe6f6059e5cb2d42e6eebd99684b3405 Mon Sep 17 00:00:00 2001 From: sammjainn Date: Sat, 29 Apr 2023 00:28:43 +0530 Subject: [PATCH] Fix design review issues --- 01_index.markdown | 33 ++++++++++++++++----------------- _sass/03-layouts/home.scss | 20 ++++++++++++++------ 2 files changed, 30 insertions(+), 23 deletions(-) diff --git a/01_index.markdown b/01_index.markdown index 1e8a364..a852f5c 100644 --- a/01_index.markdown +++ b/01_index.markdown @@ -3,7 +3,7 @@ # To modify the layout, see https://jekyllrb.com/docs/themes/#overriding-theme-defaults layout: home -permalink: "/" +permalink: '/' header: Home title: Home head: Graphlet AI @@ -13,7 +13,6 @@ background: home/bg.png ---
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Knowledge Graph Construction

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- Recent developments in Large Language Models [LLMs] like ChatGPT and Graph Neural Networks (GNNs) allow us to ETL nodes and edges into XML-like text and sentence encode them using a large language model and then combine them based on semantic inferences made by the LLM in combination with the network topology. LLMs have seen many similar documents as the nodes’ text representation on the world wide web, and if we provide a few clues... they provide state of the art entity resolution for both the blocking and matching stages! + Recent developments in Large Language Models [LLMs] like ChatGPT and Graph Neural Networks (GNNs) allow us to ETL nodes and edges into XML-like text and sentence encode them using a large language model and then combine them based on semantic inferences made by the LLM in combination with the network topology. LLMs have seen many similar documents as the nodes’ text representation on the world wide web, and if we provide a few clues... they provide state of the art entity resolution for both the blocking and matching stages!
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We can build knowledge graphs for any platform, but here are a few tools that are more up our alley to create business value using graphs and networks:
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      GPU-accelerated compute tools like RAPIDS cuGraph
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      Enterprise knowledge graphs that use RDF Triple Stores / SPARQL like StarDog or Ontotext
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    Our Principal Consultant, Russell Jurney

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    Our Principal Consultant, Russell Jurney

    The beautiful face of Russell Jurney. Bald. Shiny.
    My name is Russell Jurney. I work at the intersection of big data, large networks - property graphs or knowledge graphs, representation learning with Graph Neural Networks (GNNs), Natural Language Processing (NLP) and Understanding (NLU), model explainability using network visualization and vector search for information retrieval. @@ -237,4 +236,4 @@ background: home/bg.png Check out my network science portfolio, my blog and my O’Reilly Radar posts.
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    diff --git a/_sass/03-layouts/home.scss b/_sass/03-layouts/home.scss index b8fbc3b..de9290f 100644 --- a/_sass/03-layouts/home.scss +++ b/_sass/03-layouts/home.scss @@ -2,7 +2,7 @@ color: white; text-align: center; font-size: 45px; - padding-top: 20%; + padding-top: 5%; padding-bottom: 2rem; max-width: 80%; margin: auto; @@ -89,14 +89,13 @@ } @media screen and (min-width: 1440px) { .introduction { - background-size: contain; div { max-width: 1440px; } padding-bottom: 5%; } #introduction { - padding-top: 10%; + padding-top: 5%; } .problem-definition { div { @@ -104,17 +103,26 @@ } } } +@media screen and (max-width: 1439px) { + #introduction { + padding-top: 10%; + } +} +@media screen and (min-width: 1101px) { + .introduction { + background-size: contain; + } +} @media screen and (max-width: 1101px) { #introduction { - margin-top: 1rem; - padding-top: 2rem; + padding-top: 5%; } .introduction { padding: 2rem; text-align: justify; background-repeat: no-repeat; padding-top: 10%; - padding-bottom: 15%; + padding-bottom: 25%; div { background-image: none;