From 7795804ee59f14e8d98be08eb5244f5db4c16e61 Mon Sep 17 00:00:00 2001 From: Russell Jurney Date: Tue, 7 Mar 2023 11:04:00 -0800 Subject: [PATCH 1/9] A bunch more slides and content for home page --- 01_index.markdown | 75 ++++++++++++++++-- .../Entity-Resolution---Ditto-Encoding.jpg | Bin 0 -> 274987 bytes .../Entity-Resolution-Phase-2---Blocking.jpg | Bin 0 -> 322811 bytes ...y-Resolution-Phase-2---Manual-Matching.jpg | Bin 0 -> 388610 bytes ...esolution-Phase-3---Embedding-Distance.jpg | Bin 0 -> 414197 bytes ...lution-Phase-3---Fine-Tuned-Classifier.jpg | Bin 0 -> 493741 bytes ...tity-Resolution-Phase-3---LSH-Blocking.jpg | Bin 0 -> 464796 bytes ...Triple-Stores-vs-Property-Graphs-Small.jpg | Bin 0 -> 327535 bytes .../RDF-Triple-Stores-vs-Property-Graphs.jpg | Bin 0 -> 351397 bytes 9 files changed, 69 insertions(+), 6 deletions(-) create mode 100644 assets/slides/Entity-Resolution---Ditto-Encoding.jpg create mode 100644 assets/slides/Entity-Resolution-Phase-2---Blocking.jpg create mode 100644 assets/slides/Entity-Resolution-Phase-2---Manual-Matching.jpg create mode 100644 assets/slides/Entity-Resolution-Phase-3---Embedding-Distance.jpg create mode 100644 assets/slides/Entity-Resolution-Phase-3---Fine-Tuned-Classifier.jpg create mode 100644 assets/slides/Entity-Resolution-Phase-3---LSH-Blocking.jpg create mode 100644 assets/slides/RDF-Triple-Stores-vs-Property-Graphs-Small.jpg create mode 100644 assets/slides/RDF-Triple-Stores-vs-Property-Graphs.jpg diff --git a/01_index.markdown b/01_index.markdown index 47946cc..134a372 100644 --- a/01_index.markdown +++ b/01_index.markdown @@ -42,7 +42,62 @@ background: home/bg.png
Transformed, Cleaned Data in Silver Tables
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+ 2) Extract a graph from text using Natural Language Processing (NLP) via a chain of operations: NER —> IE —> EL. Named entity recognition (NER) points out entities corresponding to nodes. Information Extraction (IE) creates relationships [edges] between entities. Entity linking links nodes and edges extracted from text documents into single into a core graph established via ETL. +
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+ Initially a process of exploratory data analysis (EDA) reveals patterns that can be used to handle the combinatoric problems arising from the need in entity matching to compare every node in the graph with every node. The complexity of this comparison is n^2, where n is the number of nodes. This can quickly get out of hand with millions or billions of nodes! Blocking is a strategy to prune the set of nodes compared down to groups that are more manageable. +
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+ 3) Entity resolution using network topology and natural language processing. Recent developments in Large Language Models [LLMs] and Graph Neural Networks (GNNs) allow us to encode nodes and edges as XML-like text using a language model and then combine them based on semantic inferences made by the LLM in combination with those made about the network via a GNN. LLMs have seen many similar documents as the nodes’ text representation on the world wide web. +
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+ Manual blocking and matching for numerous datasets is a cumbrsome and expensive activity. Advances in AI - representation learning and an architecture from Google called Grale - make a generic entity resolution (ER) system possible. This system is configurable to work across multiple datasets by embedding records using large language models (LLMs) such as GPT-3 or ChatGPT, but tuned specifically for the entity matching task. +
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A property graph is a set of objects representing nodes [also known as vertex/vertices] and edges [also known as links].
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+ Property Graphs vs Triple Stores: Reification, making objects out of properties +
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Why should I build a knowledge graph for my business?

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I'll let you in on a secret that is driving the popularity of enterprise knowledge graphs, property graphs, graph databases and Graph Neural Networks (GNNs): MOST DATA IS GRAPH DATA. To compose a single table to get the corresponding vectors, matrices and tensors we load into GPUs to drive machine learning algorithms, several tables have usually been combined [squashed] into one table. There's a problem with this... it is a lossy process. We threw away the relationships. Graph neural networks are able to learn better to build more powerful models because they have a greater potential by matching the structure of the data’s entities and their relationships. +
+ I'll let you in on a secret that is driving the popularity of enterprise knowledge graphs, property graphs, graph databases and Graph Neural Networks (GNNs): MOST DATA IS GRAPH DATA. To compose a single table to get the corresponding vectors, matrices and tensors we load into GPUs to drive machine learning algorithms, several tables have usually been combined [squashed] into one table. There's a problem with this... it is a lossy process. We threw away the relationships. Graph neural networks are able to learn better to build more powerful models because they have a greater potential by matching the structure of the data’s entities and their relationships.
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What’s the real story with Graph Neural Networks (GNNs)?

I'll let you in on a secret that is driving the popularity of enterprise knowledge graphs, property graphs, graph databases and Graph Neural Networks (GNNs): MOST DATA IS GRAPH DATA. To compose a single table to get the corresponding vectors, matrices and tensors we load into GPUs to drive machine learning algorithms, several tables have usually been combined [squashed] into one table. There's a problem with this... it is a lossy process. We threw away the relationships. Graph neural networks are able to learn better to build more powerful models because they have a greater potential by matching the structure of the data’s entities and their relationships.
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+ Raw Data in Bronze Tables +
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- GNN Problem Types -
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z=2h1j55e#}td2Vrr*Rj7kdViT6iYGwAB0kwVzdhZ#1bmFJp(Y?Zt(by+S+;5{5>1~ zO Date: Tue, 7 Mar 2023 11:18:42 -0800 Subject: [PATCH 3/9] Adding more content, text and diagrams. Most diagrams are in place :) --- 01_index.markdown | 156 ++++++++++++++++------------------------------ 1 file changed, 53 insertions(+), 103 deletions(-) diff --git a/01_index.markdown b/01_index.markdown index 134a372..e40725e 100644 --- a/01_index.markdown +++ b/01_index.markdown @@ -24,23 +24,29 @@ background: home/bg.png

-

We build knowledge graph factories

+
+

We build knowledge graph factories

- Knowledge Graph Construction Architecture + Knowledge Graph Construction Architecture
-

We build property graphs in 3 steps

-
1) Transform myriad datasets into a common ontology. This means we Extract, Transform, Load (ETL) [or ELT] multiple, large and small datasets from different sources with different formats into a common property graph schema using tools like Python, PySpark, Databricks or Snowflake. How much ETL varies by industry from minimal with cybersecurity applications to simplified graph model with fewer makes it easy to access, query, analyze and model in a graph database such as Neo4j, TigerGraph, ArangoDB or Neptune.
+
+

We build property graphs in 3 steps

+
+
+ 1) Transform myriad datasets into a common ontology. This means we Extract, Transform, Load (ETL) [or ELT] multiple, large and small datasets from different sources with different formats into a common property graph schema using tools like Python, PySpark, Databricks or Snowflake. How much ETL varies by industry from minimal with cybersecurity applications to simplified graph model with fewer makes it easy to access, query, analyze and model in a graph database such as Neo4j, TigerGraph, ArangoDB or Neptune. +
+
- Raw Data in Bronze Tables + Raw Data in Bronze Tables
- Transformed, Cleaned Data in Silver Tables + Transformed, Cleaned Data in Silver Tables
@@ -54,13 +60,13 @@ background: home/bg.png
- Raw Data in Bronze Tables + Raw Data in Bronze Tables
- Raw Data in Bronze Tables + Raw Data in Bronze Tables
@@ -74,99 +80,75 @@ background: home/bg.png
- Raw Data in Bronze Tables + Raw Data in Bronze Tables
- Raw Data in Bronze Tables + Raw Data in Bronze Tables
- Raw Data in Bronze Tables + Raw Data in Bronze Tables
- Raw Data in Bronze Tables + Raw Data in Bronze Tables
-
+
-
-

What is a property graph, knowledge graph and triple store?

+
+

What is a property graph, knowledge graph and triple store?

A property graph is a set of objects representing nodes [also known as vertex/vertices] and edges [also known as links].
- Property Graphs vs Triple Stores: Reification, making objects out of properties + Property graphs vs RDF Triples. Both are knowledge graphs.
-

Why should I build a knowledge graph for my business?

+
+

Why should I build a knowledge graph for my business?

I'll let you in on a secret that is driving the popularity of enterprise knowledge graphs, property graphs, graph databases and Graph Neural Networks (GNNs): MOST DATA IS GRAPH DATA. To compose a single table to get the corresponding vectors, matrices and tensors we load into GPUs to drive machine learning algorithms, several tables have usually been combined [squashed] into one table. There's a problem with this... it is a lossy process. We threw away the relationships. Graph neural networks are able to learn better to build more powerful models because they have a greater potential by matching the structure of the data’s entities and their relationships.
-
- -
-

What’s the real story with Graph Neural Networks (GNNs)?

-
I'll let you in on a secret that is driving the popularity of enterprise knowledge graphs, property graphs, graph databases and Graph Neural Networks (GNNs): MOST DATA IS GRAPH DATA. To compose a single table to get the corresponding vectors, matrices and tensors we load into GPUs to drive machine learning algorithms, several tables have usually been combined [squashed] into one table. There's a problem with this... it is a lossy process. We threw away the relationships. Graph neural networks are able to learn better to build more powerful models because they have a greater potential by matching the structure of the data’s entities and their relationships. -
-
+
- Raw Data in Bronze Tables +
- -
-

How long is this course?

- The course is spread over five half-days. The order of topics is: -
-
  • -
      Day 0 - Before class, Platform Setup of the Docker, notebook Github software for the course.
    -
      Day 1 - Theory and use cases
    -
      Day 2 - Quantitative networks: social network analysis and network science
    -
      Day 3 - Building, refining and serving a knowledge graph from end-to-end
    -
      Day 4 - Graph machine learning from traditional graph ML to graph neural networks (GNNs)
    -
      Day 5 - Full-stack graph ML applications
    -
  • +
    +

    What’s the real story with Graph Neural Networks (GNNs)?

    +
    I'll let you in on a secret that is driving the popularity of enterprise knowledge graphs, property graphs, graph databases and Graph Neural Networks (GNNs): MOST DATA IS GRAPH DATA. To compose a single table to get the corresponding vectors, matrices and tensors we load into GPUs to drive machine learning algorithms, several tables have usually been combined [squashed] into one table. There's a problem with this... it is a lossy process. We threw away the relationships. Graph neural networks are able to learn better to build more powerful models because they have a greater potential by matching the structure of the data’s entities and their relationships.
    +
    +
    +
    + Property Graphs vs Triple Stores: Reification, making objects out of properties +
    +
    -
    Students begin Day 0 setup before the course and we debug by email. At the start of Day 1 we will verify everything works for all students.
    -
    -

    Who should attend this course?

    - This course is for data scientists and machine learning engineers who want to extend their work with data tables and DataFrames to datasets with a relational or graph structure - entities (nodes/vertices) and their connections (edges/links). -
    -
    -
    -
    GNN Problem Types -
    -
    -
    -
    - Beginning with a grounding in theory and real-world use cases, we will build knowledge graphs using natural language processing (NLP), analyze networks using graph analytics and extract the full potential of relational structures in data by automating business procsses using graph machine learning including graph kernels, embeddings and graph neural networks (GNNs). When the course is completed, you will be able to incorporate graph machine learning in your daily work to build more powerful models and systems. -
    -->
    -
    -

    Principal Consultant

    - 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. - I am a startup product and engineering executive focused on building products driven by billion node+ networks. I have worked at cool places like Ning, LinkedIn and Hortonworks. I co-founded Deep Discovery to use networks, GNNs and visualizations to build an explainable risk score for KYC / AML. - - I am a four-time O'Reilly author with 120 citations on Google Scholar for being the first to write about “agile data science” - agile development as applied to data science and machine learning. I am an applied researcher and product manager with 17 years of experience building and shipping data-driven products. - I am currently fascinated by knowledge graph / property graph construction, graph representation learning, graph neural networks (GNNs), NLP/NLU techniques such as information extraction, named entity resolution (NER), coreference resolution, fact extraction, and entity linking. I do network science and machine learning - so I get stuff done :) - Check out my network science portfolio, my blog and my O’Reilly Radar posts. -
    -
    \ No newline at end of file From ee9165aa413c1f1add35b293d9840237170cc890 Mon Sep 17 00:00:00 2001 From: Russell Jurney Date: Tue, 7 Mar 2023 12:15:54 -0800 Subject: [PATCH 4/9] Set twitter and github usernames --- _config.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/_config.yml b/_config.yml index 594f0e8..196d4f0 100644 --- a/_config.yml +++ b/_config.yml @@ -25,8 +25,8 @@ description: >- # this means to ignore newlines until "baseurl:" out of myriad sources of structured and unstructured data using big data tools. baseurl: "" # the subpath of your site, e.g. /blog url: "" # the base hostname & protocol for your site, e.g. http://example.com -twitter_username: jekyllrb -github_username: jekyll +twitter_username: rjurney +github_username: rjurney phone: 570-758-5858 # Build settings From cebb20dcd806732cab5c13d3731a2260a080b6c8 Mon Sep 17 00:00:00 2001 From: Russell Jurney Date: Tue, 7 Mar 2023 12:23:27 -0800 Subject: [PATCH 5/9] Working on opening text at top --- 01_index.markdown | 38 ++++++++++++++++++++++++++------------ 1 file changed, 26 insertions(+), 12 deletions(-) diff --git a/01_index.markdown b/01_index.markdown index e40725e..3e31544 100644 --- a/01_index.markdown +++ b/01_index.markdown @@ -13,12 +13,23 @@ background: home/bg.png ---
    +

    Knowledge Graph Construction

    - Graphlet AI is a data engineering, data science and artificial intelligence consultancy specializing in knowledge graph construction, also known as property graph construction. We transform and refine raw data on your data lake to build large networks ranging in the millions, billions or even trillions of nodes and edges that model entire business domains to solve complex problems with global footprints. We use big data tools and go beyond simple ETL by using machine learning and artificial intelligence to construct a graph model of your business domain that maps closely to solutions to your business problems. Using a modern graph database, your data science and machine learning teams can then efficiently mine this refined graph to find solutions to your most pressing data science problems. +
    + Graphlet AI is a data engineering, data science and artificial intelligence consultancy specializing in knowledge graph construction, also known as property graph construction. We build data pipelines that take raw data and feed your graph database clean data. +
    +
    +
    + We transform and refine raw data on your data lake to build large networks ranging in the millions, billions or even trillions of nodes and edges that model entire business domains to solve complex problems with global footprints. +
    +
    +
    + We love big data and large networks. We use big data tools to scale data pipelines that go beyond traditional ETL and entity resolution using artificial intelligenecs - graph machine learning - to construct a high fidelity network model of your business domain that maps directly to solutions to your business problems. It lets you run the queries that answer problems vexing you and driving features your customers demand. Using a modern graph database, your data science and machine learning teams can then efficiently mine this refined graph to find solutions to your most pressing data science problems. +
    @@ -28,7 +39,7 @@ background: home/bg.png

    We build knowledge graph factories

    - Knowledge Graph Construction Architecture + Knowledge Graph Construction Architecture
    @@ -40,13 +51,13 @@ background: home/bg.png
    - Raw Data in Bronze Tables + Raw Data in Bronze Tables
    - Transformed, Cleaned Data in Silver Tables + Transformed, Cleaned Data in Silver Tables
    @@ -60,13 +71,13 @@ background: home/bg.png
    - Raw Data in Bronze Tables + Raw Data in Bronze Tables
    - Raw Data in Bronze Tables + Raw Data in Bronze Tables
    @@ -80,25 +91,25 @@ background: home/bg.png
    - Raw Data in Bronze Tables + Raw Data in Bronze Tables
    - Raw Data in Bronze Tables + Raw Data in Bronze Tables
    - Raw Data in Bronze Tables + Raw Data in Bronze Tables
    - Raw Data in Bronze Tables + Raw Data in Bronze Tables
    @@ -115,7 +126,7 @@ background: home/bg.png
    - Property graphs vs RDF Triples. Both are knowledge graphs. + Property graphs vs RDF Triples. Both are knowledge graphs.
    @@ -149,6 +160,7 @@ background: home/bg.png
    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:
    +
  • @@ -163,13 +175,15 @@ background: home/bg.png
  • +

    Principal Consultant

    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. I am a startup product and engineering executive focused on building products driven by billion node+ networks. I have worked at cool places like Ning, LinkedIn and Hortonworks. I co-founded Deep Discovery to use networks, GNNs and visualizations to build an explainable risk score for KYC / AML.
    +
    - I am a four-time O'Reilly author with 120 citations on Google Scholar for being the first to write about “agile data science” - agile development as applied to data science and machine learning. I am an applied researcher and product manager with 17 years of experience building and shipping data-driven products. + I am a four-time O'Reilly author with 122 citations on Google Scholar for being the first to write about “agile data science” - agile development as applied to data science and machine learning. I am an applied researcher and product manager with 17 years of experience building and shipping data-driven products.
    I am currently fascinated by knowledge graph / property graph construction, graph representation learning, graph neural networks (GNNs), NLP/NLU techniques such as information extraction, named entity resolution (NER), coreference resolution, fact extraction, and entity linking. I do network science and machine learning - so I get stuff done :) From 5ebbe7866f211db2c0d025723d975d9a51d99118 Mon Sep 17 00:00:00 2001 From: Russell Jurney Date: Tue, 7 Mar 2023 12:39:09 -0800 Subject: [PATCH 6/9] Updated system architecture diagram --- ...KG-Factory-System-Architecture-Diagram.jpg | Bin 299937 -> 244328 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/assets/slides/KG-Factory-System-Architecture-Diagram.jpg b/assets/slides/KG-Factory-System-Architecture-Diagram.jpg index 30fc020b6d8f82e8bd23089332b71365eaa175d1..7f2dba6536573b568808082c072e6b9e39ce2a08 100644 GIT binary patch literal 244328 zcmdqK1zZ$c{|7uv2o^|*K}k0P(h^EZODrWJC0$Fmfgm6tt&)OB35e3EA|fClAR#H; zCDH=#%0Tw!bH_wQO(HR(n!FVT2zGU zqN|{*wVkzzqXCtxwUv#7psO%7lDQz*hE8)(Qz3^qS_)HNf}~PuDqN)!w?&vx@v(EU z8J$0Wo{CR^ozsw)$AC}3@B%9p*Lf~3j`O@6oZM_&T!QC0!9OZ=p#}vajBg04NJyc@ zfuDq_(RR7GxUjo$v)dv}IXDFb1USxfad2_5fg{))+-w{TT-j_KXm&YBm^c_A%n5T*uMu$!A33vx*bTo&NDAb$D61ujlbNr_9Bxh_la@n4dX5WmcON%H(I 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14:11:42 -0800 Subject: [PATCH 7/9] More cleanup --- 01_index.markdown | 31 +++++++++++++++---------------- 1 file changed, 15 insertions(+), 16 deletions(-) diff --git a/01_index.markdown b/01_index.markdown index 3e31544..a288014 100644 --- a/01_index.markdown +++ b/01_index.markdown @@ -86,7 +86,7 @@ background: home/bg.png

    - Manual blocking and matching for numerous datasets is a cumbrsome and expensive activity. Advances in AI - representation learning and an architecture from Google called Grale - make a generic entity resolution (ER) system possible. This system is configurable to work across multiple datasets by embedding records using large language models (LLMs) such as GPT-3 or ChatGPT, but tuned specifically for the entity matching task. + Manual blocking and matching for numerous datasets is a cumbersome and expensive activity. Advances in AI - representation learning and an architecture from Google called Grale - make a generic entity resolution (ER) system possible. This system is configurable to work across multiple datasets by embedding records using large language models (LLMs) such as GPT-3 or ChatGPT, but tuned specifically for the entity matching task.
    @@ -118,6 +118,18 @@ background: home/bg.png
    +
    +

    Can a graph model my data?

    +
    + Property graphs can represent data from any database you throw at them. +
    +
    +
    +
    + Simple vs property graphs +
    +
    +

    What is a property graph, knowledge graph and triple store?

    @@ -132,28 +144,15 @@ background: home/bg.png

    Why should I build a knowledge graph for my business?

    - I'll let you in on a secret that is driving the popularity of enterprise knowledge graphs, property graphs, graph databases and Graph Neural Networks (GNNs): MOST DATA IS GRAPH DATA. To compose a single table to get the corresponding vectors, matrices and tensors we load into GPUs to drive machine learning algorithms, several tables have usually been combined [squashed] into one table. There's a problem with this... it is a lossy process. We threw away the relationships. Graph neural networks are able to learn better to build more powerful models because they have a greater potential by matching the structure of the data’s entities and their relationships. + I'll let you in on a secret that is driving the popularity of enterprise knowledge graphs, property graphs, graph databases and Graph Neural Networks (GNNs): MOST DATA IS GRAPH DATA. To compose a single table to get the corresponding vectors, matrices and tensors we load into GPUs to drive machine learning algorithms, several tables have usually been combined [squashed] into one table. There's a problem with this... it is a lossy process. We threw away the relationships. Knowledge graphs modeled using graph machine learning (Graph ML) and graph neural networks (GNNs) are able to learn better to build more powerful models because they have a greater potential by matching the structure of the data’s entities and their relationships.
    - + Networks vs sequence representations. Networks preserve inherent relational bias of data
    - -
    -
    -

    What’s the real story with Graph Neural Networks (GNNs)?

    -
    I'll let you in on a secret that is driving the popularity of enterprise knowledge graphs, property graphs, graph databases and Graph Neural Networks (GNNs): MOST DATA IS GRAPH DATA. To compose a single table to get the corresponding vectors, matrices and tensors we load into GPUs to drive machine learning algorithms, several tables have usually been combined [squashed] into one table. There's a problem with this... it is a lossy process. We threw away the relationships. Graph neural networks are able to learn better to build more powerful models because they have a greater potential by matching the structure of the data’s entities and their relationships. -
    -
    -
    -
    - Property Graphs vs Triple Stores: Reification, making objects out of properties -
    -
    -

    I use a certain tool or platform. Can you help me?

    From 212f2d17649f8147bcf87df2375aa2bbab48e4db Mon Sep 17 00:00:00 2001 From: Russell Jurney Date: Tue, 7 Mar 2023 14:28:37 -0800 Subject: [PATCH 8/9] Wrapping up initial changes. Added headshot of Russell Jurney and brief description :) --- 01_index.markdown | 7 ++++--- assets/home/russell_jurney_headshot.jpg | Bin 0 -> 126347 bytes assets/home/russell_jurney_headshot.png | Bin 0 -> 1219460 bytes 3 files changed, 4 insertions(+), 3 deletions(-) create mode 100644 assets/home/russell_jurney_headshot.jpg create mode 100644 assets/home/russell_jurney_headshot.png diff --git a/01_index.markdown b/01_index.markdown index a288014..d416c7d 100644 --- a/01_index.markdown +++ b/01_index.markdown @@ -18,7 +18,7 @@ background: home/bg.png
    -
    +
    -

    Principal Consultant

    +

    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. I am a startup product and engineering executive focused on building products driven by billion node+ networks. I have worked at cool places like Ning, LinkedIn and Hortonworks. I co-founded Deep Discovery to use networks, GNNs and visualizations to build an explainable risk score for KYC / AML. @@ -184,6 +184,7 @@ background: home/bg.png
    I am a four-time O'Reilly author with 122 citations on Google Scholar for being the first to write about “agile data science” - agile development as applied to data science and machine learning. I am an applied researcher and product manager with 17 years of experience building and shipping data-driven products.
    +
    I am currently fascinated by knowledge graph / property graph construction, graph representation learning, graph neural networks (GNNs), NLP/NLU techniques such as information extraction, named entity resolution (NER), coreference resolution, fact extraction, and entity linking. I do network science and machine learning - so I get stuff done :) Check out my network science portfolio, my blog and my O’Reilly Radar posts. diff --git a/assets/home/russell_jurney_headshot.jpg b/assets/home/russell_jurney_headshot.jpg new file mode 100644 index 0000000000000000000000000000000000000000..2fee9e8204e9e253c70cc5587aa7742659e9393e GIT binary patch literal 126347 zcmbTe2V4_N*D#(C0wEMBp#@O7^b$H)AOg~Rkx-F3O>-yeTp1##&_czb|A`uY+e80eql z`#y+8JJiiL5(EZO1Jy@Cpzo^?S+9_gAaz;U0KANgd!Q>;#x2laHo_%HR$fL<7Iflt zM39S{FE)hV73=98a8mGP_kBTrZ}*dewo1lw#zAMWUfz09IILxqiIrQFubY~?;OSHR zCnD4%{Db_lAujw8{(b>?^@x*#l)=>jm|QI@$WLh!;(Jn1i~LmnbH--;X996peia#% zw40oq9KVX1jJ&I|l8cI(tD+P?N)CmRl~b0LSCB@b)aB%X!vEtC1RjWU_fWUc*8TA~ z;OeB{kM{}>50?p7kO{a% ztOXVy7>aYl0?mcUorBcR;IJ+sfjFzcK);`eH1i4!3B-E^22oJPZ|vgc9YC(2bTBqn z*AKvlxCFRi^|en50z=4nd%LTnbk)#mN=kBQMMadnypFaO8im$YQPtAbK7&@)(vkbw zS3A%x)E^rV^0Tiy&{suHO-n&rO%<)Er2W74C9{Kn5b$JeEY3R&>#mCn^yjC%v%2?x 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