Knowledge Graph – A Powerful #DataScience Technique to Mine Information from Text. Learn how to build #knowledgegraph using text from Wikipedia pages, using #Python & popular spaCy #opensource library #softwaredevelopment #data #tutorial @AnalyticsVidhya

Given set of SHACL constraints & set of datalog rules, #research detects which constraints could be violated by application of inference rules on some graph instance of the schema, updates original schema to capture new facts #semantics #knowledgegraph http://arxiv.org/abs/1911.00598

Managing multiple truths in #datamodeling, #ontologies & #knowledgegraphs by @palexop #semantics #vagueness #datascience #dataengineering #EmergingTech #dmzone #presentation

Same Stats, Different Graphs: Exploring the Space of Graphs in Terms of Graph Properties. #Data analysts use statistics to summarize large datasets, but it can be misleading. #Research considers similar problem in graph mining #knowledgegraph #analytics http://arxiv.org/abs/1911.01527

The Great(er) Bear - using @Wikidata to generate better artwork. Wikipedia holds structured #data about people and things. It uses SPARQL to query that data. @edent writes something to query Wikidata to generate a more accurate artwork #dataviz #graphDB

Parts of #Google Search, including BERT, go #opensource. BERT helps Google better understand intent of some queries, nothing to do w page content. You can’t optimize for BERT. But BERT can be used for text summarization #SEO #semantics #AI @hamletbatista

#schema.org 5.0 is out. No additions to core, but vocabulary for classification of #healthcare topics for more real estate properties in development. New releases on 1st of month, changes in "Pending" section #SEO #semantics #Google h/t @danbri @aaranged

Reasoning over paths in large scale #knowledgegraphs has many applications. @healx #research on approach to automatically build & rank paths between source & target entity pair with learned embeddings using a knowledge base completion model #EmergingTech

Graphs #Analytics for Fraud Detection, using Graph #NeuralNetworks for Anomaly detection. GraphSAGE is Stanford #opensource project: deep neural network-based NRL toolkit, implemented in Tensorflow, making it ideal to develop an anomaly detection system

#semantic understanding of scenes is important for #apps. But in what space should diverse information be grounded, in what structure? 3D Scene Graph outputs a layered graph. Layers have nodes, nodes have attributes, edges between nodes h/t @drfeifei

#Semantics at Scale is made possible with the advent of tools like BERT, bert-as-service, support for dense vector manipulations in Elasticsearch. Degree may vary depending on use case, but search results can benefit from augmenting keyword based results

Look for semantic data systems to become more oriented around knowledge bases rather than inferential analysis, at least at first, while at the same time incorporating the latest advances in machine learning and auto-categorization.

The latest Amazon Neptune release brings together a host of capabilities that enhance developer productivity with graphs. This post summarizes the key features we have rolled out and pointers for more details. Getting started This new engine release will not be automatically applied to your existing cluster. You can choose to upgrade an existing cluster […]

Practical Gremlin, the guide to @apachetinkerpop #graphdatabase opensource framework by @gfxman, just went past the 1000 commit mark and has a new edition #database #datascience #analytics #tutorial

#Knowledgegraphs have proven to be effective for high-quality recommendation. This #research designs a novel attentive knowledge graph embedding (AKGE) framework for recommendation, which exploits both semantics & topology in interaction-specific manner

#Semantic Datatypes For Fun and Profit. How do you model identifiers? This seemingly simple question can reveal a surprising amount of complexity about the nature of identity & modeling, how we handle units in #datamodel #database #datascience @kurt_cagle

#Microsoft Project Cortex Builds A Custom #KnowledgeGraph for Your Company. Service brings #AI to the vast quantities of #data companies store within #Microsoft365. Unlocks knowledge stored about a topic/client/project inside of documents, surfaces them

How to query #Wikidata (or any other #LinkedData endppoint) from within #rstats? @kala_karu explains. Documented code here: https://t.co/L9b8CEJWNu #tutorial #datascience #knowledgegraph #data #tech h/t @linkedktk @statistik_zh

Organizations moving to graph-based intelligence need to look at the core commercial, operational, logistical questions they want to answer first, then build graph #datamodel to optimize for relationships in the business question #graphDB @DeniseKGosnell

.@NvidiaAI RAPIDS cuGraph #opensource library is on a mission to provide multi-GPU graph #analytics for billion/trillion scale graphs. Experimental results on release of a single-node multi-GPU version of PageRank: on average 80x faster than #ApacheSpark

More and more #opendata platforms are becoming linked #data. The ONS, @WikiData & Scottish Government all offer SPARQL endpoints to their #linkeddata. SPARQL acts as pattern-matching language @digidatajen @ODcamp #tutorial #knowledgegraph h/t @linkedktk

A case for response time focused query processing by @olafhartig at #ISWC19 Rethinking optimization criteria for Web querying. Optimizing Query execution time is not the same as optimizing Query Response time #research #www #linkeddata #keynote

This article summarizes recent #research on Emotion Recognition Using Graph #NeuralNetworks. DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation #AI #deeplearning #knowledgegraph #datascience #innovation #tech #data https://towardsdatascience.com/emotion-recognition-using-graph-convolutional-networks-9f22f04b244e?source=rss----7f60cf5620c9---4

"We propose constructing one #knowledgegraph per query & show this method compresses information and reduces redundancy" > Improving long-form question answering by compressing search results / Angela Fan @facebookai h/t @aaranged https://ai.facebook.com/blog/research-in-brief-training-ai-to-answer-questions-using-compressed-search-results/

Using #Semantic #SEO & Structured #Data to Future proof Your Search Strategy with @cyberandy h/t @TheodoraPetkova #knowledgegraph

An analysis of the unique problem @TAKEALOT faced in facilitating reliable deliveries to customers and how they use a #graphdatabase to deliver a performant and scalable solution, by @filipe_ppt #retail #business #innovation #data https://dev.to/fppt/managing-delivery-networks-a-use-case-for-graph-databases-2jb0

Property graphs provide a lot of flexibility in data modeling; but how do you know when to use which feature?

In this blog post, we’re going to walk through designing a graph processing algorithm on top of Neo4j that discovers the influence and sentiment of tweets in your Twitter network.

Why is it so hard to standardize a Graph Query Language ? It is because graph databases are strongly dependent on the data model and the physical layer... 37 comments on LinkedIn