Giuseppe Futia, PhD on LinkedIn: #NumPy #gat #graphs
Check out my new article for Towards Data Science: "Graph Attention Networks Under the Hood". A Step-by-step Guide From Math to #NumPy https://lnkd.in...
Interactive and Interpretable Machine Learning and Graph-Based Data Science
Sat, Jan 30, 2021, 11:30 AM: Hi Everyone,Agenda:11:30 am - 11:45 Introductions/Meet and greet11:45 - 12:45: Interactive Visualization for Interpretable and Interactive Machine Learning with Professor
Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot...
Understanding narratives requires reasoning about implicit world knowledge related to the causes, effects, and states of situations described in text. At the core of this challenge is how to...
Excited to share my new Chrome extension, Side Portal :) Browse Wikipedia spatially - in a graph view, and with a right sidebar like Roam Research!@Conaw @roamhacker @thepericulum @cortexfutura @visakanv @Mappletons @alycosta @andy_matuschak @azlenelzahttps://t.co/04VptEgcbA pic.twitter.com/shYgOqSOOd— Dharam Kapila (@DharamKapila) January 26, 2021
Deep Knowledge as the Key to Higher Machine Intelligence
Third in a series on Cognitive Computing Research – The Age of Knowledge Emerges By Gadi Singer, Intel Labs The next big thing - Cognitive AI To address a new level of higher machine intelligence that can truly ‘think’ in new situations, a categorical transition is needed to evolve AI beyond the
"Dimensions of Commonsense Knowledge" a survey of a wide range of popular commonsense sources with a classification of their relations.(Ilievski et al, 2021)https://t.co/7rCcH9glpG@BoschResearch @oltramale pic.twitter.com/ZENgmKm0Mt— WikiResearch (@WikiResearch) January 27, 2021
Mindblowing momentum behind Aura right now. 7 month after we pivoted the roadmap in the midst of COVID, we today announce General Availability of Aura Enterprise on GCP, and open up the EAP for AWS! Check out that list of customers & breadth of use cases! https://t.co/EQXqJNK6lv pic.twitter.com/VbozcQHdUi— Emil Eifrem (@emileifrem) January 27, 2021
Discuss: "We shouldn’t be fixated on the graph itself, but on enabling knowledge workflows. KG technologies allow the introduction of automation into information worker workflows, helping them save time on mundane information processing tasks." @mikektung https://t.co/nLwRPLOlly— Aaron Bradley (@aaranged) January 21, 2021
JSON-LD (endorsed by #schema.org Jun. '13) has finally almost overtaken microdata (recommended in the original #schema.org release of Jun. '11) https://t.co/7Isx6tX0Iv pic.twitter.com/xnfqc0oQ8N— Aaron Bradley (@aaranged) January 21, 2021
Bias in ontologies - a preliminary assessment / C. Maria Keet https://t.co/x1VaU9yX4R 2/2 pic.twitter.com/pdeyXdmFZs— Aaron Bradley (@aaranged) January 21, 2021
In this week's #twin4j, Bhavesh Pandey starts building a graph backed asset management systemhttps://t.co/tXfNUTky6K#Neo4j pic.twitter.com/e8CMyVAtHU— Neo4j (@neo4j) January 25, 2021
In this week's #twin4j, @BarrasaDV shows how to use a SKOS taxonomy for semantic search on a document repository https://t.co/hGIiPxclys #neo4j #n10s pic.twitter.com/lXSXYX1XQK— Neo4j (@neo4j) January 23, 2021
"A Review of the Semantic Web Field," by @PascalHitzler @KState, looks at the history, key concepts, #standards, and prominent outcomes of Semantic Web research https://t.co/riJ7rz9gGC. Hitzler discusses his work in an original video at https://t.co/B5jKfzrKIt. pic.twitter.com/JyqXetKLAq— Communications of the ACM (@CACMmag) January 26, 2021
I just wrote a blog about a 1 hour experiment I did today: using hugging face transformer QA model and SPARQL queries to DBPedia for a simple natural language interface: https://t.co/QjfvWHNTsv— mark_l_watson (@mark_l_watson) January 18, 2021
New #openaccess research from @jjkoehorst and colleagues: A protocol for adding knowledge to #Wikidata: aligning resources on human coronaviruses. #Covid19 Read it here: https://t.co/TWkHhRPlA1 pic.twitter.com/ayoSFs0ztd— BMC Biology (@BMCBiology) January 25, 2021
Language Models are Open Knowledge Graphs (Paper Explained)
#ai #research #nlp Knowledge Graphs are structured databases that capture real-world entities and their relations to each other. KGs are usually built by human experts, which costs considerable amounts of time and money. This paper hypothesizes that language models, which have increased their performance dramatically in the last few years, contain enough knowledge to use them to construct a knowledge graph from a given corpus, without any fine-tuning of the language model itself. The resulting system can uncover new, unknown relations and outperforms all baselines in automated KG constructi...
Philip Vollet on LinkedIn: #datascience #machinelearning #nlp
Language Models are Open Knowledge Graphs .. but are hard to mine! Dive into the latest research on creating knowledge graphs using transformer based language...
IPRally is building a knowledge graph-based search engine for patents
IPRally, a burgeoning startup out of Finland aiming to solve the patent-search problem, has raised €2 million in seed funding. Leading the round is JOIN Capital and Spintop Ventures, with participation from existing pre-seed backer Icebreaker VC. It brings the total raised by the 2018-founded company to €2.35 million. Co-founded by CEO Sakari Arvela, who […]
Manas Gaur on LinkedIn: ACM CoDS COMAD Tutorial - Explainable AI using KG
Happy #FirstMonday 2021 **** Tutorial Alert @ ACM CoDS-COMAD **** What a great experience to deliver a tutorial on "Explainable AI using Knowledge Graphs...