#Knowledgegraphs are important resources for many #artificialintelligence tasks but often suffer from incompleteness. #research proposes to use pre-trained language models for knowledge graph completion. Novel framework named KG-BERT
KGC 2020 Agenda is now live – KGC Newsletter Issue 08
Hi everyone, The Knowledge Graph Conference 2020 agenda is now live! Check it out. May 4-5 Workshops & Tutorials 2 workshops and 7 tutorials on building the state of the art KGs Schedule May 6-7 Main Conference 30 speakers of KG pioneers and leaders sharing vision for KG innovations Schedule KGC 2020 Tickets Join over 130 […]
Bringing #graphdatabases technology and advanced analytics together. We at Redfield are proud to announce a new integration between #OrientDB graph database...
kNN Classification members of congress using similarity algorithms in Neo4j | Graph people
Image taken from wikipedia, Couple of days ago I was presenting “How to use similarity algorithms” in a Neo4j online meetup with Mark Needham. Among other use-cases we discussed how the…
Oct 25, 2019 · 11 min readThere is an unreasonable amount of information that can be extracted from what people publicly say on the internet. At Heuritech we use this information to better understand what people want, which products they like and why. This post explains from a scientific point of view what is Knowledge extraction and details a few recent methods on how to do it.What is knowledge extraction?Highly structured databases make it easy to reason with and can be used for inference. For example in WikiData or YAGO, entities are isolated and linked together with relations. However, most of the human knowledge expressions take the form of unstructured texts, from which it is very hard to reason and get wisdom. Consider the example here:The raw text on the left contains a lot of useful information in an unstructured way, such as birthday, nationality, activity. Extracting those information corresponds to a challenging field in Natural Language Processing, which may require
Prototyping a simple knowledge graph application with JSONs, MongoDB and automatically generated GraphQL APIThis post is a result of joint work with Anna Konieczna and Artur Haczek.Source: Noble ConnectionsSimple knowledge graph applications can be easily built using JSON data managed entirely via a GraphQL layer. We describe a quick recipe for prototyping one such demo, Noble…
Knowledge graph applications in the enterprise gain steam
Knowledge graph applications can be technically challenging and time-consuming to build, but enterprises are starting to find that the work can lead to positive returns.
There are only a handful of publicly available knowledge graphs. And among those, only a few provide data with enough breadth to in some way represent the entire internet, and with enough granulari…
Knowledge Graph Metadata: What Facebook really knows about you?
If you’re one of the holdouts who still has a Facebook account you’ll be happy to know that Big Zuck now lets you control — and delete —…Continue reading on Medium »
Knowledge Graph Semantic Enhancement of Input Data for Improving AI
world factual information that can augment the limited labeled data to train a machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for many practical applications, it is convenient and useful to organize this background knowledge in the form of a graph. Recent academic research and implemented industrial intelligent systems have shown promising performance for machine learning algorithms that combine training data with a knowledge graph. In this article, we discuss the use of relevant KGs to enhance the input data for two applications that use machine learning—recommendation and community detection. The KG improves both accuracy and explainability.Machine learning algorithms trained with a large labeled data have shown promising performance in solving pr
Knowledge Graphs & NLP @ EMNLP 2019 Part I - Michael Galkin - Medium
#KnowledgeGraphs can be applied to a wide variety of #NLP tasks: question answering, dialogue systems etc. @michael_galkin summarizes EMNLP 2019 #research: Augmented Language Models, Dialogue Systems & Conversational #AI, KGs from Text, KG Embeddings
Knowledge Graphs & NLP @ EMNLP 2019 Part II - Michael Galkin - Medium
Part 2 of @michael_galkin review of #knowledgegraph related papers from EMNLP 2019: Question Answering over KG, NLG from KGs, Commonsense reasoning with KGs, and some old school Named Entity/Relation Recognition & Linking #research #AI #EmergingTech #data
This is the first in a short series introducing Knowledge Graphs. It covers just the basics, showing how to write, store, query and work with graph data using RDF (short for Resource Description Fo…
Knowledge Graphs 4 – Querying your knowledge graph using .NET – Andrew Matthews
This installment leaves the CLI behind to show how we consume a knowledge graph within our programmatic environments. The framework I use to work with RDF is dotNetRdf.
Knowledge Graphs and AI in the Pharma Sector-PoolParty Semantic Suite
The pharmaceutical and healthcare industry needs to break up departmental data silos with Knowledge Graphs and AI to understand the value of their data.
Knowledge Graphs and Causality - Vademecum of Practical Data Science
"We might conclude that we don’t need to represent knowledge in graphs explicitly anymore, but one of the hottest fields right now involves using deep learning to learn embedding representations of the base knowledge graph." h/t @aaranged @gm_spacagna [LINK]http://datasciencevademecum.com/2019/12/19/knowledge-graphs-and-causality/[\LINK] [IMAGE]https://datasciencevademecum.com/wp-content/uploads/2019/12/dark_1x01_-_string_wall_full-825x510.jpg[\IMAGE]
Knowledge Graphs and Knowledge Networks: The Story in Brief
at least since Vannevar Bush’s 1945 seminal piece when he discussed why people of science should then turn to the massive task of making more accessible our bewildering store of knowledge to implement machine processes (http://bit.ly/45VBush). Following the importance of conceptual models in data management and knowledge representation in Artificial Intelligence (AI) in the 1980s, the 1990s saw the emergence of the concept of ontology i
Knowledge Graphs and their central role in big data processing: Past,…
#KnowledgeGraphs and their central role in #bigdata processing: Past, Present, and Future #keynote #presentation #research #AI #datascience #data @amit_p
