GraphNews

1. Knowledge Graph

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 »

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

Intelligent systems designed using machine learning algorithms require a

n

(Submitted on 4 Mar 2020) Abstract: In this paper we provide a comprehensive introduction to knowledge graphs,

#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

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…

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.

The pharmaceutical and healthcare industry needs to break up departmental data silos with Knowledge Graphs and AI to understand the value of their data.

"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]

My friend Arun Batchu and I have had several spirited discussions about a recent article in the July 2019 Scientific American about…

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

#KnowledgeGraphs and Knowledge Networks: The Story in Brief #research #datascience #analytics #AI @amit_p h/t @WikiResearch @cyberandy

A powerful combination for the semi-automatic generation of insights

#KnowledgeGraphs and their central role in #bigdata processing: Past, Present, and Future #keynote #presentation #research #AI #datascience #data @amit_p

#Knowledgegraphs applied in #retail. Semantic searches are common: “sassy dress” / “dress for dancing” will occur more than a search for a specific product name. KG based on product attributes/semantic entities can be used to associate words #EmergingTech

Representation of #innovation ecosystems as #knowledgegraphs would enable generation of reports with new insights, execution of advanced #data analysis tasks. An #ontology to capture essential entities & relations, data sources #research #EmergingTech

Direct online marketing and sales are nowadays an essential part of almost any business that addresses an end consumer, such as in tourism. On the downside, the data and content required for such marketing and sales are typically distributed, and difficult to identify and use, especially for small and medium enterprises. Further, a combination of content management and semantics for automated online marketing and sales is becoming practically feasible now, especially with the global adoption of knowledge graphs. A design and feasibility pilot of a solution implementing semantic content and ...

Enterprises should consider semantic and Enterprise Knowledge Graph technology for their Data Governance efforts. Enterprise Knowledge Graphs describe a linked set of information that meaningfully brings together data and metadata silos.

sourced Ampligraph, a suite of machine learning models that can uncover new knowledge from existing knowledge graphs. We’re also exploring how knowledge graphs can contribute to social good. Working with Lambert Hogenhout, Chief of Data Analytics for the United Nations, we organized a “Knowledge Graphs for Social Good” workshop. The effort focused on the UN’s 17 Sustainability Development Goals: concrete areas like poverty, hunger, and health where action is needed to impr

intelligence projects in military and security forces, Quid and RecordedFuture enable competitive analytics, Bottlenose and others enable online reputation analytics, Microsoft Graph enables new kinds of productivity apps for the

Hello, ACL 2019 has just finished and I attended the whole week of the conference talks, tutorials, and workshops in beautiful Florence…

This post commemorates the first anniversary of the series where we examine advancements in NLP and Graph ML powered by knowledge graphs! 🎂 1️⃣

19, trying to make sense of the data surrounding the virus is a Herculean task. Vast in volume and ceaselessly produced, this data emanates from domains as different as virology and economics and is produced by a multitude of people and organizations. Unsurprisingly the standards to which this data conforms are as multitudinous as its sources. It just so happens that making sense of messy data from disparate sources is one of the things at which knowledge graphs excel. Moreover, knowledge graphs make it possible to derive new knowledge from intelligently connecting information residing in those disparate data repositories. Given that the ability to better analyze data and gain new insights is of obvious use to people trying to respond to the pandemic, those working with knowledge graph technologies have started to talk about how those technologies – and their skills – might

We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades. Over the last few years, a number of new graph databases

This past fall, all aspects of the computable knowledge structure KBpedia – its upper ontology (KKO), full knowledge graph, mappings to major leading knowledge bases, and 70 logical concept groupings called typologies – became open source.

pandemic) and have our LinkedIn feeds (trained by AI, ironically) locked into the data management world, these trends are