A knowledge graph (KG) is a data structure which represents entities and relations as the vertices and edges of a directed graph with edge types. KGs are an important primitive in modern machine...

Ein Knowledge Graph als moderne Form der Datenarchitektur. Gäste werden künftig zunehmend auch sprachbasiert nach Informationen suchen. Die Sprachanfragen werden also mittels Diktierfunktion auf dem Smartphone eingesprochen oder in Form von Fragen an digitale Assistenten wie den Google Assistant oder Alexa von Amazon gerichtet. Bei der

Memgraph, the streaming graph application platform, today announced Memgraph 2.0, the public launch of its source-available platform, which makes it easy

Leveraging more data sources using machine learning on graphs to enhance item suggestions for Decathlon members

🤖 A Python library for learning and evaluating knowledge graph embeddings - GitHub - pykeen/pykeen: 🤖 A Python library for learning and evaluating knowledge graph embeddings

Not all information needs to be seen at first sight. Child drawings allow you to expand a parent node and see more elements. See the #graph at the level...

Zero-shot Visual Question Answering using Knowledge Graph https://t.co/R9VNiLFdZE pic.twitter.com/8TbY3lkgGu— Aaron Bradley (@aaranged) July 13, 2021

Learn about a Google patent that enables searchers to explore connections between related entities in different types of media.

Geometric Deep Learning on Molecular Representations Atz et al.: https://lnkd.in/dEeydSb #ChemicalPhysics #ArtificialIntelligence #Biomolecules...

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The success of neural network embeddings has entailed a renewed interest in using knowledge graphs for a wide variety of machine learning and information retrieval tasks. In particular, recent...

Our e-book aims to shed light on what we believe is a real game-changer for those looking to improve upon simplistic answers sometimes arrived at by using traditional ML algorithms and approaches. We show how you are able to combine the power of both graphs and ML (in a variety of different ways) to help you arrive at better answers compared to using standard ML approaches alone.

Iconology is a branch of art history that investigates the meaning of artworks in relation to their social and cultural background. Nowadays, several interdisciplinary research fields leverage...

Graph Neural Networks for Natural Language Processing: A Survey Paper https://lnkd.in/gefRmQi Deep learning has become the dominant approach in coping...

Google introduces the #OpenSource Insights Project Exploratory #visualization provides an interactive view of #OSS projects dependencies A full dependency graph is built/published, incorporating metadata, so you can see how it may affect your #software

[vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” overlay_strength=”0.3″ shape_divider_position=”bottom”][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” width=”1/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text]What Is Ethical AI? Ethical, or responsible, artificial intelligence (AI), “is the...

Taxonomies are crucial for businesses and institutions to handle bigger amounts of data. Manually organizing thousands of concepts into a knowledge tree has so far been the only way to do this. Aside

Causaly, the London-based company that allows researchers and specialists to intuitively map and navigate the intricate landscape of biomedical research, has raised $17 million from investors to grow its team and expand into new markets.

We present Arianna+, a framework to design networks of ontologies for representing knowledge enabling smart homes to perform human activity recognition online. In the network, nodes are ontologies...

Graph clustering aiming to obtain a partition of data using the graph information, has received considerable attention in recent years. However, noisy edges and nodes in the graph may make the...

Over the recent years, Graph Neural Networks have become increasingly popular in network analytic and beyond. With that, their architecture noticeable diverges from the classical multi-layered...

How simple structured data trumps clever machine learning

On the role of knowledge that is structured, explicit and intelligible in providing a path toward higher machine intelligence

"The UI allows individuals with no previous knowledge of the Semantic Web to query the DBpedia knowledge base...." > Interface to Query and Visualise Definitions from a Knowledge Base @anelia12430996 & Hélène De Ribaupierre https://t.co/QGSJSEq4Ab pic.twitter.com/EIhZRVikK0— Aaron Bradley (@aaranged) March 15, 2021

NetVec: A Scalable Hypergraph Embedding System https://t.co/CHaT76MA7T pic.twitter.com/ZVChTtb0zH— Aaron Bradley (@aaranged) March 18, 2021

Nice machine learning read of the week: E(n) Equivariant Graph Neural Networks. The paper: https://lnkd.in/dfZ3Gh4 Source code: https://lnkd.in/dNfzQRj...

Knowledge about entities and their interrelations is a crucial factor of success for tasks like question answering or text summarization. Publicly available knowledge graphs like Wikidata or...