Boole is your local analytics consultant. Based in Auckland, New Zealand, Boole offers web analytics services including Google Analytics tracking installation, data reporting, and optimisation. Boole's services range from auditing to consulting and teaching. A digital analytics expert, Boole provides analytics debugging and troubleshooting services to help you fix your existing Google Analytics setup. Boole works with a range of industry leading tools such as Google Analytics, Google Optimise, Google Data Studio, Google Tag Manager, Hotjar, Facebook Pixel, and others to help you get the most of your marketing.

What is Data Commons (datacommons.org)? Read Hesper's introduction to Data Commons (datacommons.org) to learn more about the open knowledge graph built from thousands of public datasets.

Learn more about how Bayer uses semantic web technologies for corporate asset management and why it enables the FAIR data in the corporate environment.

An introduction to WikiPathways by Tett Bioinformatics is an overview of the collaboratively edited structured biological pathway database that discusses the history of the project, applications of the open dataset, and ways to access the data programmatically.

Hesper's article about question answering explains how question answering helps extract information from unstructured data and why it will become a go-to NLP technology for the enterprise.

Read more about how document understanding AI works, what its industry use cases are, and which cloud providers offer this technology as a service.

Lexemes are Wikidata's new type of entity used for storing lexicographical information. The article explains the structure of Wikidata lexemes and ways to access the data, and discusses the applications of the linked lexicographical dataset.

Boole is a marketing consultant in Auckland, New Zealand. Boole helps you optimise your website content, product, and services through A/B testing, personalisation, and product recommendations supported by accurate and timely measurement of your key business metrics such as web conversion rates.

Tett is a bioinformatics consultant in Auckland, New Zealand. Tett Bioinformatics offers bioinformatics services including genomics and biomedical data analysis and discovery.

The guide to exploring linked COVID-19 datasets describes the existing RDF data sources and ways to query them using SPARQL. Such linked data sources are easy to interrogate and augment with external data, enabling more comprehensive analysis of the pandemic both in New Zealand and internationally.

The introduction to the Gene Ontology graph published by Tett outlines the structure of the GO RDF model and shows how the GO graph can be queried using SPARQL.

Hesper is a knowledge management and data integration consultant in Auckland, New Zealand. Hesper's insights into state-of-the-art data, information, and knowledge management enable it to help organisations reassess their data analysis, integration, and enrichment approaches in light of advanced semantic technologies that are evolving every day. Enterprise knowledge graphs, knowledge bases, ontologies, and taxonomies are emerging technologies that support better decision-making and knowledge integration and enable automated knowledge inference over internal and external data.

The overview of the Nobel Prize dataset published by Hesper demonstrates the power of Linked Data and demonstrates how linked datasets can be queried using SPARQL. Use SPARQL federation to combine the Nobel Prize dataset with DBPedia.

Learn why federated queries are an incredibly useful feature of SPARQL.

As digital products and services are becoming more and more complex, so are the technical requirements for correctly implementing user measurement and analytics. Boole helps you better understand your audience by setting up measurement using Google Analytics.

What are the best online Arabic dictionaries?

How to pronounce numbers in Arabic?

List of months in Maori.

Days of the week in Maori.

The list of country names in Tongan.

The list of IPA symbols.

What are the named entities?

What is computational linguistics?

Learn how to use the built-in React hooks.

Learn how to use language codes in HTML.

Learn about SSML.

Browse the list of useful UX resources from Google.

Where to find the emoji SVG sources?.

What is Wikidata?

What's the correct markup for multilingual websites?

How to use custom JSX/HTML attributes in TypeScript?

Learn more about event-driven architecture.

Where to find the list of all emojis?

How to embed YouTube into Markdown?

What is the Google Knowledge Graph?

Learn SPARQL.

Explore the list of coronavirus (COVID-19) resources for bioinformaticians and data science researchers.

Sequence logos visualize protein and nucleic acid motifs and patterns identified through multiple sequence alignment. They are commonly used widely to represent transcription factor binding sites and other conserved DNA and RNA sequences. Protein sequence logos are also useful for illustrating various biological properties of proteins. Create a sequence logo with Sequence Logo. Paste your multiple sequence alignment and the sequence logo is generated automatically. Use the sequence logo maker to easily create vector sequence logo graphs. Please refer to the Sequence Logo manual for the sequence logo parameters and configuration. Sequence Logo supports multiple color schemes and download formats.

Sequence Logo is a web-based sequence logo generator. Sequence Logo generates sequence logo diagrams for proteins and nucleic acids. Sequence logos represent patterns found within multiple sequence alignments. They consist of stacks of letters, each representing a position in the sequence alignment. Sequence Logo analyzes the sequence data inside the user's web browser and does not store or transmit the alignment data via servers.

Te Reo Maps is an online interactive Maori mapping service. All labels in Te Reo Maps are in Maori, making it the first interactive Maori map. Te Reo Maps is the world map, with all countries and territories translated into Maori. Please refer to the list of countries in Maori for the Maori translations of country names. The list includes all UN members and sovereign territories.

Phonetically is a web-based text-to-IPA transformer. Phonetically uses machine learning to predict the pronunciation of English words and transcribes them using IPA.

Punycode.org is a tool for converting Unicode-based internationalized domain names to ASCII-based Punycode encodings. Use punycode.org to quickly convert Unicode to Punycode and vice versa. Internationalized domains names are a new web standard that allows using non-ASCII characters in web domain names.

Bioinformatically is an online journal about everything bioinformatics. It includes industry news, research highlights, and a variety of editorials. Bioinformatically helps you start your day with everything you need to know and a dash of fun.

My Sequences is an online platform for storing and analyzing personal sequence data. My Sequences allows you to upload your genome sequences and discover insights and patterns in your own DNA.

Словообразовательный словарь «Морфема» дает представление о морфемной структуре слов русского языка и слов современной лексики. Для словообразовательного анализа представлены наиболее употребительные слова современного русского языка, их производные и словоформы. Словарь предназначен школьникам, студентам и преподавателям. Статья разбора слова «сладкоежка» по составу показывает, что это слово имеет два корня, соединительную гласную, суффикс и окончание. На странице также приведены слова, содержащие те же морфемы. Словарь «Морфема» включает в себя не только те слова, состав которых анализируется в процессе изучения предмета, но и множество других слов современного русского языка. Словарь адресован всем, кто хочет лучше понять структуру русского языка.

Разбор слова "кормушка" по составу.

Разбор слова "светить" по составу.

Разбор слова "сбоку" по составу.

Разбор слова "шиповник" по составу.

Разбор слова "народ" по составу.

Разбор слова "впервые" по составу.

Разбор слова "свежесть" по составу.

Разбор слова "издалека" по составу.

Разбор слова "лесной" по составу.

Hesper
Insights
Topics
Contact

A beginner's guide to graph embeddings

Understanding what graph embeddings are and why they are important for graph analytics.

Graph data underpins a broad array of applications in industries ranging from transportation and telecom to banking and healthcare. As graphs are becoming more and more pervasive, many organisations seek to leverage graph analytics and machine learning to derive insights from their graph data.

Instead of working with the graph data directly, many graph analytics implementations use graph embeddings—compressed representations of the graphs. Such representations enable a range of graph machine learning applications which include link prediction, similarity search, node classification, clustering, and community and anomaly detection.

So what are graph embeddings, exactly?

Embedding is a common technique used in machine learning to represent complex discrete items like English words or nodes of a graph as vectors which encode the information contained in the data while greatly reducing its dimensionality.

More specifically, graph embedding is the task of creating vector representations for each node in a graph so that distances between these vectors predict the occurrence of edges in the graph. Intuitively, the generated graph embeddings act as "compressed" representations of the nodes in the graph, i.e. feature vectors, for downstream machine learning tasks.

How are graph embeddings generated?

There are multiple graph embedding implementations that rely on different embedding algorithms. The most popular ones include node2Vec, GraphSAGE, and PyTorch-BigGraph.

The goal of each of these algorithms is to "learn" a feature representation for each node in a given graph. The choice of algorithm commonly depends on the structure and size of the input graph. PyTorch-BigGraph, for example, can handle multi-entity/multi-relation graphs with billions of nodes and trillions of edges.

The bottom line

Graph embeddings are used for building graph machine learning models which power a growing number of graph analytics and intelligence applications. This highlights the importance of graph embeddings and the algorithms used to generate them for graphs of different types and varying complexity.

Last updated on 2 Oct 2020 by Anton Vasetenkov
Cover
See also
Linked data for the enterprise: Focus on Bayer's corporate asset register
An overview of COLID, the data asset management platform built using semantic technologies.
Towards more linked lexicographical data: Lexemes on Wikidata
A glimpse into the meaning and other properties of words described with structured and linked data.
Document understanding: Modern techniques and real-world applications
How document understanding helps bring order to unstructured data.
Why federation is a game-changing feature of SPARQL
SPARQL federation is an incredibly useful feature for querying distributed RDF graphs.
Harnessing the power of the Oxford English Dictionary for linguistic research and NLP applications
How the OED Text Annotator may help bring text mining and natural language processing technologies to the next level.
Interested?
Get in touch now.
Hesper
Your local knowledge engineering guru.
Copyright © 2020 Hesper NZ. Various trademarks held by their respective owners.