Amazon.com: Knowledge Graphs and Big Data Processing (Lecture Notes in Computer Science Book 12072) eBook : Janev, Valentina, Graux, Damien, Jabeen, Hajira, Sallinger, Emanuel: Kindle Store

We present principled approaches to train and deploy dyadic neural embedding models at the billion scale, focusing our investigation on the application of semantic product search. When training a...

This particular article is a discussion about a recommendation to a given standard, that of SPARQL 1.1. None of this has been implemented yet, and as such rep…

User profiling has long been an important problem that investigates user interests in many real applications. Some recent works regard users and their interacted objects as entities of a graph and...

GraphDB 9.10 features smart updates via SPARQL templates and Kafka as well as graph-path search optimizations

Software development organizations are seeking to add quality to their products. Testing processes are strategic elements to manage projects and product quality. However, advances in technology and the emergence of increasingly critical applications make testing a complex task and large volumes of information are generated. In fact, software testing is a knowledge intensive process. Because of this, these organizations have shown a growing interest in Knowledge Management (KM) programs, which in turn support the improvement of testing procedures. KM emerges as a means to manage testing knowledge, and, consequently, to improve software quality. However, there are only a few KM solutions supporting software testing. This paper reports experiences from the development of an approach, called Ontology-based Testing Knowledge Management (OntoT-KM), that aims to assist in launching KM initiatives in the software testing domain with the support of Knowledge Management Systems (KMSs).  OntoT-KM provides a process guiding how to start applying KM in software testing. OntoT-KM is based on the findings of a systematic mapping on KM in software testing and the results of a survey with testing practitioners. Moreover, OntoT-KM considers the conceptualization established by a Reference Ontology on Software Testing (ROoST). As a proof of concept, OntoT-KM was applied to develop a KMS called Testing KM Portal (TKMP), which was evaluated in terms of usefulness, usability, and functional correctness. Results show that the developed KMS from OntoT-KM is a potential system for managing knowledge in software testing, so, the approach can guide KM initiatives in software testing.

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...

This article, the first in a series, introduces and defines the concept of a knowledge graph. It highlights its origins and basic characteristics.

An increasing number of cities are creating 3D city models to support visualization and simulations in the urban planning process. The 3D city models...

For some reason I keep hearing the word "upper ontology" more frequently in recent times. I find this problematic, because in many of the contexts I hear about it, it is introduced as a kind of secret sauce that 20 years of Semantic Web research have been hiding from the greater public.

And if you do there's a simple way to prove it.

We believe that the future for cooperation in the construction industry is more based on data and not so much on files.

We are excited to announce that we are running an online panel this November where will explore the foundations of why graphs are the right tool for complexity...

Mike Tung on the problems with search and the future of knowledge representation

Build and Analyze Graph Database with Neo4j

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

The first end-to-end clinical knowledge graph creation using Spark NLP and Neo4j.

The framework achieves great coverage and performance across different incident triaging scenarios, and also outperforms other state-of-the-art root cause analysis methodologies.

The Pandora Papers leak includes a colossal 2.94 terabytes of data. Unravelling the contents was no easy task

Click to learn more about author Thomas Frisendal. Many of us have a lot to do. And we have short delivery cycles, sprints, and a lot of peers to share data models with. In search of something lightweight, which is quick and easy, and may be produced (or consumed) by other programs? Stay with us on a […]

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

Operationalizing AI initiatives and data AI are among major trends for CIOs to watch in 2021.

I would suggest that Google does not have its own intelligence. If I search for, say, ‘Arnold Schwarzenegger and Harvard’, Google will only suggest documents that contain BOTH Arnold Schwarzenegger and Harvard.

Knowledge graphs help in organizing unstructured data in a way that information can easily be extracted where explicit relations between multiple entities help in the process.

Knowledge Graphs (KGs) have become one of the most powerful tools for modeling the relations between entities in various fields, from biotech to e-comme...

This might seem like a trivial question at first, but it's not. Before we can evaluate whether a particular markup is actually helping Google understand the content of a web page, we need to make sure that Google can crawl and index its markup.