(testing signal)

Tag: knowledgegraphs

Integrating IoT Data with Digital Twin Knowledge Graph

The creation of a DigitalTwin knowledge graph data model confronts the need for access to measurement data in order that the DigitalTwin can create timely performance metrics, identify promptly performance issues, and so on.
However, the quantity of raw data in an Industrial IoT is staggering. A typical process manufacturing plant might have greater than 100,000 measurement points each of which is streaming data by the second or even faster. So how can the raw data be integrated to allow performance analysis?

Just … important:

Someone(?) determines a subset of the data that can and…

Improving Machine Learning: How Knowledge Graphs Bring Deeper Meaning to Data

Enterprise machine learning deployments are limited by two consequences of outdated data management practices widely used today. The first is the protracted time-to-insight that stems from antiquated data replication approaches. The second is the lack of unified, contextualized data that spans the organization horizontally.
Excessive data replication and the resulting “second-order effects” are creating enormous efficiencies and waste for data scientists in most organizations. According to IDC, over 60 zettabytes of data were produced last year, and this is forecast to increase at a CAGR…

Building a Knowledge Graph for Job Search using BERT Transformer

While the natural language processing (NLP) field has been growing at an exponential rate for the last two years — thanks to the development of transfer-based models — their applications have been limited in scope for the job search field. LinkedIn, the leading company in job search and recruitment, is a good example. While I hold a Ph.D. in Material Science and a Master in Physics, I am receiving job recommendations such as Technical Program Manager at MongoDB and a Go Developer position at Toptal which are both web developing companies that are not relevant to my background. This…

Data intelligibility and the quest for mutually understood meaning

The other day, I came across a Quora question I just had to answer. This is not unusual for me. It wasn’t that the question itself didn’t deserve an answer. It was that one of the respondents appeared to be as ill-informed as the questioner, thus reinforcing the questioner’s initial impression with a wrong answer. 
Because it was a question about knowledge graphs, I couldn’t let this go. I felt compelled to give a reasonable answer.
The question was this one: Is a knowledge graph just another means of knowledge visualization? My answer was no. I explained why, saying that the main…

Knowledge Graph Forum: Technology Ecosystem and Business Applications

Ontotext is thrilled to invite you to the Ontotext & partners virtual Knowledge Graph Forum, Oct 26 & 27, 2021. This event is shaped by Ontotext’s vision that knowledge graphs serve as a hub for data, metadata and content. 35+ speakers from around the globe will share their experiences through real-life cases and platforms demonstrations. Save your spot now.

Getting Started with Jupyter+IntelligentGraph

Since IntelligentGraph combines Knowledge Graphs with embedded data analytics, Jupyter is an obvious choice as a data analysts’ IntelligentGraph workbench.

The following are screen-captures of a Jupyter-Notebook session showing how Jupyter can be used as an IDE for IntelligentGraph to perform all of the following:

Create a new IntelligentGraph repository
Add nodes to that repository
Add calculation nodes to the same repository
Navigate through the calculated results
Query the results using SPARQL

GettingStarted is available as a JupyterNotebook here: GettingStarted JupyterNotebook

Images of the GettingStarted JupyterNotebook follow:


Using the Jupyter ISparql, we can easily perform SPARQL queries over the same IntelligentGraph created above.… Read more...

Fuse Graph Neural Networks with Semantic Reasoning to Produce Complimentary Predictions

Fuse Graph Neural Networks with Semantic Reasoning to Produce Complimentary Predictions
Photo by Maxime VALCARCE on Unsplash

IntelligentGraph = Knowledge Graph + Embedded Analysis

IntelligentGraph adds analysis capability embedded within RDF graphs.

At present calculations are either delivered by custom code or spreadsheets. The data behind these is inevitably tabular. In fact, so dominant are spreadsheets with analysis that the spreadsheet often becomes the ‘database’ with the inherent difficulties of syncing that data with the source system of record.

The real world is better represented as a network or graph of interconnected things, therefore a knowledge graph is a far better storage organization than tables or objects. However, there is still the need to perform ad hoc numerical analysis over this data. 

Confronted with this dilemma, knowledge graph data would typically be exported in tabular form to a datamart or directly into, yet again, a spreadsheet where the analysis could be performed.


Graph Neural Networks Combined with Semantic Reasoning Deliver ‘Total AI’

The ability for machines to reason—not just identify patterns in massive data amounts, but make rule or logic based inferences on domain specific knowledge—is foundational to Artificial Intelligence. The growing momentum around Neuro-Symbolic AI and the increasing reliance on Graph Analytics demonstrate how important these developments are for the enterprise.

Combining AI’s symbolic knowledge processing with its statistical branch (typified by machine learning) produces the best prescriptive outcomes, delivers total AI, and is swiftly becoming necessary to tackle enterprise scale applications of mission-critical processes like foretelling equipment failure, optimizing healthcare treatment, and maximizing customer relationships.


Build and Analyze Knowledge Graphs with Diffbot

Access Knowledge Graph with Diffbot

Diffbot is the world’s largest knowledge graph that allows you to access a trillion connected facts across the web.

To start with Diffbot, sign up for a 14-day free trial. This free trial will allow you to search Diffbot’s Knowledge Graph.

After logging in, click the Search option to search the Diffbot Knowledge Graph.

Image by Author

Have you ever wondered:

  • which school Apple’s employees went to,
  • what their majors were,
  • and what the most popular roles at Apple are?

We can answer these questions using Diffbot’s Search. Start with searching for the Diffbot ID of Apple Inc. by:

  • selecting “Organization”
  • filtering by “name” that “contains” “apple”
  • clicking “Search”
Image by Author

You will see 61,820 results that are related to apple.


Answering Causal Questions in AI

Two of the main techniques used in order to try to discover causal relationships are Graphical Methods (such as Knowledge Graphs and Bayesian Belief Networks) and Explainable AI. These two methods form in fact the basis of the Association level in the Causality Hierarchy (Figure 1), enabling us to answer questions such as: What different properties compose an entity and how are the different components related each other?

In case you are interested in finding out more about how Causality is used in Machine Learning, more information is available in my previous article: Causal Reasoning in Machine Learning.

Knowledge Graphs are a type of Graphical Technique commonly used in order to concisely store and retrieve related information from a large amount of data.