Unstructured content is ubiquitous in today’s business environment. In fact, the IDC estimates that 80% of the world’s data will be unstructured by 2025, with many organizations already at that volume. Every organization possesses libraries, shared drives, and content management systems full of unstructured data contained in Word documents, power points, PDFs, and more. Documents like these often contain pieces of information that are critical to business operations, but these “nuggets” of information can be difficult to find when they’re buried within lengthy volumes of text. For example, legal teams may need information that is hidden in process and policy documents, and call center employees might require fast access to information in product guides. Users search for and use the information found in unstructured content all the time, but its management and retrieval can be quite challenging when content is long, text heavy, and has few descriptive attributes (metada

The final blog from the Synaptica team on the Stanford University course series on Knowledge Graphs. This week looked at open research questions.

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Graph Convolutional Networks (GCNs) generalize the idea of deep convolutional

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constructed reports, that is easy, but as they become long, then things get really difficult. Thankfully there are a whole bunch of tools that are now becoming available that allow for the extraction of insights.The tools are possible owing to the advancements in natural language processing — the use of computational techniques and models to analyse natural language — language as it is used around us — in the documents, in voice, in chats. The explosion of content generated, and especially Wikipedia — has made various advancements possible. Thanks to Wikipedia, which contains topics arranged in a structured manner, and thanks to the effort put into translation by Go

Facebook appears to be testing the addition of Wikipedia knowledge panels in search results, according to reports from multiple users.Based on the screenshots shared on Twitter, this feature is reminiscent of Google’s integration with Wikipedia.Here’s an example that was spotted a few days ago:New? Facebook shows Wikipedia snippits in search resultsh/t @jc_zijl pic.twitter.com/zcbQJmauhE— Matt Navarra | 🚨 #StayAtHome (@MattNavarra) June 9, 2020Just like in Google’s search results, the Wikipedia knowledge box in Facebook search shows key details about the entity being searched for.You’ll also notice there’s a lone Instagram link, which is a stark contrast to Google’s search results containing links to all popular social media profiles.Unlike Google’s knowledge panels, which link to a number of domains where people can learn more about a entity, Facebook is trying to keep people within the Facebook ecosystem as much as possible.Here’s another example that looks

This week, Cambridge Semantics was named a Leader in The Forrester Wave™: Enterprise Data Fabric, Q2 2020, and we could not be more delighted. Forrester used 25 criteria to evaluate the 15 most significant enterprise data fabric vendors to show how each vendors' platforms measure up in their ability to accelerate data integration, minimize the complexity of data management, and quickly deliver use cases. Cambridge Semantics received top scores for Vision, Road Map, Solution Awareness, Data Preparation, Data Integration, Data Catalog, and Data Processing. This evaluation, we believe, validates Cambridge Semantics' breakthrough approach to the data fabric, and reflects the feedback we are hearing from customers and partners using our solution to integrate and manage large volumes of data quickly and at scale.

This story was originally published on InsideBigData .Knowledge graphs are one of the most important technologies of the 2020s. Gartner predicted that the applications of graph processing and graph databases will grow at 100% annually over the next few years.Over the last two decades, this technology was adopted mostly by engineers and ontologists, hence the majority of knowledge graph tools were designed for the users with advanced programming skills.In 1900, 40% of the population was involved in farming.Today it’s 1%. Coding is the modern day “farming” as only 0.5% of the world’s population knows how to code.NoCode brings equal opportunities to talent of all trades.Let’s just imagine, what impact this could have if the majority of world’s population was able to take advantage of cutting edge technologies to solve top of mind problems.Empowering top talent with NoCode approachEngineering and programming are important skills but only in the right context, and only for

>)You may have read that companies such as Amazon, Facebook, Microsoft, JPMorgan and Bank of America have made large investments to develop their own proprietary knowledge graphs, to make “strategic use of data and extend business boundaries[1]”.How is this relevant to the “SQL World”?What does this have to do with you, the SQL/relational database professional? Why should you care that there is a new world of databases that is alien to most relational databases experts and users?For the near future maybe you shouldn’t care. After all, about 80% of the database infrastructure in the world is relational, so for you SQL is a sure bet.But wait, here’s big data, ever growing big data. Big data is complex, it has variety and it is difficult to

up the value of e anytime I need it (figure 1);Figure 1. A data dictionary with key and value of arithmetic expressions.(ii) if I do not have that option then the only other alternative to get the value of e is to actually compute the arithmetic expression and get the corresponding value. The first method, let’s call it the data/memorization method, which does not require me to know how to compute e while the second does. That is, in using the second method I (or the computer!) must know the procedures of addition and multiplication, shown in figure 2 below (where Succ is the ‘successor’ function that returns the next natural number).Figure 2. Theoretical definition of the procedures/functions of addition and multip

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Visualizations are key to communicating messages and taking complicated ideas and making them simpler to interpret.

based Technology Enables Companies in the Financial Sector to Quickly and Efficiently Integrate and Analyze the Explosion of Disparate Data from RegulatorsOver the past decade, the advance of digital innovation combined with the growing number of regulatory guidelines in all jurisdictions has sped up the rise of Regulatory Technology, or RegTech for short. Originally, its main application was to enhance regulatory processes in the Financial sector, but nowadays it is expanding more and more in to any regulated business. A good sign that this technology is steadily going mainstream is also the fact that more and more regulatory bodies are supporting RegTech development and implementation.According to a September 2019 report from Juniper Research, the value of global spending on RegTech is expected to jump to more than US $127 billion by 2024, up from US$25 billion spent in 2019. Automation in Know Your Customer (KYC) processes and background checks are set to be so

Read about why and how knowledge graph technology helps build networks of interwoven digital objects in the world of cultural heritage.

time.Therefore, we have recognised that building a “Talent Knowledge Graph” will allow us to effectively operate in our domain as well as to fully unlock the predictive power of artificial intelligence and provide unique insights from our heterogeneous data sources.After Google’s reveal of its Knowledge Graph platform in 2012, the term has been rapidly growing in popularit

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BLINK is an Entity Linking python library that uses Wikipedia as the target knowledge base.

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1. Knowledge Graph

The Centre for Digital Built Britain’s National Digital Twin programme has launched an open consultation seeking feedback on the proposed approach to the development of an Information Management Framework for the built environment. 

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Neo4j’s Alicia Frame explains how life science researchers can exploit graph databases to get truly granular insight into big data to make major leaps forward in medical research.Complex data sets hold the key to advancing medical breakthroughs. These data sets tend to be voluminous and heterogeneous by nature, presenting an insurmountable challenge for traditional data analysis methods as they struggle to link patterns and outcomes. The unfortunate consequence is a slowdown in the progress of research.Anyone who works in life sciences is aware that they are working with highly connected information; the challenge is making sense of these connections. Unfortunately, many scientists are still using relational databases and spreadsheets which makes mapping important patterns and connections unintuitive and difficult, if not impossible.Graph technologyGraph technology is emerging as an enabler for researchers to trawl gargantuan amounts of unstructured data, turning it into valuab

The team continue their blogs on the Stanford University course on Knowledge Graphs. This week “What is the role of knowledge graphs in machine learning?”

based search systems do not reflect the semantics of individual input words of search queries. For example, a query for the word “house” would not return records for the words “building” or “real estate”. How can such relationships be represented in a technical system? One approach is to include synonyms. Search engines like Elasticsearch provide methods to integrate synonym lists. However, a list of synonyms itself is required for configuration.

With the huge volume of research now available, and the ability to manage and analyze enormous amounts of data, you could say there has never been a better time to be a researcher. The opportunities to build on each other’s work are greater than ever.However, all that information brings challenges, with researchers indicating that just navigating all that information takes more time than ever.So how about a system of information that could give researchers precise answers to their questions immediately, rather than having to browse a list of webpages that might hold the answer deep in the text?Researchers at Elsevier's DiscoveryLab – part of the Innovation Center for Artificial Intelligence (ICAI) in Amsterdam – are building a system to deliver just that. Dr. Frank van Harmelen, Professor of Computer Science at the Vrije Universiteit (VU) Amsterdam and Academic Director of Elsevier's DiscoveryLab, explained:What we’re developing is an integrated knowledge graph for resea

For autonomous vehicles (AV), the ability to share information about their surroundings is crucial. With Level 4 and 5 autonomy in sight, solving the challenge of organization and efficient storing of data, coming from these connected platforms, becomes paramount. Research done up to now has been mostly focused on communication and network layers of V2X (Vehicle-to-Everything) data sharing. However, there is a gap when it comes to the data layer. Limited attention has been paid to the ontology development in the automotive domain. More specifically, the way to integrate sensor data and geos...

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