#NLP is now good enough that we can measure how well systems reads text in terms of extracted knowledge: Knowledge Base Population @diffbot released KnowledgeNet reproducible #dataset to measure this, w #opensource state-of-the-art baseline #research #AI

Analyzing US Lobbying Data in #Neo4J #graphdatabase

SHACL2SPARQL is a prototype Java implementation of the algorithm described in #iswc19 best #research paper award winner Validating SHACL constraints over a SPARQL endpoint (Corman, FLorenzano, Reutter & Savkovic). #opensource #github #softwaredevelopment

Gartner Names PoolParty as Visionary in Metadata Management Magic Quadrant. Our modern approach combining machine learning and knowledge graphs makes the difference!

generation retrieval systems to improve retrieval performance. Knowledge graph abstracts things into entities and establishes relationships among entities, which are expressed in the form of triples. However, with the expansion of knowledge graph and the rapid increase of data volume, traditional place retrieval methods on knowledge graph have low performance. This paper designs a place retrieval method in order to improve the efficiency of place retrieval. Firstly, perform data preprocessing and problem model building in the offline stage. Meanwhile, build semantic distance index, spatial quadtree index, and spatial semantic hybrid index according to semantic and spatial information. At the same time, in the online retrieval stage, th

AtOoEEeib8ndhFNAFIw/citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.538.7139&rep=rep1&type=pdf , the original “Knowledge Graph” that Google acquired, has been the promise of pushing data control aspect from the database to the application layer including the possibility of people rolling their own situated personal ontologies in the expectation for being able to reconcile them through peer to peer interactions.Freebase exercised full control over the “Schema” and has demonstrated a million fold leverage of meta level knowledgeIn it’s day it had over 3000 million nodes and 3000 carefully defined and curated edge types.Years later Wiki

pandemic) and have our LinkedIn feeds (trained by AI, ironically) locked into the data management world, these trends are

sourced Ampligraph, a suite of machine learning models that can uncover new knowledge from existing knowledge graphs. We’re also exploring how knowledge graphs can contribute to social good. Working with Lambert Hogenhout, Chief of Data Analytics for the United Nations, we organized a “Knowledge Graphs for Social Good” workshop. The effort focused on the UN’s 17 Sustainability Development Goals: concrete areas like poverty, hunger, and health where action is needed to impr

n

n

Bill Slawski is an SEO legend.

Our interview with Teodora Petkova, sharing her views on weaving text into the semantic web and her recent book The Brave New Text.

n

haystack discoveries that evade traditional analytic workflow.

I have seen several tools for converting spreadsheets to RDF over the years. They typically try to cover so many different cases that learning how to use them has taken more effort than just writing a short perl script that uses the split() command, so that’s what I usually ended up doing. (Several years ago I did come up with another way that was more of a cute trick with Turtle syntax.)

In part 1 of this article series, I provided a quick primer on graph data structure, acknowledged that there are several graph based algorithms with the notabl…

Semantic Web Company and Ontotext implemented a seamless integration of their products to solve numerous use cases in knowledge graph management.

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.

viruses worldwide has

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

n

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

Wan Kenobi, Alec Guinness are nodes and