SERP analysis is an essential step in the process of content optimization to outrank the competition on Google. In this blog post I will share a new way to run SERP analysis using machine learning and a simple python program that you can run on Google Colab. SERP (Search Engine Result Page) analysis is part of keyword research and helps you understand if the query that you identified is relevant for your business goals. More importantly by analyzing how results are organized we can understand how Google is interpreting a specific query. What is the intention of the user making that search?What search intent Google is associating with that particular query?The investigative work required to analyze the top results provide an answer to these questions and guide us to improve (or create) the content that best fit the searcher. While there is an abundance of keyword research tools that provide SERP analysis functionalities, my particular interest lies in understanding the semanti

First data modeling is based on two assumptions. The first

[Submitted on 17 Apr 2020] Download PDF Abstract: ``Classical'' word embeddings, such as Word2Vec, have been shown to capture

based entity recommendation framework

Within a growing organization, finding the right information can take up a good part of your day.

L3S member Prof. Dr. Sören Auer, Director of the TIB and Professor of Data Science & Digital Libraries at Leibniz Universität Hannover, is one of the world's most cited researchers in the field of artificial intelligence. Analyses of academic data from the online platform AMiner (https://www.aminer.org/ai2000/ke) have shown that Sören Auer is one of the most influential scientists in the field of knowledge engineering (knowledge modelling).  The list, which puts him in 4th place, honors the work of the researchers over the last 10 years. The "Most Influential Scholar" award is given in recognition of outstanding technical achievement with lasting impact. How can the handling of information, data and knowledge be improved and made more effective? In view of the enormous technological progress, how can knowledge and information be digitally networked so that they can be better used by machines in the future? Auer and his team at the TIB and the L3S in Hannove

literally as well as figuratively (through investments, supplying

For this blog Ahren Lehnert explains why this may be a golden moment for KM to address theoretical benefits in the context of real-world emergencies.

GDELT is the largest, most comprehensive, and highest resolution open database of human society ever created. Its vast archives of more than a quarter billion georeferenced records covering the entire world over 30 years, coupled with massive networks that connect all of the people, organizations, locations, themes, and emotions underlying those events, offers unprecedented opportunities to understand and interact with our world in fundamentally new ways.

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“You must unlearn what you have learned” (Master Yoda) The “conceptual” level

The lack of support for stateful cloud-native application behavior is a roadblock to many cloud use-cases. This article looks at graph knowledge-based systems which offer one approach to the design of next-generation platforms.

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Last month in Populating a Schema.org dataset from Wikidata I talked about pulling data out of Wikidata and using it to create Schema.org triples, and I hinted about the possibility of updating Wikidata data directly. The SPARQL fun of this is to then perform queries against Wikidata and to see your data edits reflected within a few minutes. I was pleasantly surprised at how quickly edits showed up in query results, so I thought I would demo it with a little video.

answering is a natural human cognitive mechanism that plays a key ole in the acquisition of knowledge. We are constantly…

19 tool designed to help doctors make quicker decisions.

Ontology Model

distanceFeature engineering

Graph Data (Image credit actify)eCommerce catalogs are created by sourcing data from sellers(3P), suppliers/brands(1P). The data provided by partners (sellers, suppliers, brands) are often incomplete, sometimes missing crucial bits of information that our customers are looking for. Even though partners adhere to a spec (an agreed format for sending product data) there is a vast amount of data buried in the title, description and images. Besides the data provided by our partners there are lots of unstructured data on the internet in the form of product manual, product reviews, blogs, social media sites etc.At Walmart we are working on building a Retail Graph that captures the knowledge about product and its related entities to help our customers better discover products in our catalog. It’s a product knowledge graph that can answer questions about products and related knowledge in the retail context. Such a system can be used to power semantic search, recommendation system etc.

grade features and scale. We appreciate your candid stories and collaboration, and we’ve used this to create a better solution. As such, we’re excited to announce Neo4j for Graph Data Science™, the first data science environment built to harness the predictive power of relationships for enterprise deployments. Neo4j for Graph Data Science is an ecosystem of tools that includes: With Neo4j for Graph Data Science, data scientists are empowered to confide

infection, repeated use of the public places and transportation, the spreading path of the virus becomes a network structure. Thus a

2 together with its impact on human health. One aspect of this is organizing existing and emerging information about viral and host cell molecular biology, disease epidemiology, phenotypic progression, and effect of drugs and other treatments in individuals.

(Submitted on 18 Mar 2020) Abstract: In the active research area of employing embedding models for knowledge graph

bound task that can take months for subject matter experts (typically researchers, clinicians, and health professionals) to comple

Accurately predicting the possible behaviors of traffic participants is an

19 tracking project data available to the public.  This data is paired with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. This dataset is updated hourly at 45 minutes past the hour.

by Allard Oelen and Markus Stocker

What do eBay, Airbnb, Microsoft, Lending Club, and Comcast have in common? They're all using knowledge graphs to understand their customers, business decisions, and product lines. Why? Because they easily and intuitively visualize the nature, depth, and interdependence of the relationships they've created with their business decisions.

This article has two sections. The first describes the longstanding difficulties in integrating data for analytics and more recently, data science and AI.

László Barabási never bothered to learn English. “My worst grades were always in English because I thought, Why study it? You can never leave this country,” explains Barabási, director of the Center for Complex Network Research (CCNR) at Northeastern University in Boston. “It wasn’t until I got to the University of Bucharest and became interested in research that I understood the importance of being able to read academic papers in English.” Barabási emigrated from Romania to Budapest with his father in the summer of 1989, a few months before Ceaușescu was overthrown, and completed a master’s degree in physics at Eötvös Loránd University two years later. But it wasn’t until after he’d earned a Ph.D. in physics at Boston University in 1994, while working as a postdoc at IBM’s legendary Thomas J. Watson Research Center, that Barabási became inter