GraphAware Blog

Find out what's new in the Neo4j world

Why You Should Start Thinking About Your Organization as a Graph

22 Oct 2018 by Janos Szendi-Varga Knowledge Graph NLP Neo4j Connected Data Knowledge Platform

Do you think there is no space for a graph database in your company? Or it would be a huge effort to integrate a graph database into your product? I have to tell you: You can use a graph database like Neo4j without touching your product, and you can use it for managing your company’s knowledge as well as to improve your software development process. So, even if your business problem is not inherently graphy (hard to believe in 2018), there are a few reasons why you should think about your environment as a graph.

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Bring Order to Chaos: A Graph-Based Journey from Textual Data to Wisdom

26 Sep 2018 by Dr. Alessandro Negro, Dr. Vlasta Kůs NLP Knowledge Graph Sentiment Analysis word2vec NER

Data is everywhere. News, blog posts, emails, videos and chats are just a few examples of the multiple streams of data we encounter on a daily basis. The majority of these streams contain textual data – written language – containing countless facts, observations, perspectives and insights that could make or break your business.

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Caring about sentiment: how to get the most from people feelings

17 Sep 2018 by Dr. Vlasta Kůs, Dr. Alessandro Negro Sentiment Analysis doc2vec NLP Knowledge Graph

It is often useful to relate a piece of text with the sentiment expressed in it. Extracting and processing sentiments from text provides not only a new emotional access pattern to your corpus but also new knowledge which can reveal new insights. Suppose you want to build a recommendation engine which leverages reviews to spot detailed strengths and weaknesses of different hotels, such as good location but bad staff. Or, it certainly makes a difference whether an article talks about your organization in a positive or negative manner.

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Deep text understanding combining Graph Models, Named Entity Recognition and Word2Vec

10 Sep 2018 by Dr. Vlasta Kůs, Dr. Alessandro Negro word2vec NLP NER Knowledge Graph

One of the key components of Information Extraction (IE) and Knowledge Discovery (KD) is Named Entity Recognition, which is a machine learning technique that provides us with generalization capabilities based on lexical and contextual information. Named Entities are specific language elements that belong to certain predefined categories, such as persons names, locations, organizations, chemical elements or names of space missions. They are not easy to find and subsequently classify (for example, organizations and space missions share similar formatting and sometimes even context), but having them is of significant help for various tasks:

  • improving search capabilities
  • relating documents among themselves or with external information (such as connecting people in a financial document with information from a business registry)
  • relating causes (e.g weather conditions, accidents, regulatory changes) with effects (e.g. flight or tram delay, stock price changes)

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Advanced Document Representation

03 Sep 2018 by Dr. Vlasta Kůs, Dr. Alessandro Negro doc2vec NLP Knowledge Graph

Representation is one of the most complex and compelling tasks in machine learning. The way in which we represent facts, events, objects, labels, etc. affects how an autonomous learning agent can analyze them and extract insights, make predictions and deliver knowledge.

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Solving the bucket-filling problem with Neo4j

02 Sep 2018 by Vince Bickers Neo4j State Machine Optimisation Problems Shortest Path

Introduction

In the bucket filling problem you are given two empty buckets, each of a certain capacity, and a large supply of water. By filling, emptying and transferring water between the two buckets, you must try to end up with a situation where one of the buckets contains a required volume of water, or where both buckets together contain the required volume.

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GraphAware Expands to Asia-Pacific

26 Mar 2018 by Luke Housego GraphAware

Nearly six years ago I started reading about this thing called Graph Data. Now unlike almost everyone in the modern world of Graph, I am not a coder. I was once upon a time but it has been many years since I typed a line of code. No I do not have a GitHub login. As an enterprise architect I often joke that I do powerpoint for a living. But I could see how this re-envisioning of data and how it could be used would transform enterprises. It is not just a technical change. It changes everything. People. Organisation. Knowledge. Processes. Products. Services. And yes, technology too.

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