01 Feb 2019
by Janos Szendi-Varga
Neo4j
GraphAware
Few years ago I decided that one day I would create a Graph Technology Landscape map, which would be useful for everyone who wants to discover the players around graph technologies. I started to collect the companies and products, but my research has never manifested into a proper blog post. Till now. I am happy to announce, that the first version of my landscape is published, I hope we can consider this as a start of a long journey.
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28 Jan 2019
by Jan Zak
Analytics
Connected Data
Community Detection
Voice
In social network analysis, a conventional approach relies heavily on available metadata, allowing to match a virtual entity (social network account) to a real-world entity (person, company) in the network. However, a single person using multiple accounts for any reason obviously breaks the connection, forming multiple virtual entities in the network. Or multiple people can share their account, forming a single virtual entity in the network. If these cases are not taken into account, they can affect reliability of social network analysis significantly without any warning, possibly leading to misinformed decisions and further bad consequences.
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11 Jan 2019
by Christophe Willemsen
neo4j
cypher
search
In this blog we will go over the Full Text Search capabilities available in the latest major release of Neo4j.
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27 Dec 2018
by Luanne Misquitta
Neo4j
GraphAware
2018- it’s been such a whirlwind of activity at GraphAware, and we’re so proud of everything we’ve accomplished this year.
In fact, we grew and grew, announcing ourselves in Australia and then, later in the year,
expanding into the Americas.
“Neo4j is one of the most disruptive and transformative technologies I have seen in my career,” said Kyle McNamara, CEO, Americas. His team are well on their way to increasing GraphAware’s presence and strengthening the already close bond we have with Neo4j.
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16 Dec 2018
by František Hartman
testing
docker
neo4j
Automated testing is the cornerstone of any successful software project.
Applications using the Neo4j database are no exception. This blog post
shows how to use the Neo4j Docker
image and the Testcontainers
library for integration testing in
Java using JUnit.
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28 Nov 2018
by Kyle McNamara
GraphAware
BOSTON, MA, Nov. 28th, 2018 – GraphAware, a leading Neo4j consulting practice, today announced the official launch of its US entity GraphAware, Inc., headquartered in Boston, MA. This strategic investment by GraphAware aligns with Neo4j’s own rapid market expansion.
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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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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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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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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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