In this 3-part series “Know what you know: Competing on Analytics with Knowledge Graphs” Dr. Alessandro Negro, Chief Scientist at GraphAware, walks you through analytics, knowledge graphs and its “competition”.
In the series Graphs in Law Enforcement, we discussed data sources and modelling, data quality and credibility with source ratings, and we spoke about fusing entities.
In the first part of the series Graphs in Law Enforcement, Data sources and modelling, we discussed graphs in law enforcement investigations, their data sources, data provenance, INTs and how to model sources in graphs. In part 2, Data quality and credibility, we covered source ratings (source reliability & information credibility) and their importance in investigation graphs for law enforcement.
In this 4-part blog series “Graphs in Law Enforcement”, we will examine Data Sources in Law Enforcement Graphs, a talk by Luanne Misquitta, our VP of Engineering, at GraphConnect 2022.
Criminal investigations are driven by finding the connections and hidden links between wide-ranging sources of data to ultimately disrupt criminal activity. Graph solutions are a powerful tool for law enforcement to optimise their interaction with data from these varied sources.
Knowledge Graphs (KGs) have become the backbone of multiple applications, including search engines, chatbots, and question and answering tools, where interactivity plays a crucial role.
Graph visualisation is just what it sounds like - a visual representation of your data as a graph. A graph is a structure of objects that are connected. Thus graph visualisation is the visualisation of entities (nodes), and relationships among them.
It is always a valuable opportunity to understand our product better and recognize user needs. At GraphAware, building Hume, a graph-powered insight engine, we are proud of making an impact on our customers’ success. However, we use Hume also to support our processes and help our own needs. In the case of the event that took place throughout December, we were also able to have great fun and integrate the team.
Graphs are a natural fit for investigative use cases. Whenever you want to analyse a situation containing people, objects, locations, and events (POLE), graphs emphasising relationships between objects are your natural companion.
Welcome to the first blog in the business series of GraphAware blog! This series is designed for us non-techies out there. Personally, I was shocked when I found out how big and common knowledge graphs are and how often graph databases are used in today’s world - and I had first heard of them just a couple of months ago. So, for people like me, for marketers and non-tech people in business, I’ll try to open the door to the world of graphs, and their potential and take you through it step-by-step. It seems only appropriate that we start with...
This blog post is the first part in a series on Effective Graph Visualisations, showcasing features emerging from years of experience in the field.
Knowledge Graphs (KGs) have become one of the most powerful tools for modeling the relations between entities in various fields, from biotech to e-commerce, from intelligence and law enforcement to fintech. Starting from the first version proposed by Google in 2012, the capabilities of modern KGs have been employed across diverse applications, including search engines, chatbots, and recommendation systems.
In our last MET Art Collections post we ingested and processed part of a dataset containing more than 470,000 artworks from The Metropolitan Museum of Art and created a knowledge graph using Hume, GraphAware’s insights engine.
The Metropolitan Museum of Art recently published a dataset of more than 470,000 works of art under the CC-zero License. Representing such a collection as a knowledge graph allows us to explore it in a unique way - seeing the artworks, their authors, donors, mediums, tags, or art movements deeply connected, being able to traverse the links between them and discover unexpected relations.
If you have read our post Hume in Space: Monitoring Satellite Technology Markets with a ML-powered Knowledge Graph, you surely wonder: is there a way to extract relations among named entities without heavy investment? Investment in terms of time to label training dataset and to develop, train and deploy a machine learning model?
Everyone has a passion for something. Be it music, politics, sports, coffee or … pancakes. Such passion makes you strive for new information, for understanding of the current trends. Take pancakes: you might watch for new recipes on your favourite website, you might look at cooking shows or youtube videos to get more inspiration about how to serve them … but overall, you can probably handle this pretty well. It’s not like there is much room for revolutionising the pancake recipe.
“Lateral thinking” was a big topic back in 2004 when I was in the Network Operations Center (NOC) business; one definition is:
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.
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.
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.
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...
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.
“Relevance is the practice of improving search results for users by satisfying their information needs in the context of a particular user experience, while balancing how ranking impacts business’s needs.”