Several scientists in the first half of the 20th century made enormous contributions to science and technology in different fields. We can mention several Nobel Prize winners, including Albert Einstein, Ernest Lawrence, and Niels Bohr, who provided fundamental contributions to physics studies. But what about their role as fundraisers? And what about their influence in funding research topics unrelated to their scientific contribution, such as life sciences and biology?
Have you ever dreamed of producing a Knowledge Graph (KG) from a pile of textual documents at the click of a button?
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.
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.
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, 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.
Companies of any size have to manage and access huge amounts of data providing advanced services for their end-users or to handle their internal processes. The greater part of this data is usually stored in the form of text. Processing and analyzing this huge source of knowledge represents a competitive advantage, but often, even providing simple and effective access to it is a complex task, due to the unstructured nature of the textual data. This blog post will focus on a specific use case: provide effective access to a huge set of documents - later referred as a corpus -...
A book tells us a story, but for a computer it is a wall of text. How can we use graphs and NLP to help our machines make more sense of a story?
“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.” 
In recent years, the rapid growth of social media communities has created a vast amount of digital documents on the web. Recommending relevant documents to users is a strategic goal for the effectiveness of customer engagement but at the same time is not a trivial problem.
Without question, Github is the biggest code sharing platform on the planet. With more than 14 millions users and 35 million repositories, the insights you can discover by analyzing the data available through its API are surprising and revealing.
A great part of the world’s knowledge is stored using text in natural language, but using it in an effective way isstill a major challenge. Natural Language Processing (NLP) techniques provide the basis for harnessing this huge amountof data and converting it into a useful source of knowledge for further processing.