AI-powered literature search and extraction tools can understand the context of your research, regardless of domain.
You probably know the feeling when you’re not quite sure which papers or patents you’re looking for. It’s not oranges, but not really apples either — and you can’t really put your finger on what it is.
In this instance, traditional keyword-based tools limit you to the keywords you already know (oranges and apples). They require you to build long and quite complicated strings of words to find the relevant scientific documents.
In contrast, you can train AI-powered research tools to understand the context of your research, including key terminology, synonyms and hypernyms.
In effect, when you train research tools in your domain, they might tell you that you’re probably not looking for apples or oranges — but clementines 🍊
You are unique
…and so is your research domain and process.
That’s what makes literature search and data extraction highly manual processes. They require highly skilled researchers who know what papers and patents are relevant in their specific domain.
Traditional search tools are based on keywords, meaning that you need to know what you are looking for in your research. The tools don’t know anything about your research. That’s a challenge when you don’t know what you’re looking for, as you can easily miss important and relevant documents in the vast amount of published literature.
Similarly, extracting data from papers and patents is extremely time-consuming, as literature is often very technical and each research area has its own nuances.
No research is identical, and to advance our research — and avoid drowning in the abundance of literature — we need tools that better understand what we’re looking for.
How AI-powered research tools understand the nuances of all researchers in any domain
[Image of fingerprint]
The way that we’re all unique humans with our own fingerprints, the same way your research is unique, and research tools that are trained in our domain understand this.
What does that look like in practice?
When you search for literature using trained AI tools, you provide the machine with an existing and relevant paper or patent. The tools will then find the most important terms in that paper and identify synonyms and hypernyms, which forms your research fingerprint, which is matched to documents in the database.
Then, the tools score other literature based on how similar it is to the search’s virtual fingerprint. You can see this score as a percentage, where 100% would be a duplicate.
This is often called a content-based recommendation engine. The machine is not showing and recommending what other researchers have read, but only documents that match your fingerprint.
So now we’ve found the literature we need. For some of us, an even more arduous process begins: to identify key information in each document and put it in tables.
You can use trained research tools to automatically extract the data you need from documents.
When you train the AI-powered research tools in your research domain, you basically teach it your language: the terminology, the format of papers and patents, abbreviations and what information is most important to you.
So you specify to the tools what key information you would like to extract from the corpus of documents, and they will look through the documents for that information, including abbreviations, synonyms and hypernyms.
And it’s fast. Very fast. You can extract data from up to 60 documents in only a few minutes.
How do you train the machine in your domain?
The short answer is that it’s easy and only takes about one day in man-power. The longer answer is for another day in another post.
Or reach out to us, and we’ll happily talk you through it!
👉 Traditional research tools limit you to the keywords you know, but AI-powered research tools can learn your research domain. This means it understands the specific terminology, synonyms, hypernyms and more.
👉 You can train research tools in any research domain.
👉 Trained research tools better understand the nuances of what you are looking for. They turn your research query into a unique virtual fingerprint which is matched to similar documents.