With an AI-powered search program, we utilize the potential of semantic mapping. Using concept-based search technology, our software locates the most pertinent results from the global database. The outcomes are then represented by our Semantic Map, which highlights the conceptual connections between the topics.
With the help of Machine Learning and AI-augmented search, our technology automatically identifies relations between texts, maps them to semantically related keywords and then generates a visual representation of the data.
The process is almost entirely automated, while it is necessary for a human expert to categorize generated topics, optionally naming them for easier navigation and collaboration with other team members.
Since Semantic Map can provide additional analytics, one can improve business activity with the help of this powerful tool.
Another advantage is that you can navigate between documents and distill the required knowledge really simply and quick.
We have prepared a demo version based on the entire English Wikipedia and also tested pilot projects with Swiss and Austrian publishers. You are welcome to test our demo Semantic Map and see how different documents, topics, fragments, and keywords are semantically interconnected and co-related.
Another available demo is a Semantic Map of COVID-19 research papers. The goal of the COVID-19 Semantic Map is to show how experts can study academic papers by providing a navigable and structured 2-D representation of the entire collection of documents.
The common problem related to all known approaches to studying based on keywords and topic analysis is when one term is used in different contexts. Our Semantic Map provides the possibility to study keywords in specific contexts – that’s why the same keyword may appear in several places on the map. It can help experts find surprising connections between keywords and corresponding documents just by examining the highlighted areas.
Besides, the Semantic Map provides unsupervised classification of texts, which, for example, improves the search functionality as described in the case study on AI-assisted search.
The Semantic Map technology can be deployed on any public or private cloud or on a dedicated server. Please contact us with more details of you business case to request a quote.
Check out our demo based on the English Wikipedia.
We have already tested millions of documents. Our solution can serve the data from a relatively small instance, while a powerful enough server or cluster will be necessary for the initial implementation (training) of the model.
We provide comprehensive support for the project after its launch, including updates to the data and the integration of new requirements. Please contact our sales for quotes.