Semantic Map is a tool that provides visual representation of relations and patterns between different pieces of information. It’s commonly used in education (primarily, linguistics and language learning) and advanced data analytics, as it helps to both clarify and classify relations between multiple data items, for example, huge and complicated documents, building a clear visual hierarchy.
Our solution takes this technology to the next quality level through AI usage.
A well-designed combination of ML techniques and AI-powered search allows to automatically identify relations between texts and map them to semantically related keywords. To display relations, semantic map generates ‘clots’, or dense areas in the map, associated with the key topics of any document in the collection. At the end, the user gets visual representation of the data patterns.
The range of this technology usage is vast, and its implementation can help to solve various tasks.
For instance, companies can detect trending social media topics by grouping posts into clusters and semantically sort them, or they can improve customer experience gathering and classifying multiple user reviews and applications by topics, making the information convenient for further analysis.
Another important application is data labeling: automatically detected groups of similar documents can be labeled in batches for significant saving in tagging big number of documents.
Our tool provides full automation in the process of data processing, analyzing large amounts of textual content. In further perspective it saves hundreds of human specialists working hours.
A well-trained AI-model allows to analyze data and provide results with 98% accuracy rate.
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. The important feature of our solution is that it takes context and surroundings of the keywords into consideration, allowing to find quite unexpected relations between them. As a result, keywords that are not explicitly used in the document but important for semantic core are displayed.
When it comes to building efficient business strategy, our tool is indispensable. For example, you can analyze financial reports or market conditions data, and the results will be automatically visualized. As a result, you’ll have detailed structure of the most vital business variables, which will improve decision making processes.
Seeing the visual demonstration of information patterns based on AI-powered search and analysis of the most vital data, you’ll optimize your marketing and content strategies.
Gather social media reports, customer reviews and present trends, find connections between vital insights and be able to enhance your sales.
There’s hardly a sphere that could benefit more. Whether you learn foreign languages, write scientific reports and reviews or study an academic course, semantic map is a tool that will make the process easier and more efficient.
Identify patterns and relations between knowledge and information pieces to get proper understanding of a subject.
Writing an article and need to analyze invoices and find patterns between them? AI-powered search with semantic map representation is a perfect way to achieve high quality and consistency of the content.
We’ve prepared two demo versions for you!
Number one is based on the entire English Wikipedia and was already practically with several Swiss and Austrian publishers. Here we present how different documents, topics, fragments, and keywords are semantically interconnected and co-related.
The second one is dedicated to Covid-19. Its goal is to show how experts can study Covid related academic papers by providing a navigable and structured 2-D representation of the entire collection of documents.
This solution is based on analysis of more than 30 000 scientific papers dedicated to Covid-19, the amount that could take lifetime of several specialists to read through.
As you may see, the technology automatically identifies relations between texts and generates data ‘clots’ and map areas, associated with the key topics of any document in the collection, where every dot represents a particular document.
You may notice that the same keywords may appear in several places on the map and at a rather far distance from one another.
This is the display of one of solution features. Technology studies keywords in specific contexts solving the common problem of studying block based on usage of terms in different contexts.
Moreover, Covid semantic map displays keywords which might not be explicitly used in the document, but which have semantic proximity to a core text.
"Many people find semantic maps convenient and efficient tools for study and research. We have applied our comprehensive experience in AI solutions development to take this technique to the next level. Our solution aims to relieve specialists from time-consuming data search and research and to present visual demonstration of information patterns for further usage."
Yuri Svirid, PhD. — CEO Silk Data
We have already processed millions of documents, and our solution can serve the data amounts of various scales. Moreover, it’s recommended to provide as many documents as possible on the initial stage to properly train the AI model.
Two main benefits are that our product saves time, sparing you from time-consuming operations and provides visual demonstration of data patterns and hierarchy which is very useful for proper understanding and further analysis.
Of course. We provide comprehensive support for the project after its launch, including updates to the data and the integration of new requirements.