We live in a world of big data, and semantic engines are a perfect solution to keep up. In order to make good decisions, we need to have access to the needed information in real-time. Human beings are unable to search for a specific piece of information among the extensive amount of data, as it would simply take too much time if it were possible at all. The answer to this problem is semantic engines. They are based on semantics, which in regards to linguistics is the study of meaning in language.
Semantic engines can find high-quality information in infinitely big data sets. Any company that needs to have or provide quick access to information finds these engines irreplaceable. They’ve been proven especially useful in the banking industry. It’s due to the amounts and complexity of data that is being dealt with on a daily bases.
Semantic engines – what are they?
Semantic engines are powered by machine learning (ML) and can process a large amount of data extremely quickly and efficiently. Their purpose is to recognise the user’s query and find the needed information within a large amount of data.
Semantic engines go way beyond traditional analytical tools (that only look for literal matches to the query) and can actually comprehend users intention.
They are very accurate and effective as their software is integrated with natural language processing (NLP).
Semantic engines will be a vast facilitation for everyone who needs to regularly mine amongst immense volumes of information.
They are irreplaceable, especially in giant corporations and organisations that deal daily with big data (vast volumes of data that cannot be processed by traditional analytical and mining tools due to their vast size and complexity).
Natural language processing – what hides behind the term?
Natural language processing (NLP) is essentially text mining and its analysis. It’s applied in multiple fields that, in one way or another, deal with processing and interpreting human language (e.g., speech recognition, chatbots, machine translations, search engines). In other words, it can be described as the process of deriving high-quality information from any text.
Natural language processing is a part of computer science that focuses on deriving meaningful information from natural language text, with the use of artificial intelligence.
Natural language processing is used for deriving meaningful informations from natural language text
That’s why the integration of semantic engines with NLP enables them to distinguish linguistic nuances just as humans do and thus to correctly understand the user’s query.
Instead of just detecting the entries containing the initial keywords, they also comprehend the possible interpretations and return such results.
Semantic engines in banks – what for?
Semantic engines can comprehend much more complex queries than traditional search engines that search only for initial keywords. Therefore, they can distinguish between different types of commands, such as:
- the type of transaction (e.g. ‘withdrawal’, ‘ATM’, ‘transfer’)
- the organisation or person with whom the transaction took place (e.g., the company name)
- whether the transaction came into our bank account or from it (e.g., ‘income’, ‘outcome’)
- the date of the transaction – including time periods (e.g. ‘since yesterday’, ‘last week’, ‘November’, ‘from 01/03/2015 until yesterday’)
- the category (the purpose of the transaction – e.g. ‘car and transport’)
- the amount – including the fixed value ranges (e.g. ‘from 1000PLN’, ‘1000-5000PLN’, ‘<1000 PLN’, etc.)
As visible, semantic engines not only can distinguish between various types of commands but also comprehend one command being written in multiple different ways.
Semantic engines go beyond the level of structural code and are based on ML and integrated with NLP. It’s the source of their semantic abilities, and it’s what makes them so effective at extracting high-quality information.
Ontological banking, ontology-centric banking – what is it?
Ontology in banking
Ontology in informatics means a common vocabulary or a shared understanding between business “what” and IT “how”. Ontology-centric approach enables computers to comprehend web-based information and conduct automated operations on this data. They help to create a thread of understanding between the logic-based business specifications and the technical construct that uses this to create a language comprehensible by all users.
Ontologist in banking
In the field of semantic banking, a new position appeared – an ontologist. Think of them as the new generation business analysts. They are key in creating ontology-centric systems.
Their responsibility is to structure the language in businesses. To do that, they define the main concepts, relations in between them, and the rules. The test data and all the requirements need to be provided to the ontologist for him to begin the work.
Afterward, the person delivers the results to the IT department after several rounds of trial and error (testing and improving until success). As a result of the joint work of the ontologist and software developers, it’s possible to create a language for an industry that can be comprehended by both: humans and machines.
Semantic engines’ versatility in FinTech industry
Semantic software can be customised and trained to interpret particular vocabulary in any domain. Meaning, that once the software is trained for a particular bank, it significantly improves data management within the ontology-centric banking. The highest benefit is that the introduced solution is highly flexible and can be applied to a small part of the banking system or across multiple fields.
Semantic engines can be integrated with any mobile or internet banking system. What’s more, some semantic engines can operate in multiple languages.
Semantic engines go way beyond traditional analytical tools and can actually comprehend users intention
Overall, the introduction of semantic engines into a banking system helps to manage data within that system. It increases the search for information and the accuracy of the results. It will not only implement better user experience for the customers of the bank.
If customized and trained for the internal systems of the organisation, it can significantly speed up the processes happening within the company. As a result, it allows for effective, real-time decision making, which can influence and direct further actions to improve a business strategy and adjust it to the current situation (that’s always defined by up-to-date data).
WEBSENSA – how do we use semantic engines?
At WEBSENSA, we are developing natural language parsers, processors, and interpreters.
- Our semantic engines operate in both english and polish.
- It’s possible to deploy the semantic engine as SaaS (Software as a Service) or On-Site.
- The parsers, processors, and interpreters are compatible with Speech-To-Text libraries for creating Voice Controlled Solutions and are easy to integrate into existing web and mobile applications.
- We customise and train them to interpret a specific language in any domain: banking, e-commerce, e-mail search, TV control, and many more.
Semantic engines in FinTech and other industries
Overall, semantic engines can be applied to any field where there is a need to search through more structured and complex data.
If you have any questions about the possible usage of semantic engines feel free to contact us.
If you are interested in machine learning and artificial intelligence briefly mentioned in this article, check out our section entirely devoted to AI!
