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IREENE is a system for analysing the semantic similarity of texts in a repository. It displays the most relevant articles based on a set of supplied keywords, a metric to assess the similarity between the papers, and the similarity between all the documents in a latent space. IREENE additionally includes data extraction, recognition of essential keywords utilised throughout texts, and frequency of recurrence analysis.

How does it work?

In the case of the digital industry, data-driven engineering and manufacturing refer not only to machine-generated data fed through IIoT but also to the vast accumulation of unstructured data, including textual content written in natural languages. The volume of available data is even bigger as virtual organisations build on the free flow of information and knowledge between direct partners and third parties.

Design, engineering, manufacturing, and other processes of industrial enterprises are deeply embedded in textual data, usually human-generated content such as patent files, scientific publications or industrial standards like IEEE or IEC. In order to embrace both the volume and potential of pertinent but heterogeneous data, it is necessary to make it machine-readable first. This is where IREENE comes in.

Input files to IREENE could include a wide range of inputs such as patents, user requirements sheets, customer feedback, troubleshooting descriptions, failure, and fault reports, insights from previous projects, regulatory considerations, engineering standards such as those defined by ISO, IEEE, or IEC, not to mention product-relevant scientific publications.

IREENE processes input files of different formats (e.g. text documents, spreadsheets, presentations) in order to create a knowledge graph representing the contents of processed sources. The data sets used in the development have been subjected to topic modeling, which as an unsupervised machine-learning technique to detect similarities between documents and cluster expressions that statistically define the contents of a document in the most accurate manner.

IREENE uses a topical model to enable functionalities of (a) smart semantic search and (b) visual knowledge-graph browsers. Smart semantic search and visual knowledge-graph browser are the enablers to apply the Business Platform for Distributed and Decentralized Data Exchange Ecosystems not only to the traceability use case but for Electronics and ICT as an enabler for the digital industry and optimised supply chain management covering the entire product lifecycle in large ecosystems.


How we applied the state of the art to the project

IREENE (Information Retrieval Engine) is a solution to this need of providing methods of processing unstructured text documents in order to create a knowledge graph representing the contents of available sources.

IREENE adds significant capability to product knowledge management in modern manufacturing enterprises.

Topic modelling and semantic representation of existing documents’ knowledge graphs might minimise the time necessary for the manual processing of essential documentation by individuals involved in product management across several organisational verticals.

It has already achieved great success in offering insights for product management, including but not limited to operations, compliance, R&D, and intellectual property rights.


A solution for operations, compliance, R&D, and intellectual property rights.

DAC's team possesses a rare combination of technical proficiency and strategic vision, enabling us to harness the full potential of machine learning in solving complex business challenges. 

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Anonymous

Learn how DAC.digital worked with DAC.digital SA (EU R&D founded project) and how they can support your Business & Expert Solutions product and team.

DAC.digital Success Story in Business & Expert Solutions for DAC.digital SA (EU R&D founded project) - A case study | 2024

IREENE (Information Retrieval Engine) is a solution to this need of providing methods of processing unstructured text documents in order to create a knowledge graph representing the contents of available sources.


How to deal with a substantial volume of external documents?

The client stakeholders at DAC.digital SA (EU R&D founded project) were working closely with the team at DAC.digital

IREENE – ML-based tool for topic modeling and documents similarity measurement and visualization

DAC.digital

It was one if the most interesting and challenging ML projects we had - designing new technology and applying cross-vertical knowledge was needed and by taking part in such EU-founded initiatives helps us to build up the skills and knowedge and add competetive advantage to our services.