Towards the future digital workplace: the Semantic Platform

Towards the future digital workplace: the Semantic Platform

Semantic-Platform-Digital-workplace

At Konica Minolta we are committed to understand the workplace of the future and to find the best solutions for our customers. Almost one year after the presentation of Cognitive Hub during the AI Summit of New York, where partners and clients were introduced for the first time to our platform of platforms, today we present the Semantic Platform, one of the key elements on which the Cognitive Hub is built.

In order to face enterprises’ ever-increasing need to manage the growing complexity of flowing data and information in the everyday workplace, the Semantic Platform is a complex ecosystem of Machine Learning and Natural Language Processing functions that supports users in the management of information collected in their digital workplace.

Understanding the user’s work preferences

The Semantic Platform (SP) includes smart services designed to help individuals and teams to be more efficient and productive, simplifying their work with digital documentation and allowing for timesaving. Thanks to a rich ecosystem of semantic applications, the Semantic Platform leverages information from various unstructured data sources to understand the user’s and the team’s work preferences and needs. The Semantic Platform formulates an interpretation of such information that supports users and groups in taking more informed decisions. All this is possible through an event-driven microservices architecture designed to keep track of all the sources of information related with users and groups (tasks, activities, calendar, social network, data and documents) to predict their interests, to provide them with the right information at the right time, to automate routine tasks and offer them automated and adaptive decision support.

The architecture of the Semantic Platform

Analysing both contents and contexts

Within the process of data acquisition and creation of the knowledge base for the provisioning of the semantic services, there are two fundamental elements that form the base function of the Semantic Platform.

The first is the Document Enrichment Engine (DEE); the core architectural component of the Semantic Platform to interpret a digital document. DEE is a software service that takes any unstructured data (textual documents, images, webpages, multimedia recording) as input, and enriches it with relevant structured data from multiple connected information sources. The purpose is to extract and interpret the hidden information contained in the unstructured data, enabling further processing of the document by higher-level Semantic Platform components.

The second is the Semantic Enrichment and Linking Framework (SELF) that represents the user-centric view of the digital workplace. SELF analyses all users’ behaviours: how the users manage documents and data, if they achieve assigned goals or not, how they organise their daily work and interact with colleagues and groups. These are all key elements to establish the working context of the users. The Semantic Platform is able to match both the content (data and documents) and the contexts (behaviours) of the users, understanding the relationships between data and users’ actions. This layer of descriptive information allows SELF to provide highly context-aware applications and user-centred services.

Looking for partnership opportunities

The Semantic Platform is rich in opportunities for your contribution and ideas. At Konica Minolta Laboratory Europe, we have planned two main partnership modalities.

Providers of third-party applications can be involved in partnerships for developing applications built on top of the platform to deliver specific solutions for customers’ needs.

Partnerships about core platform technological components are focused on the development and enhancement of the platform itself. Within this area, we welcome cooperation as well as academic and open source community contributions, in terms of algorithms, specific Document Enrichment Engine modules, Machine Learning models and expertise in areas of NLP, Ontology Engineering and Machine Learning.

If you are interested in learning more about this project, read our whitepapers and get in touch with us.

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