Machine Learning

Concept Extraction/Semantic Tagging

Semantic tags are a valuable resource. By tagging content with keywords, one can track and find content appropriate to the subject of the matter.

We offer semantic tagging services that allow authors to automatically titrate content according to the most important terms. 

Key Features :

  • Disambiguation of Entities
  • A machine learning-based approach
  • Identifiable Named Entities
  • Concept Extraction
  • Optimized discovery of metadata
  • Value-based conceptualization 

Benefits :

Linked data is meant to help find new data streams by connecting existing ones. Prior to the advent of machine learning, we relied on our own human-generated knowledge and intuition to determine whether certain information was useful. The power of semantics is now at our disposal. 

  • Automated idea generation for your content.
  • Build smarter search capabilities on top of scales.
  • More efficiently than manual labor.
  • Streamline your content.

How does it work?

As part of our analysis of the text, we look for themes, collect keywords and terms, and determine whether related terms need to be disambiguated. So, every subject matter has a semantic fingerprint, which is composed of metadata linked to knowledge graphs.

Semantic Data Modelling

Data is becoming increasingly important for various sectors for a number of reasons. There is no doubt that the sheer volume of data that must be maintained is a significant issue. Obtaining relevant apprehensions rapidly from a vast amount of data that comes from a wide range of assemblages is essential for any successful data analytics initiative.

Using our semantic data modelling services, users can analyse data in a more targeted and effective way, while integrating data into a complete semantic model that can be easily enhanced. 

Key Features :

  • Compound and regulate statistical data.
  • Empower cognition with semantic information.
  • Systematic analysis and predictive models.
  • Providing global insight into operations through profound data point analysis.
  • Conformity and integrity

Benefits:

Move from quantitative data analysis to evidence-based analysis. Integrate data into a unified semantic database that can be modified as needed. Discover, endeavor to utilise the information that is easily accessible. Utilize organisational data more effectively by viewing it in new ways. You can gain insights through extensive data analysis.a better perspective on your business performance.

How does it work?

Big data analytics allows us to draw newfound conclusions by putting information into context. Our semantic data model represents data analytically by structuring it. Semantic content is considered in the model, providing significance to the data and drawing patterns within.

Content Recommendation

Content recommendation is a powerful tool to entice engagement by suggesting helpful content to your audience.

Using our content recommendation service, users can be categorised into different segments and recommendations can be delivered based on their preferences.

Key Features :

  • Optimal traffic flow.
  • Increases engagement and optimises conversion rates.
  • Enhances credibility and viewership.

Benefits :

Content recommendation is a great way to provide a shortlist of informative content that’s relevant to your audience. Molecular Connections understands the content of domain journals semantically, so we offer content recommendations that will increase reader engagement, conversion, and traffic to your work. It can be used as a ranking or semantic link shortener, or to help you manage your content and link profile in different formats.

How does it work?

The AI component of our service is trained to give recommendations based on what a reader has already consumed, as well as any other data the publisher knows about their users.

Content Classification

Content classification is a step-by-step process that returns one or multiple categories of content. Our objective with the Content Classification Service is to classify unstructured information by performing a knowledge graph-powered classification. Classify against a standard or your taxonomy using our automated software.  

Key Features :

  • Categorise the subject matter.
  • Machine learning approach based on advanced algorithms.
  • The aggregation’s interdisciplinary context.
  • Integrating updated content.

Benefits :

  • A broader context
  • Efficiency in content structuring.
  • Results that are unbiased

How does it work?

Using supervised machine learning and algorithms, we automate the process of analysing unstructured information by utilising knowledge graph-based semantic analysis of all available textual matter.

AI/ML Workflows

Publishing & Indexing Workflow Development provides a turnkey software solution to meet publishing goals. Beginning from the ideation stage to the final publishing stage, it helps every member involved in the process track the progress through real-time monitoring, allowing them to add value, where necessary. The software keeps all data and information in one place and empowers users to automate and streamline the entire process.

Key Benefits of Publishing & Indexing Workflow Development:
  • By removing any potential bottlenecks, the entire publishing process is streamlined.
  • System automation helps with easy work allocation and accountability.
  • Never lets you miss a deadline.
  • Process violations are avoided through real-time monitoring.
  • Content archiving solutions.
  • Time -efficient.

Molecular Connections’ Publishing & Indexing Workflow Development assures the optimal software solution to meet your publishing objectives, from design and development to execution and support. Our technical abilities, coupled with our in-depth knowledge of the publishing industry, enable us to create a strong process.

ML Model supporting Data labelling

Labelling data can refer to tagging, annotating, sorting, editing, transcribing, and analysis.

With our ML labelling systems, we offer a choice of options for optimizing and ensuring quality assurance of large datasets.

Key Feature:

  • Accurately and consistently labeled data
  • Data labeling quality assurance
  • Iterative processing
  • Higher-quality data labels 
  • Process flexibility

Benefits :

  • Quality assurance measures for data labels
  • Streamline production time
  • Document-based secure data labeling platform
  • Accurately assess real-world conditions

How does it work?

As a machine learning model learns from human-provided labels, we call this “model training.” After this process, we have created a model that is trained to predict specific data labeling.

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