Semantic Mapping: Enhancing Data Understanding and Interoperability

The data records stored across different systems in your enterprise sometimes have different formats but convey the same information. Identifying and consolidating such correlated data fields helps you achieve a unified view of complete enterprise data. This is where semantic mapping comes into the picture, as it helps you understand the interrelatedness of various data points.

Here, you will learn in detail about semantic mapping along with its benefits, challenges, and real-world use cases. Using this information, you can adopt the semantic mapping technique to give meaning to the pure data records and generate knowledgeable business insights.

What is Semantic Mapping?

Semantic mapping is the process of defining relationships between different data records or fields across various source data systems. This structured and correlated data is called semantic data and represents meaningful information that helps you utilize data efficiently.

In your business organization, different departments usually collect data separately to perform designated functions. This data is stored across various sources, such as RDBMS, CRM, ERP, or any other data systems. In several instances, the data within these sources can represent similar entities but differ in form.

Consider an example of a retail company where data related to CRM, point of sales (POS), and inventory management are stored in different databases. Suppose a customer places an order multiple times, and each order contains several different products.

You will have to store the customer’s data in the CRM system, order-related data in the POS database, and product information in the inventory management database. Here, the ‘Customer ID’ in one data system can be similar to the ‘Client ID’ in another data system. Similarly, ‘Date of Purchase’ can be the same as ‘Order Date’. This may create confusion, resulting in inaccurate business analytics.

To eliminate such discrepancies, you can use semantic mapping, which helps you understand the relations between different data points. Semantic mapping is also an essential component in creating a semantic layer, which is a metadata and abstraction layer built on the source data. This layer gives you a unified view of your enterprise dataset, allowing you to perform analytics and business intelligence operations.

Benefits of Semantic Mapping

By providing the underlying meaning of data through relationships, semantic mapping offers several advantages. Here are some important benefits of semantic mapping:

Developing Data Understanding

Semantic mapping helps you understand the correlation between data points and their relevance with business concepts. This allows non-technical employees of your organization to gain knowledge about your company’s products and services. For example, in an enterprise database, a product code X1234 represents a wireless keyboard. Creating a semantic mapping of product code with its product name clearly indicates what is represented by the product code data record.

Enhancing AI and ML Models

You can impart a better contextual understanding to AI and ML models through semantic mapping. This facilitates efficient search and retrieval operations in the LLMs as they can better understand and process human-readable language. You can use these LLMs for character recognition, sentiment analysis, and summarization.

Creating Better Data Visualizations

Understanding the relationships between data points through semantic mapping enables you to create more illustrative charts, dashboards, and visual reports. Such visuals facilitate clear communication of the data analysis findings, helping you make well-informed decisions for your business growth.

Improving Data Interoperability

Data interoperability is the capability of a data system that helps you access and exchange data with other systems without losing meaning. This ensures data integrity, which is critical for robust data integration. There are different types of data interoperability, such as syntactic, semantic, and cross-domain.

Streamlining Semantic Mapping With Airbyte

Semantic mapping begins with data extraction from disparate sources. Airbyte, an efficient data movement platform, simplifies this task by offering an extensive library of 550+ pre-built connectors. These connectors help you extract data from relevant sources, including databases, APIs, and cloud storage, and load it into your desired destination. This ensures all relevant data from multiple sources is in one place, making it simple to define relationships.

Once the data is consolidated, you can define the relationships between the data points that align with business needs. Then, you can create a semantic layer that enhances understanding and interoperability.

For custom connector requirements, Airbyte offers multiple options like Connector Builder, Low Code Connector Development Kit (CDK), Python CDK, and Java CDK.

Here are some additional key features of Airbyte:

• AI-powered Connector Builder: You can utilize AI assistant while developing custom connectors using Connector Builder in Airbyte. The AI assistant automatically pre-fills and configures necessary fields in the Connector Builder and also provides intelligent solutions to fine-tune connector configuration.‍
• Change Data Capture (CDC): Airbyte’s CDC feature lets you capture incremental changes made at the source data system. You can replicate these changes in the destination data system and keep it in sync with the source system to ensure data consistency.‍
• Multiple Sync Modes: You can opt for various sync modes to read data from a source system and write them to a destination while using Airbyte. It offers sync modes such as Incremental Append, Incremental Append + Deduped, Full Refresh Append, Full Refresh Overwrite, and Full Refresh Overwrite + Deduped.‍
• Custom Transformations using dbt: You can integrate Airbyte with dbt, a command line tool to clean and transform raw data into a suitable format. This can help you remove the data heterogeneity, making it easier to define semantic relationships.‍
• Streamline GenAI Workflows: If your extracted data is semi-structured or unstructured, Airbyte allows you to load it directly into vector store destinations like Pinecone or Milvus. You can then perform semantic mapping on this data to make it contextually rich and improve the functioning of your GenAI workflows.‍
• RAG Techniques: You can integrate Airbyte with LLM frameworks like LangChain or LlamaIndex to perform RAG transformations such as chunking and indexing. By using these techniques, you can improve the accuracy of outcomes generated by LLMs.‍
• Schema Management: Schema management is crucial for better data synchronization. Airbyte allows you to manage schema changes in source data for each connection. You can manually refresh the schema whenever necessary, saving time and effort during the data pipeline development process.

Challenges of Semantic Mapping

It is important for you to be aware of the potential challenges you may encounter while leveraging semantic mapping. Some of these challenges are as follows:

Heterogenous Data

The format and structure of data differ across various data source systems. Due to these variations, you may encounter difficulties in defining relationships. For example, the date format is YYYY-MM-DD in some data systems, while in others, it is DD/MM/YYYY format. To avoid discrepancies due to heterogeneity, you should cleanse and transform the data in all source systems.

Overlapping of Concepts

Sometimes, a single data element can have multiple meanings. For instance, a company that produces and delivers fruits is using an AI model to track operational efficiency. Suppose the term ‘date’ is used for both the harvest date and the delivery date in the production and delivery datasets.

In such instances, the AI model can become confused and may generate inaccurate results. To prevent such issues, you should give specific column names like harvest_date and delivery_date for accurate semantic mapping.

Complexities in Designing Ontology

A semantics ontology is a knowledge structure representing semantic relationships of a particular domain in a machine-readable format. You may find the process of creating ontology complex as it requires expertise and a lot of time. To overcome this challenge, using a pre-built ontology framework can be an efficient solution. One example of such a framework is FIBO, which enables you to define relations between data and the terms used in the finance domain.

Scalability Issues

It is difficult to create semantic mappings for high-volume datasets. The reason for this is that as the data volume grows, you have to manage more number of elements and relationships during semantic mapping. This can be complex and requires expensive computational resources. A complicated semantic mapping of training datasets can also increase the time required to search and generate responses in LLM.

Use Cases of Semantic Mappings

You can utilize semantic mapping in numerous applications. Here are some of its real-world use cases:

Business Intelligence

The semantic layer created after mapping enterprise data helps you understand the concepts represented by the data records. You can further utilize this data in BI tools to create interactive dashboards and reports, gain business insights, and make informed decisions.

Knowledge Graphs

A knowledge graph is an interconnected graph database that allows you to provide contextual information to data through semantic mapping, translating data into useful information. Google’s knowledge graph utilizes semantic search and mapping to improve the results generated by the Google search engine.

AI-based Document Analysis

You can semantically map the data used to train the AI model for your optical character recognition (OCR) platform. Semantic mapping improves the results produced by the OCR tool. It identifies the meaning of the extracted data points and categorizes them into relevant groups. For instance, during invoice processing, the AI OCR tool can accurately extract, correlate, and classify data as invoice numbers, dates, customer names, and amounts.

Recommendation Systems

Platforms like Netflix or Amazon use semantic mapping to recommend relevant content or products to their users. Semantic mapping enables identifying the correlation between unstructured data such as product descriptions, tags, or reviews. This helps the OTT and e-commerce platforms to suggest related content or products.

Conclusion

Semantic mapping is becoming an essential part of modern data workflows driven by AI and machine learning. This blog gives you a comprehensive overview of semantic mapping and how it benefits data integration, interoperability, and the functioning of AI and ML models.

However, you may encounter some challenges during the semantic mapping process due to data heterogeneity or overlapping of concepts. To resolve these drawbacks, you can opt for practices such as data cleaning and clearly naming data columns. These best practices allow you to leverage semantic mapping for use cases like AI-powered document analysis or product categorization.