Semantic Data and Standardization
What is Semantic Data
Semantic data is structured data designed to add meaning to data. This is achieved by creating data relationships between entities to provide truth to the data and the importance needed for data consumption. Semantic data helps maintain consistency between data relationships.
A semantic data hub allows organizations to extract meaning, relationships, and truths among all types of data. Emerging technologies, such as machine learning and artificial intelligence, particularly benefit from semantic data.
Creating data collaborations using a semantic approach enables the transformation of data into information and information into knowledge for agile decision support. While many applications attempt this within their closed ecosystems, much more value can be added, and solution implementations can scale by moving this implementation to an independent data layer that supports data management and allows for much faster conversion of legal content into digital format.
How Does Semantic Data Work?
A semantic data model works by creating relationships between data when the data is organized. This allows data to acquire meaning without human intervention or additional processing by other systems.
Data is organized into three essential parts, or triples: (a) the first data element or object; (b) the relationship; and (c) the second data element or object.
Database management systems that follow a semantic data model can be easily integrated and compared for more related data insights.
Building a semantic data model starts with understanding the outcomes of the questions that need answers for necessary decisions, whether processing inputs to construct a document, evaluating case conditions to decide on task distribution and execution, or analyzing data to determine strategies to adopt.
Semantic Data Model vs. Relational Data Model
The most significant difference between a semantic data model and a relational data model is how they are constructed. The relational data model is built using relationships between tables, columns, and rows in the database. Although associations are made in a relational data model, queries are required to discover the relationship between one data element and another.
In a semantic data model, the meanings of data are described as related to a real-world interpretation of how the data is used. The semantic model derives from facts and truths rather than a relational model needing to query for truth.
The main disadvantage of using a relational data model is the difficulty of establishing all the hierarchies and relationships between tables and elements. It is not “future-proof,” making it challenging to adjust to an evolving understanding of a problem or changes in the parameters of a given solution.
On the other hand, adopting a “pure” semantic model is also difficult, as it complicates the practical implementation of various solutions, such as search and data retrieval, database distribution, solution scalability, implementing segments of a larger problem, etc.
Currently, computational infrastructure allows for adopting hybrid solutions, enabling developers to organize some data relationally and some semantically. At Looplex, this is achieved by adopting a relational model that uses a reference semantic model in its implementation.
Knowledge Management and Bottlenecks Resolved with a Reference Semantic Model
The data and elements of a business process or legal content come from various sources that are invariably unstructured (many of them implicit and informal) or, if structured, lack a universal structuring standard.
Even adopting a unified methodology for legal knowledge management for digital transformation, a common problem encountered in countless past legal automation structuring projects is the availability of detailed data requirements.
Mapping processes for legal engineering required identifying and declaring, case by case, all operational requirements and entities.
However, identifying the details of these types and entities can be challenging, significantly extending the interaction time with client-side specialists and making it impossible to scale these concepts as legal engineers increasingly encounter new challenges.
To solve this, it is necessary to progressively map and connect standardized types that repeat across various content and workflows to a semantic model representative of that legal object. Specifically, it is necessary to reference clauses, theses, parts of a business, and other elements to:
- A structured conceptual model;
- A simplified structured glossary relating the element to a more abstract classification.
The primary purpose of using this semantic reference is to use this definition to communicate within the organization, particularly among analysts and legal engineers who (a) map and convert analog legal content into an automated solution (document assembler); (b) manage operational data storage (ODS) or connected data warehouse projects (warehouse-oriented projects) where not all elements are pre-structured; and (c) handle workflows for automating operations linked to a smart contract or a case or dispute (LegalOps and algorithmic contract execution).
This approach enables integrating the experience of delivering a legal service end-to-end without needing to build these BI integrations or content workflows in each isolated case. Additionally, new projects arrive with an increasing number of pre-mapped elements, allowing componentized content construction.
Such a reference semantic model can act as an efficient bridge, independent of the syntax of Unified Modeling Language (UML) and Entity-Relationship Modeling (ER) specific to each problem encountered.