Unraveling the Path to Genuine Semantic Interoperability across Digital Systems - Part 1
Part 1: Semantic Interoperability
Introduction
Imagine you're a data scientist working in a multi-disciplinary team. Your day-to-day involves exchanging complex datasets and intricate models with partners and collaborators globally. However, every exchange feels like a hurdle - inconsistencies, inefficiencies, and a lack of synergy often arise when people need to interpret data. You ask yourself, "Isn't there a more seamless way to ensure that data models carry the same meaning for everyone involved, regardless of the system or platform they're using?"
"Data semantics" refers to the interpretation and meaning of data, and "semantic interoperability" is the ability of computer systems to exchange data with unambiguous, shared meaning. Semantic interoperability is a common requirement to enable machine computable logic, inferencing, knowledge discovery, and data federation between information systems.
Achieving genuine semantic interoperability is a central yet challenging goal in the complex data management and exchange landscape. This post explores decentralized semantics, a vital solution aiming to transcend the limitations of traditional methods. As we delve into data semantics, we touch upon Overlays Capture Architecture (OCA), a decentralized metadata framework for defining the semantic composition of digital objects, enabling the creation of interoperable schemas, and facilitating data harmonization. We aim to showcase how decentralized semantic structures enhance data exchange and ensure the transmission of intrinsic meaning and interpretability, paving the way for more robust and meaningful interactions between digital systems and challenging the prevailing notion that semantic alignment between ontologies and knowledge graphs is the solution to semantic interoperability.
Data Semantics Demystified
Data semantics includes "morphological semantics" [1] and "epistemological semantics" [2]. Imagine morphological semantics as the blueprint of a dataset. It pertains to the textual attributes that outline the concrete structure of data, akin to how architectural plans delineate the formation of physical structures. This branch of semantics deals with the 'objects' - tangible elements like data structures, systemic variables, functions, or methods. Think of it as the compositional elements that give structures shape and functionality.
In contrast, epistemological semantics focuses on 'concepts,' analyzing contextual terms and relationships to build knowledge. It mirrors an abstract idea's cultural or communal interpretation, where traditions and norms provide life and meaning to physical structures.
Here's a breakdown:
Morphological Semantics: Picture a complex machine. Every gear, lever, and pulley (the "Objects") has a specific role and structure, defined by its physical and functional attributes. Morphological semantics is the study of these tangible elements and their interconnected parts.
Example: In a sentence, words and their arrangements (the 'objects') carry specific meanings based on their structure and syntax.
Epistemological Semantics: Imagine the operator's manual explaining how to use the machine effectively or the operational training a worker undergoes. "Concepts" encompass this contextual enhancement, deriving understanding from contextual and subjective knowledge.
Example: In understanding a sentence, the meaning isn't just in the words but in the context, tone, and relationship between those words and the broader conversation or text.
These two intertwining branches constitute data semantics, offering a comprehensive semantic representation of tangible 'objects' and intangible 'concepts.'
Figure 1. Visualizing the Intersection of Objects and Concepts in Data Semantics.
Data Semantics: Semantics is the branch of linguistics and logic concerned with meaning, and in the context of data, it encompasses objects and concepts. Data semantics involves creating a systematic plan or arrangement for data and defining how it should be structured and interpreted to achieve specific objectives. It establishes a blueprint for understanding data, ensuring it is used effectively and aligned with the intended goals. Data semantics provides the guiding framework for data, focusing on purposeful representation and how it’s organized.
Morphological Semantics Demystified
Morphological semantics stands at the forefront of driving semantic interoperability, which is crucial to ensuring seamless and meaningful communication across diverse systems. Unlike its epistemological counterpart, decentralized morphological semantics focuses on 'objects,' representing them as layered structures composed of interoperable overlays. This nuanced representation is pivotal, transforming static structures into dynamic schemas that can seamlessly traverse distributed data ecosystems.
The harmonic interaction between disparate systems in data exchange is not just about transferring data but preserving its innate, objectual meaning. Decentralized semantics facilitates this by encapsulating that meaning within meticulously crafted overlays, each layer adding depth and richness to the semantic structure, ensuring the data's integrity remains unscathed during transfer. Every piece of textual information enhances the interpretability of the object. This process transforms primary data exchange into more profound, more meaningful communication. It aligns with the data's core nature and enables semantic interoperability at scale.
Figure 2. Visualizing an Object as a layered structure of interoperable overlays.
Overlays Capture Architecture (OCA) offers a decentralized metadata framework for defining the semantic composition of digital objects, enabling the creation of interoperable schemas and facilitating data harmonization.
The Epistemic Shortfall in Achieving Semantic Interoperability
In knowledge representation, "epistemic" pertains to knowledge acquisition and understanding. It is derived from the study of epistemology and involves the logic and concepts associated with how knowledge is processed and understood. An "epistemic shortfall" in semantic interoperability indicates a gap where traditional methods fail to represent and share complex knowledge across various computational systems effectively. This shortfall highlights a challenge in preserving the depth and breadth of knowledge when it is exchanged or transferred among diverse platforms.
Although epistemological semantics does not directly contribute to interoperability in a technical sense, it is essential for ensuring that the data’s meaning is accurately interpreted and utilized. This aspect is crucial in complex domains where data interpretation is as critical as data exchange. Even though uniform nodal relationships enhance clarity for generic comprehension, the inherent nature of knowledge representation does not drive semantic interoperability. While knowledge graphs reveal the connections between related entities, they obscure the data's depth and evolutionary journey.
While aiding in generic comprehension, pursuing nodal relationships must remain generic within knowledge graphs to ensure uniform comprehension of the subjective concept. OCA enables dynamic semantic interoperability by distinctly separating the relationships of textual elements within a schema. Epistemological semantics enriches context via the intricate relationships delineated in ontologies and knowledge graphs. However, morphological semantics facilitate semantic interoperability through the inherent properties of the objects rather than the abstract concepts that house them.
OCA: The Beacon of Interoperability
As a scalable architecture, OCA streamlines the creation of each overlay and the sum of their connected parts, ensuring rich and comprehensive object representation. This foundational step enhances object utilization within assigned knowledge graphs. It's not merely about establishing relationships but understanding each nodal entity's genesis, evolution, and contextual relevance. OCA elevates semantic interoperability, making it a stable architecture for those pursuing authentic, meaningful, and robust data integration and interpretation.
Conclusion
Enhanced Data Interpretation: OCA ensures a richer data interpretation, capturing the detailed nuances often missed by traditional epistemic models.
OCA, grounded in “Decentralized Semantics,” addresses the limitations of epistemic models that often miss the intricate details of the data they represent. OCA is a robust solution that achieves semantic interoperability by providing a metadata framework for separating structural tasks into task-specific objects that, when combined, provide a digital representation of a complex object while ensuring that those components remain intact during data exchange.
The integration of OCA at the point of data capture promises a future where data is abundant, meaningful, and context-rich. This enhanced capability is pivotal for the evolution of AI and cross-industry data communication, ensuring that collected data retains its intrinsic value and meaning.
That concludes Part 1 of this series. In Part 2, we'll examine the dynamic union of stemmatics and Directed Acyclic Graphs (DAGs) and their role in bolstering data integrity. We'll see how merging these time-honored methods with cutting-edge technology creates a strong foundation that verifies and preserves the authenticity of data. This innovative approach safeguards the evolutionary aspects of data integrity across digital platforms and enhances true semantic interoperability.
Link to Genuine Semantic Interoperability across Digital Systems - Part 2: Stemmatic Traceability
References
[1] Acquaviva, P. (2016). Morphological Semantics. In A. Hippisley & G. Stump (Eds.), The Cambridge Handbook of Morphology (Cambridge Handbooks in Language and Linguistics, pp. 117-148). Cambridge: Cambridge University Press. doi:10.1017/9781139814720.006
[2] Rattan, Gurpreet, and Journal of Philosophy Inc. “Epistemological Semantics beyond Irrationality and Conceptual Change.” Journal of Philosophy 111, no. 12 (2014): 667–88. doi:10.5840/JPHIL20141111244.