Structured Data-derived Knowledge (SDK)

• SDK is a framework that extracts fine-grained, machine-interpretable semantics from structured, semi-structured, and unstructured data, representing them as knowledge graphs or RDF triples.
• It employs advanced workflows—including transformer-based NER, beam search, and ontology mapping—to accurately capture and curate explicit facts and relations.
• SDK enables FAIR principles and automated reasoning across domains by integrating quantitative metadata and standardized ontologies into scalable, interoperable knowledge artifacts.

Structured Data-derived Knowledge (SDK) encompasses the extraction, curation, representation, and deployment of machine-interpretable knowledge from structured, semi-structured, or unstructured data sources. SDK is formalized as a set of fine-grained, explicitly-typed facts and relations—often as nodes and edges in a knowledge graph (KG)—with semantics transparent to computational agents and downstream analytic workflows. SDK shifts the paradigm from static, narrative-centric documents or raw data tables to FAIR (Findable, Accessible, Interoperable, Reusable) knowledge artifacts that serve automated reasoning, analytics, and domain-agnostic knowledge discovery (D'Souza, 2022, Beheshti et al., 2016, Taheriyan et al., 2016).

1. Foundational Concepts and Formal Definitions

SDK is defined as the machine-interpretable, fine-grained semantic representation extracted from raw data sources, enabling automated reasoning via structures such as knowledge graphs, semantic triples, or formally instantiated ontologies (D'Souza, 2022).

• Core Semantic Primitives (STEM-NER): Abstract entity classes are formally defined as disjoint sets:
  • $E_{\mathrm{process}} = \{\,e \in A \mid e$ denotes a natural phenomenon or experimental activity$\}$
  • $E_{\mathrm{method}} = \{\,e \in A \mid e$ denotes a named procedure or technique$\}$
  • $E_{\mathrm{material}} = \{\,e \in A \mid e$ denotes a physical or digital entity used in experiments$\}$
  • $E_{\mathrm{data}} = \{\,e \in A \mid e$ denotes observations, measurements, or quantities$\}$
  • with $$A = E_{\mathrm{process}} \cup E_{\mathrm{method}} \cup E_{\mathrm{material}} \cup E_{\mathrm{data}}$$, and $E_i \cap E_j = \emptyset$ for $i \neq j$.
• Knowledge Graph Representation: SDK is operationalized as a labeled, directed graph $G=(E, R, T)$, with entities $E$, relations $R$, and triples $T \subset E \times R \times E$ (Wu et al., 2024). A triple $(h, r, t)$ encodes the fact that $h$ (head) is related to $t$ (tail) via relation $r$.
• RDF and Ontology-based SDK: Structured knowledge is also expressed as RDF triples (subject, predicate, object) or, in domain-specific design, as a Function-Behavior-Structure (FBS) ontology mapping attributes to ontology classes (Sahadevan et al., 2024).

SDK thus underpins both individual fact representation (semantic primitives, attribute-value pairs, triples) and large-scale, ontology-aligned knowledge graphs and is agnostic to the originating data modality.

2. Extraction and Curation Pipelines

SDK acquisition comprises multiple workflows tailored to the data source and target schema, typically operating in the following sequence:

• Text-derived SDK (scholarly abstracts, articles):
  • Preprocessing: Abstract extraction, deduplication, tokenization, and transformation to CoNLL or similar sequence labeling formats.
  • Annotation: Expert-guided schema (e.g., process/method/material/data) with established precedence and ambiguity resolution (D'Souza, 2022).
  • Named Entity Recognition: Transformer-based models (e.g., SciBERT), trained for fine-grained entity detection with token-level classification and cross-entropy loss.
• Structured Data Source Semantics:
  • Formalization: Attribute set $A=\{a_1,\,\ldots,a_n\}$ from data source $S$, semantic model sm$(S)$ as an acyclic graph over ontology classes and properties (Taheriyan et al., 2016).
  • Weighted Graph Construction: Nodes correspond to semantic types; edges are derived from known semantic models, candidate semantic types, and domain ontologies.
  • Top-$k$ Model Search: Beam search plus Steiner-tree minimization to connect attributes via minimal-cost, semantically supported paths.
  • User Feedback: Corrections incrementally refine the weighted graph and preferred semantic model space.
• Hybrid/Manual Integration (scientific tables, engineering data):
  • Metadata Extraction: Statistical summarization (mean, quantiles, frequencies) per column/field; JSON-formatted intermediate records (Mehta et al., 2023).
  • Domain Rule Application: Synthesis of new metadata fields by domain-specific logic (e.g., liquefaction flag via threshold tests).
  • Knowledge Graph Assembly: Instantiation of node and edge types per domain schema; transformation of tabular columns to Data nodes with attached statistics.
• Design Reasoning from Product Catalogues:
  • Rule-based FBS Classification: Attribute/value pairs mapped to Function, Behavior, or Structure nodes using keyword similarity, NER, measurement patterns, or explicit context classification (Sahadevan et al., 2024).
  • KG Population & Retrieval: Neo4j or equivalent graph DB schema; Cypher queries for attribute-based design specification retrieval.
• Software Artifact Mining:
  • AST-based Dataflow Analysis: Extraction of data loading, transformation, and output operations from code; validation against article text to constrain extractions to scholarly knowledge content (Haris et al., 2023).

SDK curation workflows leverage human expertise in schema definition and domain rules, but emphasize scalable automation via NLP, static code analysis, or ontology-based graph construction.

3. Knowledge Representation: Graphs, Ontologies, and Schemas

SDK can be rendered in several formal representations, each suited to data type and use-case:

• Entity Nodes and Edges: Each entity (e.g., “finite-element modelling”) becomes a graph node; relations (e.g., “hasMethod”) define labeled edges, forming a directed multigraph amenable to semantic search and inference (D'Souza, 2022).
• Attribute-driven Models: In software or design SDK systems, specific property names/values instantiate ontology classes, as in FBS design graphs where $r:\langle A,V \rangle \rightarrow T$ assigns a tuple to Function, Behavior, or Structure (Sahadevan et al., 2024).
• Statistical Summaries as Nodes: Numeric statistics or frequency distributions comprise Data nodes; links encapsulate the measurement context or the event generating the data (Mehta et al., 2023).
• RDF/OWL Templates: For semantic web applications, SDK is encoded as RDF triples or OWL templates, enabling use of standardized inference engines and semantic querying.

Further, SDK supports hierarchical and cross-domain abstraction via lightweight ontologies—initially defined ad hoc for process/method/material/data (D'Souza, 2022), generalized in domain-agnostic RDF triple schemas (Mehta et al., 2023), and formalized in application-specific ontologies such as FBS (Sahadevan et al., 2024).

4. Evaluation, Empirical Findings, and Analysis

Various metrics and analyses are employed to assess SDK quality and coverage:

• NER Model Performance: Precision, Recall, F1-score on gold-standard annotated corpora (e.g., 0.80/0.78/0.79 for STEM-NER) to justify silver-standard labeling at scale (D'Souza, 2022).
• Semantic Model Accuracy: Mean Reciprocal Rank (MRR) for attribute-type mapping, edge-set match precision/recall for full semantic models (Taheriyan et al., 2016).
• Knowledge Graph Content Statistics: Distributional counts across entity types (e.g., ‘material’ dominating in multiple STEM disciplines); entropy analysis to gauge lexical diversity and coverage (D'Souza, 2022).
• Alignment with Domain Phenomena: Cross-tabulation of metadata tags (e.g., liquefaction observed) versus raw data statistics to discover hidden domain relationships (Mehta et al., 2023).
• Design KG Rule Accuracy: Macro-averaged precision, recall, F1; in pilot studies, macro-F1 = 0.89 for FBS node classification (Sahadevan et al., 2024).
• Scalability and Coverage: Processed corpora sizes (e.g., 1M entities from 60K abstracts); entity and edge de-duplication rates; query response times in deployed graph DBs (D'Souza, 2022, Mehta et al., 2023).
• Qualitative Validation: Word-clouds of top entities per class/domain; examples illustrating cross-domain SDK expressivity.

Experimental results consistently establish that SDK extraction and knowledge graph methodologies achieve strong precision/recall for NER and attribute mapping, support cross-domain comparability, and deliver practical query and reasoning functionality over otherwise intractable data.

5. Applications, Limitations, and Future Directions

SDK directly enables advanced research, automation, and information retrieval across multiple scientific, engineering, and industrial domains.

• Applications:
  • Automated semantic search (process/method/material/data-centric).
  • Question answering, fact verification, and analytical reasoning over knowledge graphs.
  • Digital library enhancement, integration with platforms like ORKG.
  • Industrial design retrieval (FBS-based specification matching).
  • Cross-project data discovery, experiment similarity analysis, and detection of latent relationships.
• Limitations and Challenges:
  • Word-sense ambiguity and boundary cases between primitive classes (D'Souza, 2022).
  • Requirement for extensive domain rule definition or annotation schema tuning (Mehta et al., 2023, Sahadevan et al., 2024).
  • Need for richer, standardized relations and alignment with external ontologies.
  • Limited handling of complex, unstructured formats, and context-limited legacy data.
• Future Implications:
  • Extension of entity schemas to cover scientific tasks, objectives, and results.
  • Automated relation extraction and integration with external KGs (e.g., Wikidata).
  • Real-time annotation and feedback loops in digital libraries.
  • Scaling to petabyte-scale scientific repositories and hybrid unstructured/structured ingestion.

This trajectory positions SDK as a central substrate for next-generation scientific, design, and domain-agnostic knowledge synthesis, unlocking structured, FAIR knowledge across heterogeneous sources and enabling both automated and human-in-the-loop reasoning (D'Souza, 2022, Taheriyan et al., 2016, Sahadevan et al., 2024, Mehta et al., 2023).