This work proposes 5W1H+Which, a semantic indexing design that separates content extraction from ontology binding, and distinguishes business valid time, system knowledge time, and operational traces, and uses dependency records to support binding revalidation and the maintenance of derived conclusions.
Abstract
Transforming raw data into queryable knowledge requires both early extraction of reusable information and explicit types, relations, and applicability conditions for particular tasks. If indexing selects content too early around a single business schema, later tasks may be unable to use information that was omitted. If the index retains only open-ended text, however, rule-based reasoning lacks checkable premises. We propose 5W1H+Which, a semantic indexing design that separates content extraction from ontology binding. The 5W1H questions organize source-grounded content units; Which points to versioned ontology elements and records mapping relations, scope, and validation status. Time, location, system environment, and participant roles are not merely retrieval labels: together, they constrain the contexts in which facts, bindings, and rules apply. Unbound content remains searchable, while bound content enters a formal reasoning path only after premise checks. The method further distinguishes business valid time, system knowledge time, and operational traces, and uses dependency records to support binding revalidation and the maintenance of derived conclusions. A worked example of migration from an on-premises server to a cloud environment illustrates the different treatment of world-state changes, ontology-version changes, and changes in rule applicability. We formulate three groups of falsifiable hypotheses concerning cross-task evidence coverage, control of contextual misuse, and incremental update cost. The planned evaluation includes a strong typed fact-graph baseline with the same evidence, temporal information, and budget, to test whether benefits arise from 5W1H organization, deferred binding, or additional information and engineering effort. The contribution is a testable indexing mechanism, not a claim to a new universal ontology or a demonstrated performance advantage.
A seven-stage graph-grounded pipeline that converts domain documents into a complete, auditable Web Ontology Language (OWL) Terminological Box (TBox) without any unconstrained generation step is presented, demonstrating that the pipeline produces stable, reusable domain representations from large document corpora.
Maruf Ahmed Mridul, A. Talukder, O. Seneviratne· 0 citations
This work builds four modules (for drug-discovery chemistry, materials science, machine learning, and mineral geochemistry) in the ArticleMiner framework, and evaluates them on 163 papers, including a new geochemistry benchmark with expert-curated ground truth.
Md Abrar Jahin, Craig A. Knoblock, Jay Pujara· 0 citations
The results support the feasibility of ontology-driven generation for static-classification systems, whereas arithmetic risk computation and temporal event processing remain better suited to complementary procedural technologies.
Borivoj Bogdanović, S. Nikolić· Computers· 0 citations
ChinaUnicom DataAgent (UniDataAgent) is presented, an ontology grounded system for reusable question-to-report analysis that separates semantic acquisition from online execution and has the potential to be replicated in other enterprises.
Yu-Tai Duan, Ya-Hui Zhao, Zhang-Ti Li et al.· 0 citations
OaK is presented, an ontology-as-a-kernel framework that dynamically constructs and refines task-oriented ontologies for LLM agents and shows that OaK improves standard LLM agents, strengthens evidence grounding, and boosts the reliability of multi-step reasoning.
Xiaohui Zhang, Ze-Qun Sun, Cheng Yang et al.· 0 citations
This paper describes the ingestion and ontology-tagging layer that turns a validated extraction stream into a knowledge graph of 537,157 entities and 2,198,567 relationships drawn from 98,795 government documents, and describes a record-identity ladder that decides sameness from identifier columns, name columns, displa...
Vaibhav Dangaich, Kevin Lewis, Kundeshwar Pundalik· 0 citations