· Balisage Series on Markup Technologies· 0 citations· 1 references
TL;DR
This paper traces a personal and technical journey from the HTML Document Object Model of 1996 through XML, XSLT, XSD, XSD, XQuery, RDF, SHACL, and the neural systems of the present day, arriving at the holonic graph as the resolution of a tension that has persisted, largely unacknowledged, throughout the life of the markup community.
Abstract
Thirty years of working with markup languages, semantic web standards, and knowledge representation systems reveals a single recurring problem wearing many different masks: the problem of bounded, scoped, composable meaning. This paper traces a personal and technical journey from the HTML Document Object Model of 1996 through XML, XSLT, XSD, XQuery, RDF, SHACL, and the neural systems of the present day, arriving at the holonic graph as the resolution of a tension that has persisted, largely unacknowledged, throughout the life of the markup community. The holon — Arthur Koestler's term for a unit that is simultaneously a whole and a part of a larger whole — turns out to have been implicit in every major design decision the XML and semantic web stacks ever made. RDF 1.2, SHACL 1.2, and named graphs now provide the formal apparatus to make it explicit. The implications extend from knowledge governance to the grounding of large language models.
DGML — Document Graph Markup Language — is a semantic XML representation of business documents. Docugami, with Inveniam, is opening the format itself to the industry as a potential standard, with a reference implementation. Where raw extraction gives you fields, and structural markup gives you shape, DGML gives you meaning: tags that describe what each element
is
in its domain — a liability cap, an effective date, a payment obligation — not how it appeared on the page. DGML's headline property is cross-document tag consistency: a stable vocabulary across every document of a given type, making a corpus queryable without per-document prompt engineering. A four-layer architecture — semantic tagging, spatial grounding via pixel bounding boxes, cryptographic fragment-level attestation, and browser-native readability — makes DGML suitable for enterprise document AI: economical enough for an agent to process, and provable enough for its answer to be trusted. This paper introduces the format, its schema mechanism, and its packaging.
J. Paoli· Balisage Series on Markup Te...· 0 citations
A “separation of concerns” (between the generative model and the structured data it consults) is proposed as a principled, declarative approach to AI-assisted XML work.
E. Beshero-Bondar, M. Wright, M. R. Simons· Balisage Series on Markup Te...· 0 citations
LinkML is a unified framework for data and domain modeling that spans diverse formats and ecosystems: JSON, RDF, CSV, SQL, spreadsheets and more. However, until now it had only one fully-featured implementation, written in Python, which is limited in terms of performance, portability, and behavior consistency. This restricts LinkML's usability in settings such as real-time schema editing and enterprise server applications. To address these issues, we present LinkML-Scala: a robust, fast, and portable implementation of LinkML that covers the metamodel, runtime support, schema derivation, and generators for JSON Schema, SHACL, RDFS, and Table Schema. Written in Scala 3, it runs in the browser (JavaScript transpilation), on the JVM, and as native binaries. LinkML-Scala is distributed as an in-browser playground, a CLI application, a GitHub CI Action, and JVM / JavaScript libraries. In our benchmarks, it outperforms the Python implementation in every tested scenario, on average by 22.9--38.5x. We consider LinkML-Scala an important contribution toward increasing LinkML's adoption and we outline a plan for further work to ensure implementation interoperability and stability.
Piotr Sowinski, Kacper Grzymkowski, A. Plokhotnyuk· 0 citations
Coding agents have become the primary means of generating new code in many software projects, and the resulting velocity of changes makes keeping track of the reasons behind those changes challenging. This paper introduces MOOSEDev, a system designed to give coding agents structured, ontology-grounded project memory. The system captures architectural decisions, lessons, constraints, and rationales in a knowledge graph exposed to agents via a Model Context Protocol (MCP) interface. Records carry lifecycle status, provenance, and supersession links, queryable via MOOSE, a proprietary neurosymbolic engine that treats the symbolic layer as the primary reasoning substrate. We compared MOOSEDev against a production vector-memory tool on a neutral public corpus of 835 typed records. MOOSEDev returned the expected answer set essentially in full (0.98-1.00) on supersession, set-completeness, and negation questions, whereas the baseline's top-k retrieval surfaced between 6% and 27%. Conversely, relevance recall and token cost were largely equivalent between the two systems. We also describe a temporal commit-history bootstrap of our own codebase, a pre-registered live trial, and lessons learned.
This work targets a KG for Sophocles’ Antigone that supports two coupled uses: structured retrieval, through integrity and competency questions expressed in SPARQL over dramatic structure and interpretive annotations; and interactive exploration, through a lightweight read client that navigates lines across languages, shows scene context, and reports corpus statistics.
Databases are a crucial part of modern information systems, but interacting with them has traditionally required users to have some level of expertise in formal query languages. This barrier prevents non-technical users from getting the most out of the data that is stored. In this work, we propose a robust production oriented natural language interface that enables users to query document oriented Not Only SQL (NoSQL) databases (MongoDB) using natural language without requiring knowledge of complex query syntax. Unlike NL-to-SQL approaches, NL-to-MongoDB translation must handle evolving, nested document schemas and multi-stage aggregation pipelines, making schemaconsistent query generation and appropriate operator selection (e.g., match, group, lookup) error-prone. Our system addresses this via a modular agent pipeline comprising a Query Builder, Query Runner, and Response Synthesizer, combined with dual dynamic retrieval of relevant schema fragments and similar examples. This architecture allows for self-correction and efficient query generation at the enterprise scale. We evaluate our system on public NL-to-MongoDB benchmarks, including DocSpider and TEND, as well as proprietary datasets. Across DocSpider and TEND, our pipeline improves execution accuracy over prior baselines by up to 40% on DocSpider and 23% on TEND. These findings highlight the potential of our approach as an accessible and effective solution for natural language querying of document oriented NoSQL databases.
Saiteja Tallam, Rajesh Gupta Mummidisetti, Pratyusha Rasamsetty et al.· Annual International Compute...· 0 citations