PULSE: An Executable Contract Language for Spatiotemporal Knowledge Graph Engineering

Computer Science > Artificial Intelligence [Submitted on 26 Jul 2026] PULSE: An Executable Contract Language for Spatiotemporal Knowledge Graph Engineering Dongxu Yang, Ziyi Liang Knowledge graph engineering often distributes accepted state, observations, constraints, processes, and hypothetical scenarios across artifacts whose combined execution contract remains external. We present PULSE, an Object-Process-Methodology-inspired language that localizes four operational roles and their write effects in one typed runtime. Here, modes denote operational roles rather than modal or deontic logic. The implemented contract fixes evidence non-overwrite, branch isolation, grounded multi-subject timers, guarded state change, and declaration-ranked event ordering over time and space; an external runner still decides whether evidence becomes an authoritative move. GeoSPARQL, SOSA, and SHACL remain generated views. A core calculus gives an effect-confinement lemma and six safety properties. Lean 4 checks kernel analogues for positions, evidence, clocks, monitors, atomicity, and branch source retention; 88 tests, 3,534 bounded checks, and 32 Lean/Python runtime-kernel cases bound the implementation claim to the checked cases. First-author implementations of a standards composition and a separate Sismic statechart reproduce the tested cold-chain trace. Across 37,440 generated temporal traces, PULSE matches a separate workflow and distinguishes ten single-field mutants. On the complete NOAA IBTrACS since1980 subset it agrees with GEOS and an event sweep on 1,476,290 transition-zone pairs, including 4,800 sampled and 12,831 duration-qualified events. Project-specific GeoSPARQL probes measure interface coverage. Overall, the results support contract localization, safety arguments, and trace parity for the tested fragment; language superiority and usability remain outside the evaluation. Comments: 6 pages, 5 tables, 1 code listing; submitted to KGSWC 2026. Research artifact available under the Apache-2.0 license Subjects: Artificial Intelligence (cs.AI); Databases (cs.DB); Programming Languages (cs.PL) ACM classes: I.2.4; D.2.2; H.2.3 Cite as: arXiv:2608.02630 [cs.AI]   (or arXiv:2608.02630v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2608.02630 Focus to learn more Submission history From: Dongxu Yang [view email] [v1] Sun, 26 Jul 2026 17:44:05 UTC (23 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-08 Change to browse by: cs cs.DB cs.PL References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)