御社殿の改修工事が無事終了いたしました。美しくなった御社殿にてご祈祷のご奉仕を承っております。この事業においても多くの方々から御奉賛を賜り、誠にありがとうございました。工事の詳細は特設ページをご覧ください。 hongo-hikawa.jp/info/repair/

🦉 Meet SWOWL — a open-source, web-based OWL & SWRL ontology editor. Runs in one docker compose up. 👇 github.com/MyShivaRepo/swowl #OWL #SWRL #SemanticWeb #Ontology #KnowledgeGraph #OpenSource

Destiny and that fraud really were about to serve swrl to the audience before it was acceptable 😭😭

rockstar hordepointe duaghter or swrl ptrs spray son?

OntoBricks: Digital Twin Builder for Databricks, bridging the gap between your Lakehouse and Semantics OntoBricks is a web application that transforms Databricks tables into a materialized knowledge graph. It lets you design ontologies (OWL), map them to Unity Catalog tables via R2RML, materialize triples into a Delta triple store and graph DB, reason over the graph (OWL 2 RL, SWRL, SHACL), and query it through an auto-generated GraphQL API MCP The problem: your business knowledge lives in dozens of Unity Catalog tables. Relationships are implicit, semantics are buried in column names, and no LLM agent can reason over it natively. The idea behind OntoBricks is to let an ontology describe what your data "means", then materialize it as a knowledge graph you can query, reason over, and expose to AI agents (via MCP tool), using W3C and industry standards. What you can do with it: * Design ontologies visually (OWL) or import industry standards — FIBO, CDISC, IOF * Auto-map ontology entities to Unity Catalog tables via R2RML, generated by an LLM * Materialize triples into a Delta-based triple store (or LadybugDB) * Reason over the graph — OWL 2 RL inference, SWRL rules, SHACL validation * Query through an auto-generated GraphQL API, or explore an interactive graph UI * Plug it into Cursor, Claude Desktop, or the Databricks Playground via MCP The whole pipeline — Import Metadata → Generate Ontology → Auto-Map → Synchronize — runs in four clicks, powered by LLMs. It runs natively as a Databricks App. Data and governance stay in your Lakehouse with Unity Catalog. Open source, built on FastAPI Databricks Apps. By Benoît Cayla. github.com/databrickslabs/on… #Databricks #KnowledgeGraph #DigitalTwin #Ontology #SemanticWeb #DataEngineering #GraphRAG #MCP #OpenSource #EmergingTech Connected Data London 2026 has been announced! 11-12 November, Leonardo Royal Hotel London Tower Bridge 📝 connected-data.london/post/c… Join us for all things #KnowledgeGraph #Graph #analytics #datascience #AI #graphDB #SemTech #Ontology 🎟 Ticket sales are open. Benefit from early bird prices with discounts up to 30%. 2026.connected-data.london?u… 📺 Sponsorship opportunities are available. Maximize your exposure with early onboarding. Contact us at info@connected-data.london for more.

Two new #TheHobby trademark applications to talk about today: SWRL GRADING and the below design for JOEYHOLD.

#今日のボカロ紹介 タグ使ってみるか 【鏡音リン】 Swrl　 【オリジナル】 nicovideo.jp/watch/sm2792589…

Princess Swrl.. That's my favorite hair🥹

FRCTL Audio updates SWRL movement effect plugin to v1.1.0 rekkerd.org/frctl-audio-upda…

【無料】FRCTL Audio「SWRL Lite」｜滑らかなうねりを生むモジュレーションプラグイン azu-soundworks.net/【…

Plugin Offers Six Characters Of Movement:FRCTL releases SWRL: sonicstate.com/news/2026/02/…

【無料】FRCTL Audio 「SWRL Lite」無償配布！ 6種のFXを1つのノブで 同時コントロールできる シンプルモーション系エフェクトです！ 参考デモあり dtmer.info/swrl-lite/

🎁無料配布🎁 ノブひとつで音に命を吹き込むモーションエフェクト FRCTL Audio「SWRL Lite」がリリース & 無料配布‼️ ✅6種類のモーション（Chorus / Reverb / Tremolo ほか） ✅ゼロレイテンシー ✅超軽量CPU projectofnapskint.com/swrl-l… #DTM無料情報

#swsolar #sterlingwilson #SWRL Q3 2025-26 Press Release. Full year order inflow guidance revised to 11000 cr from 8000Cr.

G-Dragon's Peaceminusone Highball headlines new winter RTDs, including makgeolli seltzers, yerba mate cocktails, Greek mastiha, and large margarita cans. More launches from SWRL, CheChe, Salt Point, and Rancho La Gloria: bevnet.com/spirits/2026/new-…

A decade ago, while working on my PhD at NTUA, I became obsessed with a problem that plagues us to this day: The Dumb Client. Back then, if a developer wanted an app to perform a simple task—like buying a book—they had to hard-code every single step. If the API changed, the client broke. It was brittle, rigid, and unscalable. I proposed a solution called "DeepGraphs". My thesis was simple: Servers shouldn't just send data (JSON). They should send a map of affordances—logic that tells a software agent what it can do next. I imagined "generic agents" that could wander the web, discover new APIs, and figure out how to interact with them on the fly. Sound familiar? Fast forward to 2025. Anthropic releases the Model Context Protocol (MCP), and the tech world goes wild. When I read the documentation, I smiled. The architecture is almost identical to what I proposed in 2015-2017. * My "Digital Objects" are now MCP Resources. * My "Affordances" are now MCP Tools. * My "DeepGraph" is now the System Prompt. So, why didn't my version take over the world? I bet on the wrong engine. I tried to use Formal Logic (Semantic Web, SWRL, OWL) to power these agents. It was mathematically perfect but practically impossible to implement. Today, we use LLMs. We traded precision for probability, and that made all the difference. I wrote a detailed breakdown of how this architecture evolved, why the "Semantic Web" failed where AI succeeded, and what today's MCP agents are still missing (hint: it’s about safety). 👇 Read the full retrospective here: apilama.com/2026/01/14/how-i… #AI #ModelContextProtocol #Agents #TechHistory #SemanticWeb #Innovation #MCP

Structured Decomposition for LLM Reasoning: Cross-Domain Validation and Semantic Web Integration doi.org/10.48550/arXiv.2601.… Albert Sadowski, Jarosław A. Chudziak （Warsaw University of Technology） Rule-based reasoning over natural language input arises in domains where decisions must be auditable and justifiable: clinical protocols specify eligibility criteria in prose, evidence rules define admissibility through textual conditions, and scientific standards dictate methodological requirements. Applying rules to such inputs demands both interpretive flexibility and formal guarantees. Large language models (LLMs) provide flexibility but cannot ensure consistent rule application; symbolic systems provide guarantees but require structured input. This paper presents an integration pattern that combines these strengths: LLMs serve as ontology population engines, translating unstructured text into ABox assertions according to expert-authored TBox specifications, while SWRL-based reasoners apply rules with deterministic guarantees. The framework decomposes reasoning into entity identification, assertion extraction, and symbolic verification, with task definitions grounded in OWL 2 ontologies. Experiments across three domains (legal hearsay determination, scientific method-task application, clinical trial eligibility) and eleven language models validate the approach. Structured decomposition achieves statistically significant improvements over few-shot prompting in aggregate, with gains observed across all three domains. An ablation study confirms that symbolic verification provides substantial benefit beyond structured prompting alone. The populated ABox integrates with standard semantic web tooling for inspection and querying, positioning the framework for richer inference patterns that simpler formalisms cannot express.

$ICCT $AIAD $GPAK $AWIN $CNND $ARET $BRSHF $MVLA $NVOS $UPCO $TCHC $FUVV $SWRL conta.cc/4pherio

BLUEBERRY SWRL TRYNA GET SOME POPTUSSY

Jesse (Jessica) Cooper Tirado Large Hot 2 butter pecan swrl 2 tstd almond shot 3 cream - this shit is not that good, nobody knows why he orders it (Sometimes will make it iced for the season) Yes, he will ask for the receipt everytime and scan it into the app himself