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      "id" : "literal:SimpleModeling integrates BoK, literate models, DSL, and execution platforms to extend Harness Engineering into a foundation that governs execution based on meaning. It reduces gaps between specification and implementation and enables consistent quality and reproducibility required in the AI era.:en",
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      "id" : "literal:SimpleModeling.orgでは、BoK(Body of Knowledge)を構築・活用する一環として、用語集(Glossary)の運用を自動化しています。 :ja",
      "label" : "SimpleModeling.orgでは、BoK(Body of Knowledge)を構築・活用する一環として、用語集(Glossary)の運用を自動化...",
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      "id" : "literal:SimpleModeling.orgを支えるJSON-LD/RDFベースのナレッジグラフを、可視化デモで紹介します。:ja",
      "label" : "SimpleModeling.orgを支えるJSON-LD/RDFベースのナレッジグラフを、可視化デモで紹介します。",
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      "id" : "literal:SimpleModelingでは、Object、Knowledge、Literateの三つの構成要素からDomain、Use Case、Applicationの各適用モデルを構成します。ユースケースの意図をアプリケーション・モデルへ具体化するApplication Modelingと、承認済みの実行可能モデルをCML(Cozy Modeling Language)、Cozy、AI、Textusによって設計・実装・実行へ接続するModel Realizationを分けて扱います。 :ja",
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      "id" : "literal:SimpleModelingは、BoK・文芸モデル・DSL・実行基盤を統合し、Harness Engineeringを「意味に基づいて実行を制御する基盤」へと拡張する。これにより、仕様と実装のズレを抑え、AI時代に求められる一貫した品質と再現性を実現する。:ja",
      "label" : "SimpleModelingは、BoK・文芸モデル・DSL・実行基盤を統合し、Harness Engineeringを「意味に基づいて実行を制御する基盤」...",
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      "id" : "literal:The Semantic Integration Engine is a multifaceted semantic search engine that provides multiple interfaces, including REST, MCP, and ChatGPT WebSocket.\nAmong these, the REST API is positioned as the most general-purpose and stable interface for external exposure.\nIn this article, we demonstrated how to build a CLI on top of the REST API, enabling SIE’s query functionality to be used directly from the console and shell scripts.\nWe also confirmed the potential of combining this CLI with VS Code Codex and generative AI, allowing it to be used as a tool for acquiring external knowledge.\n:en",
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      "id" : "literal:The Semantic Integration Engine is designed as a RAG platform that integrates RDF, vector search, and knowledge graphs.In this article, with the goal of using SIE from VSCode via MCP, we present the assumed MCP specifications, architecture, a demo environment built with docker compose, and concrete examples of JSON-RPC–based MCP communication.\nAt present, because MCP registration on the VSCode side did not work as expected, verification is performed using a pseudo session; nevertheless, the article provides a concrete understanding of how SIE behaves as an MCP server for VSCode.\n:en",
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      "id" : "literal:The Semantic Integration Engine rebuilds BoK knowledge into AI-interpretable semantic data by integrating RDF, vector search, and graph retrieval. This article explains the architecture, startup procedure, query example, and interpretation of the returned JSON.:en",
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      "id" : "literal:This article examines how the premises of development processes are changing with the advent of generative AI, using the characteristics of the Unified Process as a comparative axis against agile development. In the AI era, not only programs but also natural-language artifacts such as models, specifications, and design documents become primary sources of truth. Under this premise, the Unified Process—designed as a model-centric framework—serves as a valuable reference for rethinking development processes that collaborate with AI.:en",
      "label" : "This article examines how the premises of development processes are changing ...",
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      "id" : "literal:This article explains the configuration for using the Semantic Integration Engine (SIE) from VSCode via MCP (Model Context Protocol). Following the REST and ChatGPT integrations, it introduces the design and demo setup for VSCode integration as an entry point for AI-assisted development and knowledge utilization in the local development environment.:en",
      "label" : "This article explains the configuration for using the Semantic Integration En...",
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      "id" : "literal:This article explains, at the protocol level, what happens when the Semantic Integration Engine and ChatGPT are integrated via MCP (Model Context Protocol),focusing on how ChatGPT uses SIE’s knowledge, performs reasoning, and generates the final response.It provides a detailed explanation from a protocol-level perspective.\nThe important point is that ChatGPT is not requesting “the answer itself” from SIE,but rather retrieving the materials and evidence it needs to reason on its own.\nIn this article, we recreate a simulated MCP session,and examine why SIE’s response structure—concept / passage / graph / score—has a high degree of affinity with the reasoning model of generative AI.\n:en",
      "label" : "This article explains, at the protocol level, what happens when the Semantic ...",
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      "id" : "literal:This article introduces the prototype Knowledge Graph Explorer developed for SimpleModeling.org. It visualizes the conceptual structure encoded in JSON-LD and RDF, allowing interactive exploration of domain entities and relations.:en",
      "label" : "This article introduces the prototype Knowledge Graph Explorer developed for ...",
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      "id" : "literal:This article summarizes the current state of my personal AI-driven development approach, in which ChatGPT and VS Code Codex are used selectively to rapidly iterate through specification, design, implementation, and verification.:en",
      "label" : "This article summarizes the current state of my personal AI-driven developmen...",
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      "id" : "literal:Using the Semantic Integration Engine from VSCode via MCP｜VSCodeからSemantic Integration EngineをMCP経由で利用する― An attempt to integrate a knowledge graph into the local development environment ―｜― ローカル開発環境に知識グラフを統合する試み ―:en",
      "label" : "Using the Semantic Integration Engine from VSCode via MCP｜VSCodeからSemantic In...",
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      "id" : "literal:Using the Semantic Integration Engine from VSCode via MCP｜VSCodeからSemantic Integration EngineをMCP経由で利用する― An attempt to integrate a knowledge graph into the local development environment ―｜― ローカル開発環境に知識グラフを統合する試み ―:ja",
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      "id" : "literal:前回の記事では、AI時代の開発プロセスの枠組みとして、Unified Processをプロセスの骨格とし、Component-Based Developmentを開発の中心構造として据えるという整理を行いました。\nUPは、反復・漸進 (Iterative & Incremental)、アーキテクチャ中心 (Architecture-Centric)、ユースケース駆動 (Use-Case Driven)という三つの基本原則によってソフトウェア開発プロセスを定義しています。\nこれらの原則はAI時代においても依然として有効です。\nしかしAIによるコード生成が一般化した環境では、それぞれの原則の意味や役割は従来とは少し異なる形で理解する必要があります。\n本稿では、Unified Process の三つの基本原則を手がかりに、AI時代における開発プロセスのあり方を改めて整理してみます。\n:ja",
      "label" : "前回の記事では、AI時代の開発プロセスの枠組みとして、Unified Processをプロセスの骨格とし、Component-Based Developm...",
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      "id" : "literal:本稿では、SimpleModeling.orgのために開発している「ナレッジグラフ・エクスプローラ」の試作版を紹介します。 JSON-LDやRDFに埋め込まれた概念構造を可視化し、ドメインのエンティティや関係を双方向に探索できます。:ja",
      "label" : "本稿では、SimpleModeling.orgのために開発している「ナレッジグラフ・エクスプローラ」の試作版を紹介します。 JSON-LDやRDFに埋め込...",
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      "id" : "literal:本記事では、Semantic Integration Engineと ChatGPT を MCP（Model Context Protocol）で連携させた場合に、ChatGPT がどのように SIE の知識を利用し、推論を行い、最終的な回答を生成しているのかをプロトコルレベルで詳しく解説します。\n重要なのは、ChatGPT が SIE に「答えそのもの」を求めているのではなく、自ら考えるための材料や根拠を取得しているという点です。\n本稿では、擬似的な MCP セッションを再現しながら、SIE が提供する concept / passage / graph / score という返却構造が、なぜ生成AIの推論モデルと高い親和性を持つのかを明らかにします。\n:ja",
      "label" : "本記事では、Semantic Integration Engineと ChatGPT を MCP（Model Context Protocol）で連携させ...",
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      "id" : "literal:本記事では、Semantic Integration Engine（SIE）を VSCode から MCP（Model Context Protocol）経由で利用する構成について解説します。 REST や ChatGPT 連携に続き、ローカル開発環境における AI 支援・知識活用の入り口として、VSCode 連携の設計とデモ構成を紹介します。:ja",
      "label" : "本記事では、Semantic Integration Engine（SIE）を VSCode から MCP（Model Context Protocol）...",
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      "id" : "literal:本記事では、Unified Process の特徴点を軸にアジャイル開発との比較を行い、生成AIの登場によって開発プロセスの前提がどのように変化しているのかを考察します。 AI時代においては、プログラムだけでなく、モデルや仕様書、設計文書といった自然言語の成果物が一次情報として扱われるようになります。 この前提のもとで、モデル中心に設計された Unified Process は、AIと協働する開発プロセスを考えるための有力な補助線となります。:ja",
      "label" : "本記事では、Unified Process の特徴点を軸にアジャイル開発との比較を行い、生成AIの登場によって開発プロセスの前提がどのように変化しているの...",
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      "id" : "literal:生成AIを単なるコード生成ツールとして使うのではなく、仕様検討の設計ドキュメント・ソースコード・検証可能仕様を三位一体で育てていく開発スタイルを紹介します。 AIを自由にさせすぎないための制約設計、人間が担うべき領域とAIに任せる領域の分離、そして知識を蓄積し再利用するためのドキュメント戦略が本稿の中心テーマです。:ja",
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      "id" : "literal:目的別の適用モデルから実行までをつなぐ一貫した開発の運用像 :ja",
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      "id" : "literal:近年、生成AIは自然言語のプロンプトからソースコードを生成できるまで進化してきました。これにより、従来手作業で行っていた実装の一部が自動化され、開発効率の向上が期待されています。:ja",
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