Human beings, cognative power, knowledges, history, Mathmatics, theologies, sciences, Concepts, names, noun, relations, connection, domains, categories, list, entities, Subjects, verbs, Object, sov, # Knowledge Graphs Real Knowledge Graph ArchitecturesWhile visual tools draw boxes and arrows, actual Knowledge Graphs are built using data structures that machines can query, traverse, and reason with. Here are the three main ways they are implemented in the real world. 1. The Property Graph (The Industry Standard) Tooling: Neo4j, Amazon Neptune, Apache TinkerPopQuery Language: Cypher or Gremlin This is the most popular format for enterprise software. In a Property Graph, both Nodes (entities) and Edges (relationships) can hold complex data payloads (properties/attributes). Instead of just drawing a line between SpaceX and Starlink, a Property Graph stores detailed data inside that line. Example (Cypher Query Language for Neo4j): // 1. Create the Nodes with Properties CREATE (spacex:Organization {name: 'SpaceX', founded: 2002, ceo: 'Elon Musk'}) CREATE (starlink:Service {name: 'Starlink', type: 'Satellite Internet'}) CREATE (china:Location {name: 'China', region: 'Asia'}) // 2. Create the Relationships (Edges) with their own Properties CREATE (starlink)-[:OPERATED_BY {launched_year: 2019}]->(spacex) CREATE (starlink)-[:RESTRICTED_IN {reason: 'Regulatory', enforcement: 'Strict'}]->(china) You can ask this graph questions like: "Find me all Services operated by Companies founded after 2000 that are restricted in Asia." 2. The Semantic Web (The Academic/Linked Data Standard) Tooling: Protégé, RDF, OWL, GraphDB Query Language: SPARQL This is the W3C standard (often called the Semantic Web). It breaks all knowledge down into strict "Triples": Subject -> Predicate -> Object. It is highly standardized so that different knowledge graphs on the internet can merge together seamlessly. It often uses a syntax called Turtle (.ttl). Example (Turtle Syntax): @prefix ex: . @prefix rdf: . # Defining the entities and their types ex:SpaceX rdf:type ex:Company . ex:Starlink rdf:type ex:SatelliteNetwork . ex:China rdf:type ex:Country . # Defining the relationships (Triples) ex:Starlink ex:operatedBy ex:SpaceX . ex:SpaceX ex:foundedBy "Elon Musk" . ex:Starlink ex:hasCoverageStatus ex:Restricted ; ex:inLocation ex:China . It's Semantic graphs support reasoning. If you define a rule that says "Any Satellite Network is a type of Telecommunications," the database automatically infers that Starlink is a Telecommunications entity without you explicitly writing it. 3. Web-Embedded Knowledge (JSON-LD) Tooling: Google Search, SEO Tools, Web Pages If you have ever Googled a company, movie, or recipe and seen a detailed "Knowledge Panel" on the right side of the search results, you are looking at Google's Knowledge Graph. Websites feed data into Google's graph using a format called JSON-LD hidden in the background of their HTML. Example (JSON-LD): { "@context": "https://schema.org", "@type": "Product", "name": "Starlink", "description": "High-speed, low-latency broadband internet.", "brand": { "@type": "Organization", "name": "SpaceX" }, "offers": { "@type": "Offer", "availability": "https://schema.org/InStock", "areaServed": [ { "@type": "Country", "name": "United States" }, { "@type": "Country", "name": "Japan" } ] } } This is a machine-readable format that search engines natively consume to understand exactly what a web page is about, turning the entire internet into a distributed Knowledge Graph. # history of knowledge graph Some early knowledge graphs were topic-specific. In 1985, Wordnet was founded, capturing semantic relationships between words and meanings – an application of this idea to language itself. In 2005, Marc Wirk founded Geonames to capture relationships between different geographic names and locales and associated entities. In 1998, Andrew Edmonds of Science in Finance Ltd in the UK created a system called ThinkBase that offered fuzzy-logic based reasoning in a graphical context.[8] In 2007, both DBpedia and Freebase were founded as graph-based knowledge repositories for general-purpose knowledge.[9] DBpedia focused exclusively on data extracted from Wikipedia, while Freebase also included a range of public datasets. Neither described themselves as a 'knowledge graph' but developed and described related concepts. In 2012, Google introduced their Knowledge Graph,[10] building on DBpedia and Freebase among other sources. They later incorporated RDFa, Microdata, JSON-LD content extracted from indexed web pages, including the CIA World Factbook, Wikidata, and Wikipedia.[10][11] Entity and relationship types associated with this knowledge graph have been further organized using terms from the schema.org[12] vocabulary. The Google Knowledge Graph became a complement to string-based search within Google, and its popularity online brought the term into more common use.[12] Since then, several large multinationals have advertised their use of knowledge graphs, further popularising the term. These include Facebook, LinkedIn, Airbnb, Microsoft, Amazon, Uber and eBay.[13] In 2019, IEEE combined its annual international conferences on "Big Knowledge" and "Data Mining and Intelligent Computing" into the International Conference on Knowledge Graph.[14] The development of large language models expanded interest in knowledge graphs as a way to structure information from unstructured text, with advances in language processing enabling their automatic or semi-automatic generation and expansion.[15][16][17] The term knowledge graph has since broadened to include the dynamically constructed and adaptive graph structures, which support retrieval, reasoning, and summarization in generative systems. Microsoft Research's GraphRAG (2024) exemplified this development by integrating LLM-generated graphs into retrieval-augmented generation. ## Using a knowledge graph for reasoning over data Main article: Ontology (information science) A knowledge graph formally represents semantics by describing entities and their relationships.[31] Knowledge graphs may make use of ontologies as a schema layer. By doing this, they allow logical inference for retrieving implicit knowledge rather than only allowing queries requesting explicit knowledge.[32] In order to allow the use of knowledge graphs in various machine learning tasks, several methods for deriving latent feature representations of entities and relations have been devised.[33] These knowledge graph embeddings allow them to be connected to machine learning methods that require feature vectors like word embeddings. This can complement other estimates of conceptual similarity.[34][35] Models for generating useful knowledge graph embeddings are commonly the domain of graph neural networks (GNNs). GNNs are deep learning architectures that comprise edges and nodes, which correspond well to the entities and relationships of knowledge graphs. The topology and data structures afforded by GNNs provide a convenient domain for semi-supervised learning, wherein the network is trained to predict the value of a node embedding (provided a group of adjacent nodes and their edges) or edge (provided a pair of nodes). These tasks serve as fundamental abstractions for more complex tasks such as knowledge graph reasoning and alignment. ## Obsidian Notes and Knowledge graph App The **Obsidian note graph** is a visualization feature that maps the relationships between your notes, displaying them as nodes connected by lines. It serves as a dynamic knowledge base map, helping users identify **hidden patterns**, **clusters of related ideas**, and **orphaned notes** (unlinked content). The graph operates in two primary modes: * **Global Graph:** Displays the entire vault, useful for spotting large-scale structural themes and disconnected notes. * **Local Graph:** Focuses on a specific note and its direct connections, ideal for exploring immediate context and refining links. Users can customize the view using **filters**, **tags**, and **groups** to highlight specific topics or reduce visual clutter. While some view it as a productivity tool for **Personal Knowledge Management (PKM)**, others use it primarily to discover **serendipitous connections** between disparate ideas or to audit the density and structure of their note-taking system. --- Obsidian draws its knowledge graph by parsing the **plain text** of your local Markdown files to identify links, then rendering them as a visual network using a physics simulation. It does not use a traditional database; instead, it treats your file system as the data source. ### Data Extraction: How It Gets Data The application builds the graph by scanning the **metadata cache** of your vault, looking specifically for two types of connections in the raw text: * **Wikilinks:** The primary data source. Any text enclosed in double brackets (e.g., `[[Note Name]]`) is instantly recognized as an edge connecting two nodes. * **Markdown Links:** Standard links (e.g., `[Text](File.md)`) are also parsed and converted into graph connections. Crucially, Obsidian only indexes links that exist in the **source code** of the file. It does not "render" the page to find links; therefore, links generated dynamically by plugins (like Dataview queries) or hidden in collapsed sections do not appear in the graph unless they are explicitly written in the Markdown text. ### Visualization: The Drawing Algorithm Once the data (nodes and edges) is extracted, Obsidian visualizes it using a **force-directed layout algorithm**. This simulation treats the graph like a physical system: * **Repulsion:** Every node (note) repels every other node, preventing them from stacking on top of each other and spreading them out. * **Attraction:** Links (edges) act like springs, pulling connected notes closer together. * **Gravity:** A central force pulls all nodes toward the center to keep the graph contained within the view. The final position of each note is determined by the equilibrium of these forces. Notes with many connections (high degree) naturally cluster in the center, while "orphaned" notes with few links drift to the periphery. Users can adjust the strength of these forces (Repel, Attract, Center) to tighten or loosen the cluster. ## topology please explain the topology of mathematics , quote from wikipedia: The term "topology" also refers to a specific mathematical idea central to the area of mathematics called topology. Informally, a topology describes how elements of a set relate spatially to each other. The same set can have different topologies. For instance, the real line, the complex plane, and the Cantor set can be thought of as the same set with different topologies. Formally, let X be a set and let τ be a family of subsets of X. Then τ is called a topology on X if:[15] Both the empty set and X are elements of τ. Any union of elements of τ is an element of τ. Any intersection of finitely many elements of τ is an element of τ. If τ is a topology on X, then the pair (X, τ) is called a topological space. The notation Xτ may be used to denote a set X endowed with the particular topology τ. By definition, every topology is a π-system. --- # How Obsidian draw graph Obsidian draws your knowledge graph by treating every note as a **node** and every link as an **edge**, rendering this network using a **force-directed layout** algorithm that pushes unconnected nodes apart while pulling linked nodes together. It gets its data directly from your **local Markdown files**, parsing `[[wikilinks]]`, tags, and folder structures in real-time without requiring a central database or cloud connection. ## How Obsidian Gets Data The application functions as a local-first file explorer that indexes your "vault" (a specific folder on your computer). When you open the graph view, Obsidian performs the following data extraction steps: It recursively scans the vault directory for `.md` files and reads their content to identify connections. The primary data source is the **wiki-link syntax** (`[[Note Name]]`), which automatically creates a bidirectional edge between the current note and the target note. Additionally, it parses **tags** (e.g., `#topic`) and treats them as nodes if configured, allowing you to visualize connections based on topics rather than just explicit links. The system also analyzes **unlinked mentions**, identifying text that matches existing note titles even if you haven't created a link yet, offering suggestions to strengthen the graph structure. All this data resides in a local index (`.obsidian` folder) that updates instantly when you save a file, ensuring the graph reflects your current knowledge state without latency. ## How Obsidian Draws the Graph Once the data is extracted, Obsidian uses a physics-based simulation to visualize the relationships. The core mechanism is a **force-directed graph** algorithm where nodes repel each other like magnets to prevent overlapping, while edges act as springs that pull connected notes closer. The visual representation relies on specific metrics to organize the chaos of thousands of notes. **Node size** often corresponds to the number of connections (degree centrality), making "hub" notes appear larger. **Color coding** can be applied based on file path, tags, or connection density to distinguish different clusters or "communities" of thought. The layout is **interactive and dynamic**; you can drag nodes to manually adjust the view, and the physics engine will recalculate the positions of neighboring nodes in real-time. For large vaults, Obsidian optimizes performance by rendering only visible nodes and simplifying the geometry of distant clusters, ensuring smooth navigation even with tens of thousands of notes.