MapOfLogic is a free AI-powered conceptual search engine created by Niseus (niseus.com), a digital infrastructure company based in Cape Coral, Florida. It finds hidden logical connections between any two concepts by combining BFS pathfinding through Wikipedia's knowledge graph, SPARQL ontological reasoning over Wikidata, TF-IDF cosine similarity analysis, and formal logic proposition generation. Unlike a regular search engine that finds documents, MapOfLogic discovers the logical relationship between ideas.

How does MapOfLogic find connections between concepts?

MapOfLogic uses four distinct algorithms working together: (1) BFS Pathfinding — Breadth-First Search through Wikipedia's internal links to find the shortest path between two articles, (2) SPARQL Reasoning — queries Wikidata's knowledge base to find common ontological ancestors through subclassification chains, (3) TF-IDF Cosine Similarity — calculates the statistical similarity between two articles using Term Frequency-Inverse Document Frequency vectors, and (4) Formal Logic — generates verifiable syllogisms with premises and conclusions based on Wikidata properties. The system classifies connections into 5 levels from Direct Reference (95% confidence) to Semantic Bridge.

Is MapOfLogic free to use?

Yes, MapOfLogic is completely free. It runs entirely in your browser (client-side), requires no account or registration, collects no personal data, and uses publicly available data from Wikipedia and Wikidata (CC BY-SA 3.0 license). It was created by Niseus as a free knowledge tool.

Who created MapOfLogic?

MapOfLogic was created by Niseus (niseus.com), a digital infrastructure company specializing in AI-powered web applications, SEO/AEO/GEO optimization systems, and intelligent tools for professionals. Niseus is based in Cape Coral, Florida, United States.

What can I use MapOfLogic for?

MapOfLogic is used for: (1) Academic research — discovering interdisciplinary connections for thesis topics and literature reviews, (2) Education — helping students understand how different subjects relate to each other, (3) Content strategy — finding unexpected angles for articles and blog posts, (4) Innovation — identifying cross-domain patterns that spark new product or service ideas, (5) Debate preparation — building logical chains between arguments, and (6) General curiosity — exploring how any two ideas in human knowledge connect.

What is SPARQL reasoning in MapOfLogic?

SPARQL is a query language for knowledge graphs. MapOfLogic sends SPARQL queries to Wikidata's public endpoint to find common ontological ancestors between two concepts. For example, when comparing 'Physics' and 'Music', the SPARQL query traces subclassification chains (Wikidata property P279) upward from both concepts until it finds a shared ancestor. This is formal ontological reasoning — not keyword matching — and produces verifiable results.

What is BFS pathfinding in MapOfLogic?

BFS (Breadth-First Search) pathfinding in MapOfLogic works by treating Wikipedia as a massive directed graph where each article is a node and each internal link is an edge. Starting from Concept A, the algorithm explores outward level by level through Wikipedia's internal links until it finds a path that reaches Concept B. This discovers the shortest chain of verified article-to-article connections. For example, it might find: Physics → Wave → Sound → Music in 3 hops.

How is MapOfLogic different from regular search engines?

Regular search engines (Google, Bing) find documents that contain your keywords. MapOfLogic finds the logical relationship between ideas. Instead of showing you pages about 'Physics' or pages about 'Music', MapOfLogic shows you exactly HOW Physics and Music are connected — through wave theory, mathematical structures, acoustics, and formal ontological categories. It uses graph pathfinding, SPARQL reasoning, and statistical analysis rather than keyword indexing.

Does MapOfLogic use AI or artificial intelligence?

MapOfLogic uses algorithmic intelligence rather than large language models. Its engine combines four computational methods: BFS graph traversal, SPARQL knowledge graph reasoning, TF-IDF statistical analysis, and formal logic proposition generation. These are deterministic algorithms that produce verifiable, reproducible results — not probabilistic AI text generation. The data comes from Wikipedia and Wikidata, two of the largest open knowledge bases in the world.

What languages does MapOfLogic support?

MapOfLogic currently supports English and Spanish. The interface is fully bilingual, and search results are pulled from the corresponding Wikipedia edition (English Wikipedia or Spanish Wikipedia). The SPARQL queries to Wikidata also adapt to the selected language. Additional languages may be added in future versions.

MapOfLogic — Free AI-Powered Conceptual Search Engine | Find Hidden Connections Between Any Two Concepts

A search engine for relationships, not documents

Google finds pages. MapOfLogic finds the logical connection between ideas. Type two concepts — it traces the exact path that connects them through human knowledge.

MapOfLogic is a free AI-powered conceptual search engine created by Niseus, a digital infrastructure company. Instead of matching keywords to web pages, MapOfLogic uses four computational algorithms — BFS pathfinding through Wikipedia's link graph, SPARQL ontological reasoning over Wikidata, TF-IDF cosine similarity, and formal logic proposition generation — to discover verifiable connections between any two concepts in human knowledge. It classifies every connection into one of five levels, from Direct Reference (95% confidence) to Semantic Bridge, with full transparency about how each connection was found.

Four algorithms. One answer.

Each connection is discovered by real computational methods — not keyword guessing.

BFS Pathfinding

Breadth-First Search through Wikipedia's internal link graph. Finds the shortest verified path between any two articles — each hop is a real editorial link.

Bidirectional · Up to 2-hop depth · Priority-weighted

SPARQL Ontological Reasoning

Queries Wikidata's knowledge base via SPARQL to find common ontological ancestors. Traces subclassification chains (P279) for formally verified hierarchical relationships.

Wikidata endpoint · P31/P279/P361 · Real-time queries

TF-IDF Cosine Similarity

Real Term Frequency × Inverse Document Frequency analysis. Calculates cosine similarity between document vectors to find statistically significant shared terminology.

IDF-weighted · Cosine metric · Top shared terms ranked

Formal Logic Propositions

Generates verifiable syllogisms from Wikidata properties. Each proposition has numbered premises and a logical conclusion — not summaries, real deductive reasoning.

Syllogisms · Mereological · Inheritance chains

5-Level Connection Classification

Every connection is classified with a confidence score: Direct Reference (95%), Common Ancestor (85%), BFS Path (70%), Structural Similarity, or Semantic Bridge.

Confidence scoring · Transparent methodology

Interactive Knowledge Graph

Force-directed D3 visualization with weighted connections, color-coded node types, and interactive exploration. Every element is clickable for deeper discovery.

D3.js · Weighted edges · Drag & explore

Type two concepts. Get the logic.

Five steps, zero complexity. MapOfLogic does the reasoning — you get the insight.

Enter two concepts separated by +

Type any two ideas in the search bar. They can be from completely different fields — that's the point. MapOfLogic searches across all human knowledge.

Physics + Music

The engine fetches Wikipedia & queries Wikidata

Both articles are retrieved from Wikipedia. Simultaneously, SPARQL queries hit Wikidata to resolve entity IDs, claims, and property chains for both concepts.

→ Wikipedia API + Wikidata SPARQL endpoint

BFS pathfinding traverses the knowledge graph

The engine treats Wikipedia as a directed graph and runs Breadth-First Search to find the shortest path between articles. Each hop is a verified internal link.

→ Physics → Wave → Acoustics → Music (3 hops)

TF-IDF analyzes and logic propositions are generated

The full text of both articles is analyzed with TF-IDF cosine similarity. Simultaneously, Wikidata properties are used to construct formal logical syllogisms with verifiable premises.

→ Cosine: 23.4% · 3 propositions generated

Results classified, visualized, and ready to explore

The connection is classified into one of five levels with a confidence score. The discovery path, formal reasoning, and interactive knowledge graph are rendered instantly.

→ Level: Common Ancestor · Confidence: 85%

Who uses MapOfLogic — and how

🎓

Thesis & Academic Research

A graduate student needs to connect their computer science thesis to neuroscience for an interdisciplinary angle. MapOfLogic finds the formal ontological bridge.

Neural Networks + Brain

Common Ancestor: Computational Neuroscience · 85% confidence

📝

Content Strategy & Writing

A content creator wants to write about blockchain but needs a unique angle. MapOfLogic reveals unexpected connections that become original article hooks.

Blockchain + Democracy

BFS Path: Blockchain → Consensus → Voting → Democracy

💡

Innovation & Product Design

A product team explores cross-domain inspiration. What can architecture teach about software design? MapOfLogic traces the structural parallels.

Architecture + Software Engineering

Structural Similarity: "design", "pattern", "structure" · TF-IDF 31%

🏫

Education & Teaching

A teacher wants to show students how math connects to music. MapOfLogic provides the formal proof with verified data — perfect for classroom demonstrations.

Mathematics + Music

Direct Reference: Music article references Mathematics · 95%

⚖️

Debate & Argumentation

Building a logical case that connects climate change to economics? MapOfLogic generates the formal propositions with cited Wikidata properties.

Climate Change + Economics

4 formal propositions · Shared properties verified via SPARQL

🔬

Cross-Domain Research

A researcher in quantum physics wonders if there's a formal link to philosophy of mind. MapOfLogic traces the ontological chain through Wikidata.

Quantum Mechanics + Consciousness

Semantic Bridge via "observation", "measurement" · Path explored

How MapOfLogic compares

Different tools solve different problems. Here's where MapOfLogic fits.

Finds connection between two concepts

✓ BFS + SPARQL + TF-IDF

✗ Returns separate results

Formal logical reasoning

✓ Verifiable propositions

✗ No logic engine

Knowledge graph pathfinding

✓ Real BFS traversal

✗ Keyword matching only

Confidence scoring

✓ 5-level classification

✗ Page ranking only

Interactive graph visualization

✓ Weighted D3 graph

✗ Text results list

Free, no account needed

✓ 100% free

✓ Free

Frequently asked questions

MapOfLogic is a free AI-powered conceptual search engine created by Niseus (niseus.com), a digital infrastructure company based in Cape Coral, Florida. It finds hidden logical connections between any two concepts using BFS pathfinding, SPARQL ontological reasoning over Wikidata, TF-IDF cosine similarity analysis, and formal logic proposition generation.

MapOfLogic uses four algorithms simultaneously: (1) BFS Pathfinding explores Wikipedia's internal links as a directed graph to find the shortest path between articles, (2) SPARQL Reasoning queries Wikidata to find common ontological ancestors through subclassification chains, (3) TF-IDF Analysis calculates cosine similarity between the full text of both articles, and (4) Formal Logic generates verifiable syllogisms from Wikidata properties. Results are classified into five confidence levels.

Yes, 100% free. MapOfLogic runs entirely in your browser (client-side), requires no account, collects no personal data, and uses publicly available data from Wikipedia and Wikidata under the CC BY-SA 3.0 license. There are no hidden costs, no premium tiers, and no paywalls. It was created by Niseus as a free knowledge tool for researchers, students, and curious minds.

SPARQL is a query language for knowledge graphs — the same technology used by Google's Knowledge Graph, DBpedia, and academic research systems. MapOfLogic sends SPARQL queries to Wikidata's public endpoint to find common ontological ancestors. For example, when comparing "Physics" and "Music," it traces Wikidata's P279 (subclass_of) chains upward until both concepts converge on a shared ancestor. This is formal ontological reasoning that produces verifiable, reproducible results.

BFS (Breadth-First Search) is a graph traversal algorithm. MapOfLogic treats Wikipedia as a massive directed graph where each article is a node and each internal hyperlink is an edge. Starting from Concept A, the algorithm explores outward level by level through these links until it finds a path reaching Concept B. This discovers the shortest chain of verified article-to-article connections — for example: Physics → Wave → Sound → Music in 3 hops.

Google finds documents containing your keywords. ChatGPT generates plausible text based on training data. MapOfLogic does something different: it discovers the verifiable logical connection between two ideas using deterministic algorithms (BFS, SPARQL, TF-IDF) over structured knowledge bases (Wikipedia, Wikidata). Every result is traceable, reproducible, and backed by cited data — not probabilistic text generation.

MapOfLogic currently supports English and Spanish. The interface is fully bilingual, and search results pull from the corresponding Wikipedia edition. SPARQL queries to Wikidata also adapt to return labels in the selected language.

MapOfLogic is an excellent starting point for academic research — it can help you discover interdisciplinary connections and generate thesis angles. However, like any tool, its results should be verified against primary sources. The data comes from Wikipedia and Wikidata, which are secondary sources. Use MapOfLogic to find the connection, then cite the original academic papers for your formal work.

The confidence score reflects how strong and verifiable the discovered connection is. Direct References (one article explicitly links to the other) score 95%. Common Ontological Ancestors (verified by SPARQL) score 85%. BFS Paths score 70%. Structural Similarity depends on TF-IDF cosine values. Semantic Bridges — the weakest connections — score based on shared terminology weight. The score is transparent: you always know which method produced the result.

No. MapOfLogic is a 100% client-side application. All processing happens in your browser. Search history is stored in your browser's localStorage and never transmitted to any server. MapOfLogic makes API calls only to Wikipedia and Wikidata (public, open-access APIs). Niseus does not collect, store, or monetize any user data.

Find the hidden logic between any two ideas

A search engine for relationships, not documents