Hypergraph Semantic Search Engine (HyperSearch)

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Expert Rating 2.3
morgan
Project Owner

Hypergraph Semantic Search Engine (HyperSearch)

Expert Rating

2.3

Overview

A flexible, powerful semantic search engine built entirely in MeTTa, demonstrating the language's unique capabilities in knowledge representation, pattern matching, and nondeterministic reasoning.

RFP Guidelines

Develop interesting demos in MeTTa

Complete & Awarded
  • Type SingularityNET RFP
  • Total RFP Funding $100,000 USD
  • Proposals 21
  • Awarded Projects 4
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SingularityNET
Aug. 12, 2024

Create educational and/or useful demos using SingularityNET's own MeTTa programming language. This RFP aims at bringing more community adoption of MeTTa and engagement within our ecosystem, and to demonstrate and expand the utility of MeTTa. Researchers must maintain demos for a minimum of one year.

Proposal Description

Project details

HyperSearch aims to develop a cutting-edge semantic search engine that goes beyond traditional keyword-based search, utilizing MeTTa's powerful knowledge graph and reasoning capabilities. The project will create a proof-of-concept (PoC) that showcases:
Fuzzy search capabilitiesSemantic relationship discovery
Contextual understandingDynamic knowledge graph traversal
Technical Approach
The search engine will leverage MeTTa's core strengths:
Atom-based knowledge representationComplex pattern matching
Nondeterministic reasoningDynamic knowledge graph manipulation
Key Technical Components
Knowledge Graph Construction

Develop methods to build and populate knowledge graphsSupport multiple input formats (text, structured data, etc.)
Enable dynamic graph expansion and modification
Search Mechanisms
Implement fuzzy matching algorithms
Create semantic similarity computationSupport multi-dimensional search queries
Provide explainable search results with reasoning paths
Flexibility and Extensibility
Modular design allowing easy integration
Support for custom domain-specific knowledge representationAbility to plug in different similarity and matching algorithms

Proposal Video

Not Avaliable Yet

Check back later during the Feedback & Selection period for the RFP that is proposal is applied to.

  • Total Milestones

    4

  • Total Budget

    $25,000 USD

  • Last Updated

    7 Dec 2024

Milestone 1 - Fuzzy Search Proof of Concept

Description

Develop basic fuzzy matching algorithm in MeTTaCreate test knowledge graph with sample data Implement initial search functionalityDemonstrate basic string and semantic similarity matching

Deliverables

Working fuzzy search prototype

Budget

$5,000 USD

Success Criterion

Working fuzzy search prototype

Milestone 2 - Semantic Search Enhancement

Description

Expand search capabilities to understand semantic relationshipsImplement advanced matching algorithms Add contextual understanding layerCreate methods for semantic distance calculation

Deliverables

Enhanced semantic search with relationship tracking

Budget

$10,000 USD

Success Criterion

Enhanced semantic search with relationship tracking

Milestone 3 - Knowledge Graph Construction

Description

Develop robust methods for knowledge graph populationCreate import mechanisms for various data sources Implement graph expansion and modification routinesAdd provenance and confidence tracking for graph entities

Deliverables

Flexible knowledge graph builder with multiple input support

Budget

$5,000 USD

Success Criterion

Flexible knowledge graph builder with multiple input support

Milestone 4 - Integration and Deployment

Description

Create modular API for search functionalityDevelop integration interfaces Implement basic security and access controlCreate documentation and usage examples

Deliverables

Deployable semantic search service

Budget

$5,000 USD

Success Criterion

Deployable semantic search service

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Expert Ratings

Reviews & Ratings

Group Expert Rating (Final)

Overall

2.3

  • Feasibility 3.3
  • Desirabilty 3.0
  • Usefulness 2.0
  • Expert Review 1

    Overall

    3.0

    • Compliance with RFP requirements 5.0
    • Solution details and team expertise 5.0
    • Value for money 2.0
    Unclear on team capabilities

    Promising project. Features like fuzzy matching and semantic discovery align with RFP goals. Clarity needed on implementing contextual understanding and similarity computation in MeTTa. Reasonable budget and feasible timeline, but stronger evidence of team expertise with MeTTa would improve confidence.

  • Expert Review 2

    Overall

    3.0

    • Compliance with RFP requirements 4.0
    • Solution details and team expertise 3.0
    • Value for money 3.0

  • Expert Review 3

    Overall

    1.0

    • Compliance with RFP requirements 1.0
    • Solution details and team expertise 1.0
    • Value for money 1.0
    This is a fantastic application to build but very far from a quick simple MeTTA demo, it's a whole big AI application that will take an order of magnitude more work than this proposer realizes.

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