Applications and Use Cases

Applications and Use Cases#

Objective: Develop two vertical search engines to demonstrate the functionality of the open search infrastructure and to attract institutions to use the project’s infrastructure.

Core Vertical Applications:

  1. Search for Science

    • Purpose: Enables specific search for science-related information by interlinking different sources.

    • Features: Enrichment of documents with research-related qualitative information, temporal argument search, and a focus on computer science and physics domains.

    • Goal: Provide easy access to reliable scientific information for both scientists and citizens.

  2. Mobile Privacy-Preserving, Personalized Geo-Entity Recommendation

    • Purpose: Facilitates mobile search requests for specific location-based entities.

    • Features: Analysis of web pages/sites for geo-reference, geo-entity knowledge graph, user preference consideration, and integration of privacy and transparency models.

    • Target Audience: Citizens searching for specific local entities.

Comparison with General Search Engines: Vertical search engines, unlike Google or Bing, cater to specific domains and offer optimized search strategies. Examples include Amazon’s product search, LinkedIn’s people search, and Booking.com’s hotel search. While these commercial solutions exploit user data, the project focuses on privacy and transparency.

Key Project Features:

  1. Privacy Approach:

    • Protects personal data without compromising search engine functionalities.

    • Offers a proxy-free approach integrating both the client device and search application.

  2. Transparency Concept:

    • Provides insights into the search process and reasoning behind search results.

    • Users can alter the selection of transparent information, giving them control and thereby enhancing trust.

  3. Temporal Argumentation & Conversational Search Integration:

    • Allows users to search for documents containing specific arguments, especially relevant in scientific contexts.

    • Conversational search offers a human-centric approach, focusing on search conversations over query-list interactions.