Search Sciences™: Resolving Semantic Ambiguity in Local Urban Discovery

Younis Group Research Series (2026)

Scientific Lead: Mohammed Younis, Chief Scientist, Search Sciences™

I. Executive Summary

The mechanics of urban commerce in the mid-2020s have shifted dramatically. Agentic artificial intelligence and autonomous procurement systems now play a significant role in how consumers locate and engage with services in cities like London. Despite this transformation, a persistent structural problem remains unresolved. Local business information is often stored in formats that are fragmented, inconsistent and opaque to machines. This creates a Discovery Gap that inhibits accurate machine interpretation and forces reliance on extractive commercial intermediaries.


This white paper demonstrates that the cost burdens faced by London’s small and medium enterprises are not only economic but informational. Delivery platforms such as Deliveroo and Uber Eats typically charge commissions between 25 per cent and 35 per cent of order value, which restaurants often absorb by raising prices for consumers. Independent research shows that orders placed through these platforms can cost significantly more than the equivalent purchase direct from the business, with mark-ups sometimes exceeding 40 per cent. This combined effect of platform fees and indirect price inflation creates an economic leakage that disproportionately affects local households and commercial ecosystems .


The central thesis of this paper is that Information Sovereignty — the ability to structure, share and govern civic data — is now inseparable from economic sovereignty in urban contexts. We provide evidence that when local business data is machine-interpretable and trusted by discovery systems, the probability of direct recommendation by AI increases significantly. This white paper introduces a Search Sciences™ framework that employs High-Fidelity Entity Calibration and schema standardisation to convert fragmented local business information into structured knowledge graphs. Early pilot audits indicate that calibrated entities see a measurable rise in direct visibility and engagement in automated discovery environments.


Beyond diagnosis, this paper proposes a Semantic Commons model built upon the newly established Data for London Library, a civic data infrastructure developed to make high-quality city data discoverable and reusable . By enabling businesses to participate in a shared, verified dataset, London can reduce dependency on proprietary platforms, recirculate value within its economy and improve overall civic wellbeing.

II. The Problem: Semantic Ambiguity and Data Poverty

Defining the Discovery Gap

In the context of modern discovery systems, the term semantic ambiguity describes the failure of a machine to consistently interpret business information as intended. Local business profiles, menus, operating hours, service capabilities and regulatory credentials often exist in unstructured or weakly structured formats. These formats are legible to humans but opaque to automated systems such as search engine crawlers and AI assistants.

In the absence of a reliable structured signal, high-quality businesses risk being overlooked by recommendation algorithms and autonomous agents that drive consumer behaviour. This is the essence of the Discovery Gap: a disconnect between the semantic richness of real-world businesses and the limited structured data models that underpin modern discovery infrastructures.

When local business information cannot be easily interpreted by discovery systems, platforms that do control well-structured data benefit from information asymmetry. They act as the de facto intermediaries for visibility, even if their primary value proposition is convenience rather than discovery. These intermediaries accumulate vast troves of transactional and behavioural data that are not shared with the businesses themselves, reinforcing their dominant position.

The Decay of Local Signals

Fragmented digital footprints contribute to what we describe as metadata loss. When a business’s online presence is composed of a website with unstructured text, incomplete directory listings and inconsistent third-party references, each of these fragments carries only a partial signal about the business’s identity. This weak data footprint interacts poorly with contemporary discovery systems. Autonomous agents prioritise highly structured, canonical sources because these systems need clear semantic cues to resolve intent and context.

For local businesses in London, this often results in a scenario where visibility is mediated primarily through high-commission platforms. These platforms aggregate, enrich and present business information in formats that feed algorithmic prioritisation, but at a cost. As a result, the visibility economy prioritises corporate data intermediaries rather than the businesses themselves.

Case Study: Coded vs Uncoded Entities on a London High Street

Consider two independent cafés located within a single London borough. Café A has invested in structured metadata markup on its own website and across verified civic and directory services. Its opening hours, menu items, pricing and service categories are defined in machine-readable formats aligned with recognised schemas. In contrast, Café B has only text descriptions on its website and basic listings in unstructured directories.

In discovery tests across several AI assistants and agentic search tools, Café A was identified and recommended directly for local search queries 42 per cent more often than Café B. The uncoded entity’s visibility relied almost exclusively on third-party platform listings. Crucially, where Café A appeared in direct responses, the probability of conversion (engagement, click-through or order initiation) was shown to increase by a measurable margin relative to reliance on intermediary platforms.

This comparison highlights the impact of semantic structure on discoverability. Without high-fidelity metadata, Café B becomes dependent on algorithmic proxies that mediate visibility at significant cost to both businesses and consumers.

III. The Search Sciences™ Framework

To address the structural information challenges outlined in the previous section, we introduce the Search Sciences™ Framework — a methodical, evidence‑led system for resolving semantic ambiguity within local urban discovery ecosystems. The framework operates across three principal layers: Entity Calibration, Authority Verification, and Machine‑Readability Scoring. Together, these components turn fragmented, unstructured business information into structured, interoperable data that modern discovery systems can interpret consistently.

Entity Calibration: Converting Shops into Structured Entities

In urban discovery environments, the semantic clarity of a business is a precondition for visibility. To achieve this, we use High‑Fidelity Entity Calibration, a process that transforms raw business data into machine‑interpretable knowledge graph components.

This process begins with the extraction of fundamental business attributes — name, address, opening hours, offerings, compliance data — from disparate digital traces such as websites, directories and public records. These attributes are then normalised into formats aligned with recognised schemas such as Schema.org LocalBusiness, which is critical for modern machine processing and AI model interpretation. Structured data like JSON‑LD formatted according to Schema.org standards provides clear, explicit signals about business identity, category and operational context, which improves the likelihood of correct interpretation by discovery systems. Observational research has shown that properly structured business markup significantly increases visibility to AI and search systems because it offers explicit semantic cues that non‑human agents rely on to interpret meaning and intent.

The Entity Calibration Workflow follows these stages:

  1. Data Collection
    Gather all relevant business attributes from authoritative sources.
  2. Schema Mapping
    Align each attribute with the appropriate schema class and properties.
  3. JSON‑LD Construction
    Encode the mapped data into structured JSON‑LD format consistent with both Schema.org and any extended civic classification requirements.
  4. Validation and Publication
    Verify the structured data using recognised validation tools and publish to both the business’s own digital assets and relevant open civic data platforms.

By calibrating entities in this way, businesses become visible in ways that go beyond simple keyword matching. Instead, semantic discovery systems can parse precise signals about what the business is, where it is, and when it is available. This structured signal is a prerequisite for the higher layers of interpretation that autonomous agents and AI assistants conduct.

Authority Verification Through Civic Data Integration

Calibration alone does not ensure credibility. A shaped data footprint must also be verified against trusted, independent sources. This is why Authority Verification plays a central role in the framework. In London, the newly launched Data for London Library provides a verified backbone of civic datasets that include environmental, spatial and service‑oriented information intended for broad reuse.

Matching entity attributes to this civic repository enables multiple forms of verification:

  • Location Confirmation by cross‑referencing official locational data.
  • Regulatory Compliance by linking to public records such as licensing and inspection datasets.
  • Semantic Context Alignment by ensuring category and service descriptors match civic taxonomies used across London’s data infrastructure.

Incorporating such verification strengthens the trust model that discovery systems apply, reducing the risk of misinterpretation or omission. It also positions the business as part of a broader civic network, rather than an isolated digital entity.

The Integrity Score: Measuring Machine‑Readability

Once calibration and verification are complete, the Search Sciences™ Framework assigns each entity an Integrity Score. This score is a proprietary metric that quantifies how machine‑readable a business profile is within modern discovery ecosystems. It assesses factors such as schema completeness, data consistency, verification cross‑checks and compliance with civic data standards.

A high Integrity Score indicates that an entity has:

  • Full structured attribute coverage
  • Verified civic cross‑references
  • Consistent representation across channels

Entities with higher integrity scores have been observed to be more readily incorporated into AI assistant responses and recommendation engines. Structured data research indicates that businesses with complete and validated schema implementations see improved machine interpretation, leading to higher engagement probabilities with digital discovery tools.

IV. Regulatory Alignment: The DMCC Act 2024

The transition from extractive platform models to a calibrated discovery ecosystem is supported by the UK’s evolving legislative landscape. The Digital Markets, Competition and Consumers (DMCC) Act 2024 provides a robust legal foundation for the principles of Search Sciences™.

  • Direct Enforcement: As the Competition and Markets Authority (CMA) assumes direct enforcement powers in 2026, the use of an Integrity Score offers a quantifiable metric to verify whether platforms are providing “undue preference” to their own services over calibrated local entities.
  • Fair Trading and Transparency: The Search Sciences™ framework provides a technical pathway for compliance with the DMCC’s requirements for fair dealing. By resolving semantic ambiguity, local businesses can exercise their right to clear, non-discriminatory visibility.
  • Open Interoperability: Our methodology supports the Act’s focus on “Open Choices” by ensuring that business data is structured for interoperability. This prevents Strategic Market Status (SMS) firms from using proprietary data silos to anti-competitively restrict the visibility of independent London enterprises.

V. Solving the Information Monopoly

Beyond the Gatekeepers

Structured data alone does not dissolve market intermediaries. Instead, it changes the architecture of discovery. When local businesses are legible to machines, they can be discovered directly by AI assistants, autonomous procurement agents and generative systems without relying on proprietary platforms to intermediate visibility. This shift weakens the information monopoly that extractive platforms have maintained through data silos and proprietary indexing

The Semantic Commons

A Semantic Commons is a shared layer of high‑fidelity data that represents local entities in structured form, accessible to any discovery system that adheres to interoperable standards. This layer acts as a civic resource from which multiple discovery tools can draw, reducing dependence on closed platforms and enabling more equitable economic interactions.

Stephen Downes and others in the semantic web community have long argued that shared, structured data forms the basis of more democratic information ecosystems because it allows any compliant agent to interpret and reuse data without proprietary constraints. What the Semantic Commons approach adds is a governance layer that ties structured business data to verified civic infrastructure, further strengthening trust and interoperability.

The Economic Multiplier Effect

The shift from extractive discovery models to direct machine readability can restore economic value to local communities. Instead of paying significant commissions to intermediaries, businesses could engage directly with consumers via autonomous agents built on civic data infrastructure. The result is increased retention of revenue within the local economy, stronger brand recognition outside closed marketplaces and reduced friction in consumer choice.

VI. Technical Implementation: The Search Sciences™ & DMCC Addendum

To support interoperable discovery and meet the “Open Choice” requirements of the Digital Markets, Competition and Consumers (DMCC) Act 2024, the technical layer of our methodology requires rigorous adherence to the following three pillars:

1. High-Fidelity Entity Calibration (Schema.org & DMCC Tags)

We extend the base Schema.org/LocalBusiness vocabulary to ensure absolute clarity for machine interpretation. Every calibrated entity must include:

  • Operational Attributes: Structured data for menus, real-time operating hours, and service capabilities.
  • Legislation Compliance: Use of the legislationApplies property to explicitly link the entity to the DMCC framework, asserting the business’s right to fair competition.

Example Compliance Implementation (JSON-LD):

JSON

{
  "@context": "https://schema.org",
  "@type": "LocalBusiness",
  "name": "London Artisan Bakery",
  "legislationApplies": {
    "@type": "Legislation",
    "name": "Digital Markets, Competition and Consumers Act 2024",
    "url": "https://www.legislation.gov.uk/ukpga/2024/13/contents"
  },
  "usageInfo": "Verified Civic Entity via Data for London Library",
  "isAccessibleForFree": "true",
  "isProprietary": "false"
}

3. Machine-Readability & API Interoperability

To sustain the Semantic Commons, data must be served via RESTful APIs designed for zero-friction consumption by autonomous discovery agents. These interfaces must support:

Provenance Headers: Mandatory metadata to verify that the information originates from a “Verified Civic Source” (e.g., the Data for London Library).

JSON-LD as Default Payload: Ensuring data is natively “AI-ready” without translation.

CORS (Cross-Origin Resource Sharing): Essential for allowing verified third-party civic apps to bypass platform gatekeepers and provide direct-ordering links.

VII. London 2030: The Vision

Looking forward, London can pioneer a global model of Information Sovereignty where structured civic data underpins urban discovery ecosystems, supporting not only economic resilience but also social inclusion by ensuring fair and transparent visibility for local enterprises.

VIII. Conclusion and Call to Action

As this paper has demonstrated, the dynamics of discovery in urban environments like London are not shaped solely by market economics or consumer behaviour. They are shaped by how machines interpret information — by the semantic legibility of business data. When local businesses lack high‑fidelity, structured metadata, they become dependent on extractive delivery platforms that monetise visibility through high commissions and algorithmic control. In London, independent operators typically pay between 25 per cent and 35 per cent in commission on orders listed via delivery platforms, a cost that is often passed on to consumers in the form of higher prices and reduced choice. Restaurants report that prices on these platforms are higher than in‑store equivalents to compensate for the commission burden.

This situation — where wealth is transferred from local enterprises and households to platform intermediaries — is not an inevitability. It is a structural by‑product of data poverty: the absence of verified, machine‑readable information about local businesses that autonomous discovery systems require. When discovery systems cannot directly interpret local information, they defer to intermediaries that have the resources to structure, control and monetise visibility at scale.

The Search Sciences™ Framework introduced in this whitepaper provides a methodical approach to resolving this semantic ambiguity. Through high‑fidelity entity calibration, authoritative verification against civic data sources, and rigorous machine‑readability scoring, local business information can be transformed into structured, interoperable datasets. When local entities are calibrated in this way, evidence from pilot environments suggests that AI discovery systems treat them markedly differently, with measurable increases in direct recommendation probability and reduced reliance on extractive platforms.

At a civic level, London is already building the foundational infrastructure that makes this transition possible. The Data for London Library has quickly become a centralised hub for discoverable, high‑quality datasets that span public services, demographics, infrastructure and urban analytics. This shared data cloth can be extended to include verified local business entities, forming a Semantic Commons that underpins fair, transparent and machine‑interpretable discovery throughout the city. Such a commons is not merely a technical artefact but a civic asset that supports economic inclusion, innovation and equity.

From Digital Presence to Semantic Presence

The transition from a passive “digital presence” to an active “semantic presence” is a defining challenge of the 2020s. Organisations that remain hidden in unstructured digital footprints will increasingly be invisible to the systems that mediate economic interactions. Conversely, organisations that adopt machine‑readable formats and participate in shared civic data infrastructure will secure visibility in the very systems that shape consumer behaviour, procurement processes and AI‑driven recommendations.

This transition has profound implications:

  • It restores agency to local businesses that have been marginalised by algorithmic intermediaries.
  • It strengthens local economies by reducing revenue leakage caused by high commission structures.
  • It supports consumer choice by enabling discovery systems to produce richer, more accurate results that reflect real local supply rather than curated platform silos.
  • It positions London as a global benchmark for Information Sovereignty, where structured civic data becomes a public good that supports both economic and civic objectives.

Introducing the Younis Group Discovery Diagnostic

For organisations and civic actors ready to engage with these realities, the next step is clear. The Younis Group Discovery Diagnostic is a formal assessment designed to reveal how an organisation’s information currently exists within discovery ecosystems — including search engines, AI assistants and autonomous agents — and where semantic ambiguity is creating economic and visibility costs.

The Diagnostic maps an organisation’s metadata landscape, highlights structural weaknesses, and identifies specific opportunities to improve machine interpretation through strategic data calibration. It is both an analytical baseline and a strategic roadmap for organisations that wish to transition from dependency on intermediaries to direct, equitable discoverability.

Why This Matters in 2026 and Beyond

In the era of autonomous systems, visibility is not a by‑product of presence. It is a function of semantic clarity. Businesses and civic institutions that fail to engage with the principles of Search Sciences™ risk invisibility in the very systems that shape market outcomes. Those that embrace structured data and shared civic infrastructure will not only improve their own visibility but strengthen the fabric of the urban economy as a whole.

London is at a pivotal moment. With the expansion of civic data platforms and a growing recognition that data infrastructure is a public good, the city has the opportunity to lead on a new model of urban discovery — one that safeguards local enterprise, empowers consumers and ensures that the benefits of the digital economy accrue to the communities that build and sustain it.

Next Steps

  1. Adopt the Discovery Diagnostic: Commission a formal assessment of your organisation’s semantic footprint within discovery systems.
  2. Calibrate High‑Fidelity Entities: Implement structured metadata calibrated against recognised schemas and civic data sources.
  3. Participate in the Semantic Commons: Contribute high‑quality, verified business data to shared civic infrastructure.
  4. Measure and Adapt: Track improvements in visibility, attribution and economic outcomes as structured data flows into discovery channels.

By acting on these insights, organisations and civic partners can shift the paradigm of discovery from one shaped by intermediaries to one governed by transparent, machine‑readable, equitable information — a principled realisation of Search Sciences™ in practice.

Suggested Citation: Younis, M. (2026). Search Sciences™: Resolving Semantic Ambiguity in Local Urban Discovery. Younis Group Research Series. London, UK.

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