Roster of Experts (RoE) System

• Roster of Experts (RoE) is a system that compiles and ranks experts by integrating multi-source data, entity resolution, and adaptive machine learning.
• It leverages social network metrics such as PageRank and betweenness centrality to assess expert influence and streamline team formation.
• RoE employs real-time user feedback and taxonomic mapping to adapt rankings and ensure robust, context-specific expert recommendations.

A roster of experts (RoE) is a system, methodology, or computational pipeline for constructing, assessing, and maintaining a curated, ranked list of individuals whose expertise is aligned with specific domains, tasks, or informational needs. The RoE concept is central to expert finding, team formation, knowledge management, and collaborative decision-making systems across academic, industrial, and crowdsourcing environments. Design and implementation of a robust RoE involves the integration of structured data acquisition, social network analysis, feature extraction, machine learning (including ranking and aggregation models), user- and data-driven validation, and adaptive updates reflecting both domain shifts and user feedback.

1. Data Acquisition, Integration, and Entity Resolution

Construction of a roster of experts begins with the systematic acquisition and integration of multi-source data reflecting potential experts' activities, relationships, and outputs. Social network based approaches (Bitton et al., 2012) typically ingest large-scale datasets from academic social networks (e.g., Mendeley, Academia.edu), publication records, and user profiles. These sources yield both explicit (profiles, group memberships) and implicit (coauthorship links, bookmarked publications) relations.

Entity resolution is a foundational task, linking authorships in publication data to user profiles using string similarity metrics such as the Levenshtein distance. For each author, the profile with the minimal Levenshtein distance is chosen as the match, minimizing integration errors when combining heterogeneous datasets.

Data Type	Integration Strategy	Matching Technique
User Profile	Direct ingest	N/A
Authorship	Publication-author to profile mapping	Levenshtein distance
Bookmarks	Publication to user via bookmark	Merge by publication id

The resolution process ensures the expert network is accurately constructed, preventing fragmentation due to data silos, and enabling robust downstream analyses.

2. Text Categorization and Topic Mapping

Mapping textual task descriptions (e.g., job ads, committee calls) to expert domains is accomplished via automated text categorization. Text entered by the user is first categorized using an external API (e.g., Yahoo API (Bitton et al., 2012)), returning several content categories ordered by relevance. Refinement is performed by comparing query keywords to a predefined taxonomy (such as domains from Mendeley) using the Levenshtein distance; the minimal-distance match determines the final expert search category.

This two-step process reduces ambiguity and focuses the expert search within a semantically coherent domain. The application of string distance for category selection is critical for robust mapping, especially in scenarios with noisy or user-generated input.

3. Network Construction and Social Analysis

RoE systems model the landscape of expertise through the joint construction of multiple social graphs. Two principal graphs are synthesized:

• Coauthorship network: Nodes (people) are linked by coauthorship.
• Profile-based network: Nodes are linked through user-defined group membership or profile relations.

These are merged into a unified expert network. The motivation for combining is empirical: coauthorship networks are often fragmented (many small cliques), while profile-based links add global connectivity, allowing for more comprehensive centrality and influence analyses.

Feature extraction from this unified graph leverages established network metrics:

• PageRank: authority/importance in the network.
• Betweenness: mediation/control of information flow.
• Closeness: reachability to others—indicative of influence propagation.

These measures are canonical in social network analysis and provide orthogonal information concerning an expert's role and visibility within the community.

4. Multimodal Feature Extraction and Expert Ranking

Expert rank computation integrates graph-derived metrics and additional evidence of expertise:

• Journal ranking: proxy for publication impact.
• Number of readers: user engagement with the expert's work.
• User rank: crowd-sourced scoring via positive/negative votes.

The overall scoring model is effectively a weighted linear sum of these features:

$$S(u) = \alpha \cdot PR(u) + \beta \cdot B(u) + \gamma \cdot C(u) + \delta \cdot JR(u) + \varepsilon \cdot R(u) + \zeta \cdot UR(u)$$

where the weights $$(\alpha, \beta, \gamma, \delta, \varepsilon, \zeta)$$ can be learned from training data or adapted through crowd input. This formulation accommodates noise and variability across different data sources by balancing complementary indicators, guarding against domination by any single metric.

Feature weighting is optimized via a supervised learning module (e.g., C4.5 decision trees), learning from prior expert selection outcomes and crowd-provided correctness judgments.

5. Machine Learning, Feedback, and Model Adaptivity

Model adaptivity is central to RoE robustness. By leveraging learning algorithms (decision trees as in (Bitton et al., 2012)), the system iteratively tunes feature weights based on observed performance in real-world tasks (e.g., conference committee formation, job placement). The learning component absorbs not only explicit relevance signals (e.g., user judgments, up-/down-votes) but also evolving implicit network structures as new data is ingested.

Real-time user feedback closes the loop, enabling “living” rosters that adjust to shifting domain importance, emerging research areas, and user needs. The presence of upward and downward votes allows for rapid contamination correction (e.g., removal of erroneously highly ranked individuals) and bootstrapping in nascent fields.

6. Validation, Error Minimization, and System Reliability

RoE system reliability is ensured via multi-layer validation:

• Taxonomic mapping: Reduces input ambiguity by explicit task-to-domain categorization.
• Name-matching algorithms: Minimize integration/mapping errors.
• Network metric redundancy: Use of multiple independent centrality and impact measures mitigates bias (e.g., excessive reward for publication quantity over influence).
• Feedback recourse: The system responds in real-time to direct user validation, ensuring adaptability and resilience to outdated or erroneous patterns.

The interplay of automated learning, social graph metrics, and user judgment yields a system that adapts to data drift and maintains high-precision expert recommendations.

7. Application Cases and Broader Implications

The methodologies discussed were applied to tasks such as job candidate identification, program committee assembly, and locating subject-matter experts for advisory or legal purposes—demonstrating the operational validity of the RoE framework (Bitton et al., 2012). For each task, text input and desired attributes (e.g., qualification level) are mapped to a relevant domain, and the system produces a ranked, network-aware list of potential experts.

The key generalizations are:

• Multi-source integration allows construction of expert rosters that are maximally comprehensive and current.
• Data-driven relevance mapping and adaptive multi-metric aggregation yield superior precision over single-source or non-adaptive systems.
• Combination of social network analysis, supervised machine learning, and explicit user feedback forms a robust foundation for dynamic, accurate expert ranking systems suitable for a wide range of real-world expert retrieval and team formation applications.

RoE systems so designed are capable of maintaining up-to-date, reliable, and context-appropriate expert recommendations, and serve as blueprints for future developments in automated expert finding and knowledge management infrastructure.