Socratic RAG for KOSs

• Socratic RAG for KOSs is a method that connects informal research queries to formal knowledge organization systems using a blend of dense retrieval and Socratic dialogue.
• It employs a three-stage process—initial retrieval via neural embeddings, dialogue-based query refinement, and final answer synthesis integrating canonical KOS identifiers.
• Case studies like CollabNext demonstrate its ability to improve expert discovery and bibliometric mapping while addressing semantic ambiguities in academic search.

Socratic Retrieval Augmented Generation (RAG) for Knowledge Organization Systems (KOSs) is a methodology for bridging the gap between natural-language research queries and the structured, machine-understandable taxonomies that underpin academic knowledge. By integrating retrieval-augmented generation frameworks with a Socratic dialogue loop, this approach maps ambiguous or underspecified queries onto precise Knowledge Organization System identifiers, thereby enhancing discovery across bibliometric repositories. It operationalizes the interplay of “little semantics” (domain-specific KOS structures) and “big semantics” (broad bibliometric schemas), and demonstrates broad utility via applications such as the CollabNext person-centric knowledge graph. The following sections detail its system architecture, retrieval and dialogue mechanisms, integration strategies, application in CollabNext, and open research directions (Lefton et al., 20 Feb 2025).

1. System Architecture

The Socratic RAG agent is formalized as an end-to-end three-stage pipeline:

1. Query Input and Initial Retrieval: The user supplies a natural-language topic query (e.g., "I’m looking for experts on plastic recycling near my university."). A vector-based retriever indexes KOS topics—drawing from systems such as OpenAlex, Dewey, MeSH, and SKOS vocabularies—using dense neural embeddings.
2. Socratic Dialogue Refinement: If candidate topic matches are ambiguous or confidence falls below a fixed threshold ($\theta_{\mathrm{conf}}$), the agent invokes a Socratic Dialogue Manager. This subsystem generates disambiguating queries (e.g., "Are you interested in chemical processes, supply-chain logistics, or economic aspects of plastic recycling?") targeting the highest source of uncertainty.
3. Final Answer Generation: After resolution of ambiguity, the agent fuses the refined query and the selected KOS identifiers into a natural-language response, citing canonical identifiers and mapping user intent to KOS semantics.

This pipeline is recursive: if ambiguity remains after a dialogue turn, the retrieval and Socratic questioning sequence is repeated until the model’s confidence metric exceeds $\theta_{\mathrm{conf}}$ or a satisfactory mapping is found. The process is summarized in the following pseudocode (Lefton et al., 20 Feb 2025):

def retrieve_and_ask(query): # Stage 1: initial retrieval cands = retrieve_topics(query) if confidence(cands) < theta_conf: # Stage 2: Socratic refinement question = select_question(query, cands) answer = get_user_answer(question) query = augment_query(query, answer) return retrieve_and_ask(query) # Stage 3: generate return generate_answer(query, cands[:K])

2. Semantic Retrieval and Hierarchical Reranking

The retrieval component employs domain-pretrained Transformer embeddings (e.g., SciBERT, 768-dimensional) for both queries and KOS topic labels/descriptions. Similarity is quantified by cosine similarity:

$$\mathrm{score}_1(q, t) = \frac{e(q)\cdot e(t)}{\|e(q)\|\|e(t)\|}$$

where $e(x)$ denotes the embedding for $x$. Retrieval diversity can be modulated by a temperature parameter $\tau$.

To leverage ontology structure, a hierarchy-aware reranker augments the retrieval score for each candidate topic $t$:

$$\mathrm{score}(t) = \mathrm{sim}(q, t) + \alpha \sum_{a\in\text{ancestors}(t)} \beta^{\mathrm{depth}(a, t)} \cdot \mathrm{sim}(q, a) + \gamma \cdot \mathrm{coherence\_with\_siblings}(t)$$

Here, $\alpha$ controls ancestor contribution, $\beta \in (0,1)$ applies exponential decay with depth, and $\gamma$ boosts coherence with high-scoring siblings. Approximate nearest-neighbor (ANN) indexing underpins scalable retrieval, with index entries carrying all requisite semantic metadata, including (topic_id, canonical_label, description, parent_ids, and synonyms/altLabels) (Lefton et al., 20 Feb 2025).

3. Socratic Dialogue for Disambiguation

Ambiguity is addressed by a Socratic Dialogue module whose principal function is to resolve uncertainty among candidate topics that either tie in score or fail to meet the confidence threshold $\theta_{\mathrm{conf}}$. The system generates questions targeting the largest entropy dimension, e.g., aspect (chemistry vs economics vs logistics), conceptual granularity, or field scope.

Information gain informs question selection:

$$U(\text{question}) \approx \text{entropy}_{\text{candidates}} - \sum_{\text{answer}} P(\text{answer}) \cdot \text{entropy}_{\text{after filtering}}$$

An example interaction is:

• Agent: “When you say ‘plastic recycling,’ are you most interested in the chemical processes (e.g., polymer depolymerization), the logistical aspects (waste collection and supply chains), or economic/policy concerns?”
• User: “Supply-chain logistics.”
• The system restricts candidate KOS entries accordingly (e.g., under “Waste Management and Disposal”) and re-invokes retrieval.

While the deployed system is not RL-based, a plausible objective is to maximize user confirmation rate or minimize dialogue turns. The reward can be set as $R = +1$ for final mapping acceptance, $-0.1$ per extra turn, and the loss function is:

$$L = - \mathbb{E}_{\tau \sim \pi_\theta}\left[R(\tau)\right]$$

The implication is that future RL-based dialogue policies could further optimize this procedure (Lefton et al., 20 Feb 2025).

4. Generation and Response Synthesis

The generation module utilizes a GPT-family LLM (e.g., GPT-3.5/4), conditioned by a prompt template embedding both the (refined) user query and top-K KOS entry descriptions:

“You are a scholarly assistant. The user wants: {refined_query}. Based on these KOS entries: * {topic_id}: {label} — {short description} * … Provide a concise explanation of which topic best matches, why, and include the canonical KOS identifier.”

Topic descriptions are collated in the context window; the LLM outputs a human-interpretable mapping, e.g., “It sounds like you’re focusing on supply-chain logistics of plastic recycling, which aligns with OpenAlex Topic ID 0x1234 (Waste Management and Disposal → Reuse & Recycling Logistics).” Candidate responses may be reranked by log-probability, with the highest scoring selected (Lefton et al., 20 Feb 2025).

5. KOS Integration and Ontology Alignment

Mapping procedures ensure each KOS concept is persistently identified (SKOS URI, MeSH ID, OpenAlex concept ID) and merged across sources using crosswalk tables (e.g., Wikidata ↔ OpenAlex) and shared external identifiers (ORCID for individuals, ROR for institutions). Synonyms and alternative labels are indexed to enable robust string-based mapping of colloquial queries.

Semantic expansion is achieved by including SKOS broader/narrower relations and synonyms per candidate concept, thereby presenting a more comprehensive result set. A plausible implication is that future ontology-alignment tools such as AML or LogMap could enhance reconciliation of overlapping KOS entries across heterogeneous domains (Lefton et al., 20 Feb 2025).

6. CollabNext: Case Study Application

CollabNext embodies the Socratic RAG framework in a concrete bibliometric knowledge graph. Anchored on OpenAlex, it links people, works, institutions, and topics. To increase visibility of underrepresented scholars (e.g., HBCU faculty), default entity rankings prioritize geospatial proximity rather than citation counts.

Users engage Socratic RAG to pose queries such as “plastics waste management near Howard University,” iteratively clarify intent through dialogue, and ultimately receive KOS-matched OpenAlex topic IDs. Evaluation metrics include:

• Precision@K: Fraction of top-K retrieved topic IDs accepted by the user.
• Average number of dialogue turns until a final mapping is made.
• Qualitative user satisfaction (survey score 1–5 on clarity of final answer).

While no full quantitative results are yet reported (prototype phase), these metrics establish a groundwork for future assessment (Lefton et al., 20 Feb 2025).

7. Open Challenges and Research Directions

Bridging between “little semantics” (deep, domain-specific ontologies) and “big semantics” (broad bibliometric schemas) remains challenging, especially in domain intersections where KOS coverage is incomplete. Open research questions include:

1. Detecting and handling truly novel topics not present in current KOS hierarchies; such gaps might signal emerging research domains.
2. Supporting user-driven refinement and expansion of search granularity (both narrowing and broadening taxonomic scope) within a single session.
3. Leveraging interaction logs to adapt question-selecting heuristics and dynamically update concept embeddings.

Proposed extensions involve establishing feedback loops to suggest emergent or missing concepts to KOS curators, developing RL-trained dialogue policies for efficient user engagement, and supporting linkage to additional scholarly artifacts (datasets, code, posters) via topic-based “Rosetta Stone” identifiers (Lefton et al., 20 Feb 2025).

In summary, Socratic RAG synergizes multi-round dialogue, dense retrieval, and hierarchy-aware reranking to map informal research queries to canonical, machine-actionable KOS identifiers. The CollabNext demonstration underscores both its utility for democratizing expert discovery and for surfacing researchers historically marginalized in the scholarly ecosystem.