Example-Based Dataset Search

• Example-based dataset search is a method that uses data fragments, such as partial tables or records, as queries to retrieve semantically similar datasets.
• It leverages techniques like embeddings, schema labeling, and LLMs to perform joinable, unionable, and semantic retrieval for effective data augmentation.
• Applications span data integration, scientific recommendations, and multimodal search, while addressing challenges in scalability, privacy, and evaluation.

Example-based dataset search encompasses a family of methods and systems that enable users to retrieve, recommend, or augment datasets using examples as queries, rather than relying solely on keyword or metadata search. In this paradigm, the user supplies a representative fragment, an instance, or detailed specification of the desired data—such as a partial table, a sample record, an attribute set, or a full-sentence description of analytical intent—and the system returns datasets that are structurally, semantically, or functionally similar, joinable, or augmentative. Example-based dataset search integrates methodologies from information retrieval, database integration, machine learning, and, increasingly, LLMs, addressing multiple open research questions around expressiveness, scalability, interactivity, and downstream utility.

1. Foundations and Taxonomy of Example-Based Dataset Search

Example-based dataset search distinguishes itself from metadata- or keyword-driven approaches by leveraging data instances or example-based specifications as queries. Two central paradigms characterize contemporary systems:

• Constructive Dataset Search (“dataset construction”): Here, the user provides a partial data artifact (e.g., a table, a record, or a sequence). The system searches for datasets that can augment (via union) or extend (by joining additional columns/rows) the example, enabling table completion or integration (Chapman et al., 2019).
• Instance and Attribute-Based Search: This includes approaches where an instance or attribute vector derived from an example is used to find visually, structurally, or semantically similar datasets or records (Tao et al., 2016, Chen et al., 2020, Castelo et al., 2021).

A high-level taxonomy includes:

Search Paradigm	Example Input	Retrieval Goal
Joinable Search	Partial Table	Find joinable relations/tables
Unionable Search	Sample Table/Row	Find row/record-compatible datasets
Semantic Search	Full-Sentence Query	Retrieve by inferred intent/semantics
Recommendation Search	Description+Example	Suggest suitable datasets (recomm.)
Multimodal Search	Image/Graph Example	Retrieve cases by visual/signal cues

Techniques typically leverage content-derived signals—column/attribute embeddings, value or distribution sketches, and semantic representations—in addition to (or instead of) hand-curated metadata (Li et al., 31 Aug 2025, Viswanathan et al., 2023, Castelo et al., 2021).

2. Methodological Approaches and Technical Frameworks

The implementation of example-based search systems spans several technical design spaces:

• Tabular and Joinable Search: Early approaches used MinHash sketches and LSH-based set containment methods (e.g., LSH Ensemble, JOSIE) to rapidly identify joinable tables via set overlap or containment (Li et al., 31 Aug 2025). Recent methods encode columns or tables into embedding spaces learned from pre-trained LLMs or contrastive learning, enabling rich semantic matching. For example, DeepJoin, PEXESO, and COIL compute vector embeddings for columns and match query–candidate tables via cosine similarity or token-level matching.
• Schema Labeling and Mixed Ranking: Schema label generation methods learn to assign semantically meaningful labels to columns using multi-label classification, matrix factorization, and word embedding co-occurrence (via SPPMI and CoFactor) (Chen et al., 2020). Mixing label-based similarity (e.g., using fastText and Word Mover’s Distance) with classical field-based ranking (e.g., BM25 over descriptions) addresses vocabulary mismatch in example queries.

Example scoring for vector datasets is often based on set-to-set matching functions, e.g.,

$$S(Q, D_i) = \sum_{j=1}^n \max_{k=1, ..., m} \text{sim} (v_{q_j}, v_{i_k})$$

where $Q, D_i$ are query and candidate sets, and $v_{q_j}, v_{i_k}$ their vector elements (Li et al., 31 Aug 2025).

• Interactive and Recommendation-Oriented Search: Systems such as DataFinder (Viswanathan et al., 2023) use bi-encoder architectures trained with contrastive learning on (query, dataset) pairs for ranking datasets from natural language queries. These systems outperform baseline BM25 and k-NN retrievers in P@k, MAP, and MRR.
• Task-Based and Utility-Driven Search: Some platforms (e.g., Mileena (Huang et al., 2023)) focus on discovering datasets that maximize downstream ML performance, using task-specific utility functions and precomputed statistical sketches (semi-ring aggregations) for rapid evaluation.

3. Role of LLMs and Semantic Representations

LLMs contribute crucial capabilities for expressiveness and generalization in example-based dataset search (Li et al., 31 Aug 2025, Lin et al., 25 Jul 2025):

• Query Understanding: Natural language and complex example queries can be interpreted for intent, schema, and attribute matching (DataFinder (Viswanathan et al., 2023), DataScout (Lin et al., 25 Jul 2025)).
• Semantic Modeling: Embedding both example queries and candidate datasets with LLM-derived representations enables robust, context-aware similarity matching, overcoming syntactic heterogeneity in column names, attributes, or data values.
• Interactive and Dialog-guided Search: LLMs can facilitate feedback-driven refinement of search queries, generate interpretive explanations, and even recommend relevant filters based on unseen aspects of the data corpus.
• Query Reformulation and Attribute Suggestion: DataScout (Lin et al., 25 Jul 2025) employs LLMs to generate query reformulations and attribute-based filters, reflecting actual “search space” structure (e.g., via k-means clustering on attribute embeddings followed by LLM-based cluster naming and filter suggestion).

LLMs also support data pre-processing (schema inference, table cleaning), enabling higher-quality retrieval in both RAG and pretraining/fine-tuning pipelines (Li et al., 31 Aug 2025).

4. Application Domains and System Implementations

Example-based dataset search methods have been implemented across diverse settings:

• Data Integration and Augmentation: Auctus (Castelo et al., 2021) profiles uploaded or ingested datasets, indexes by content summaries (e.g., via k-means clustering for distributions) and supports example-based join/union queries to recommend augmentations. ElasticSearch and LSH-based indexes support efficient query evaluation.
• Scientific Dataset Recommendation: DataFinder (Viswanathan et al., 2023) constructs a benchmark dataset of (research idea, relevant dataset) pairs, evaluates multiple IR methods, and deploys a bi-encoder retriever with superior MAP and MRR performance.
• Multimodal and Visual Search: VizWiz Dataset Browser (Bhattacharya et al., 2019) offers instance-based, multimodal retrieval via full-text or categorical filtering, entropy-based answer diversity ranking, and coupled qualitative–quantitative analysis; SlopeSeeker (Bendeck et al., 19 Feb 2024) links trend labels (crowdsourced and quantitatively mapped) to chart segments for fine-grained, interactive trend search.
• Object-Centric Retrieval: ReSeDis (Huang et al., 18 Jun 2025) unifies corpus-level retrieval and pixel-level referring object grounding: object proposals (YOLOv8) are scored by CLIP cross-modal similarity and then globally ranked, providing both image recall and localization precision. This end-to-end framework supports referring-based search in large-scale visual collections.

5. Challenges, Limitations, and Open Research Directions

Central challenges remain for realizing robust, scalable, and generalizable example-based dataset search (Chapman et al., 2019, Li et al., 31 Aug 2025):

• Metadata and Content Quality: Many datasets lack high-quality, complete metadata, limiting the effectiveness of structural or semantic alignment (see also Google Dataset Search analysis (Benjelloun et al., 2020)).
• Expressive Query and Schema Understanding: Open representations for user-supplied examples—across tabular, vector, and multimodal domains—require models that can robustly infer intent, resolve schema, and align at varying granularities.
• Indexing and Scalability: Efficient search across tens of thousands of datasets often combines multi-level indexing: schema-label embeddings, precomputed sketches (Auctus (Castelo et al., 2021)), class-balanced kd-trees for hard example mining (DDS-NAS (Poyser et al., 17 Jun 2025)), or approximate nearest-neighbor search for vector sets (Li et al., 31 Aug 2025).
• Privacy and Access Control: Privacy-preserving approaches are underdeveloped; recent advances such as semi-ring aggregations with local differential privacy (Factorized Privacy Mechanism) in Mileena (Huang et al., 2023) provide one line of solution, but general, scalable frameworks remain an open problem.
• Federation and Heterogeneity: Cross-repository (federated) search and handling of diverse data formats, ownership regimes, and schema inconsistencies are only partially addressed (Li et al., 31 Aug 2025).
• Evaluation Benchmarks: Robust evaluation requires not only capturing precision/recall but also utility for downstream tasks (e.g., augmentation benefit, localization accuracy in multimodal settings).

Future work is expected around federated query protocols, unified cross-modal representation learning, privacy-preserving search, integration of dataset quality metrics, and standardized evaluation suites for multi-phase retrieval–augmentation workflows (Li et al., 31 Aug 2025, Castelo et al., 2021, Lin et al., 25 Jul 2025).

6. Mutual Reinforcement with LLMs, Systematic Benchmarks, and Interactive Search

The mutually beneficial relationship between example-based search and LLM infrastructure is bidirectional (Li et al., 31 Aug 2025):

• LLMs support open-ended, interactive, and semantically expressive dataset search.
• Sophisticated search over high-quality, well-indexed external datasets in turn augments LLMs for RAG and domain-specific fine-tuning, improving downstream performance and reliability.

Benchmark datasets—including DataFinder (Viswanathan et al., 2023), community-driven systems like DS4RS (Shao et al., 13 Aug 2025), and unified evaluation frameworks (ReSeDis (Huang et al., 18 Jun 2025))—provide necessary testbeds to compare retrieval, semantic matching, augmentation, and recommendation methods, with field-specific benchmarks emerging for code, images, trends, and tabular data.

Integrated systems now support:

System Name	Key Feature	Representative Paper
Auctus	Augmentative join/union search, profiling	(Castelo et al., 2021)
DataFinder	Semantic recommendation, bi-encoder retriever	(Viswanathan et al., 2023)
DataScout	LLM-assisted reformulation, semantic filters	(Lin et al., 25 Jul 2025)
SlopeSeeker	Semantically labeled time-series trends	(Bendeck et al., 19 Feb 2024)
DS4RS	Recommender-specific semantic search, explain.	(Shao et al., 13 Aug 2025)
Mileena	Task-based utility, DP-preserving aggregation	(Huang et al., 2023)

By integrating query interpretation, semantic indexing, dynamic ranking, and interactive feedback, contemporary example-based dataset search platforms enable both structured explorations and flexible, user-driven discovery and augmentation. Continuing advances in neural representation learning, LLM-based query mediation, and multi-modal indexing are expected to further unify and generalize the field.

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In summary, example-based dataset search represents a shift from static, metadata-bound retrieval to dynamic, content-aware, and semantically robust data discovery. Systems in this domain increasingly leverage LLMs, advanced embeddings, and multi-phase augmentative pipelines to meet complex, user-specific analytical needs. While the area has advanced markedly, addressing challenges in privacy, integration, interpretation, and benchmarking remains central to realizing flexible and intelligent data search ecosystems (Li et al., 31 Aug 2025, Lin et al., 25 Jul 2025, Viswanathan et al., 2023).