SIRIS-Lab/impuls-query-parsing

IMPULS Query Parsing Dataset

A multilingual dataset for training and evaluating query parsing models that convert natural language queries into structured JSON for R&D project semantic search.

Dataset Description

This dataset was created as part of the IMPULS project (AINA Challenge 2024), a collaboration between SIRIS Academic and Generalitat de Catalunya to build a multilingual semantic search system for R&D ecosystems.

The dataset contains natural language queries in Catalan, Spanish, and English paired with their structured JSON representations, designed for training models to:

• Extract semantic search terms from natural language
• Identify structured filters (funding programme, year, location, organization type)
• Detect query language and intent

Supported Tasks

• Query Parsing / Semantic Parsing: Convert natural language to structured JSON
• Information Extraction: Extract entities and filters from queries
• Multilingual NLU: Understanding queries across CA/ES/EN

Dataset Structure

Data Splits

Split	Examples	Description
train	682	Synthetic, template-generated queries
test	100	Real queries from domain experts

Schema

Each example contains a structured JSON with the following fields:

{
  "doc_type": "projects",
  "filters": {
    "programme": "Horizon 2020 | FEDER | SIFECAT | null",
    "funding_level": "string | null",
    "year": ">=2020 | 2015-2020 | null",
    "location": "Catalunya | Spain | null",
    "location_level": "region | province | country | null"
  },
  "organisations": [
    {
      "type": "university | research_center | hospital | company | null",
      "name": "UPC | CSIC | null",
      "location": "Barcelona | null",
      "location_level": "province | region | null"
    }
  ],
  "semantic_query": "intel·ligència artificial salut",
  "query_rewrite": "Human-readable interpretation of the query",
  "meta": {
    "id": "TRAIN_001",
    "source": "synthetic | expert",
    "lang": "CA | ES | EN",
    "original_query": "The original natural language query",
    "intent": "Discover | Quantify",
    "style": "Concise | Verbose | Technical",
    "components": ["Content", "Programme", "Year", "Location"],
    "resolvability": "Direct | Adapted | Partial",
    "notes": "Optional notes about interpretation"
  }
}

Field Descriptions

Field	Description
doc_type	Document type to search (always "projects")
filters.programme	Funding programme (H2020, Horizon Europe, FEDER, SIFECAT, etc.)
filters.year	Year filter (single year, range, or comparison like ">=2020")
filters.location	Geographic filter
filters.location_level	Geographic granularity (country, region, province)
organisations	List of organization filters with type, name, and location
semantic_query	Core thematic content for vector search
query_rewrite	Human-readable interpretation
meta.original_query	The original natural language query
meta.lang	Query language (CA/ES/EN)
meta.intent	Query intent (Discover/Quantify)
meta.resolvability	How well the query maps to the schema

Dataset Statistics

Language Distribution

Language	Training	Test
Catalan (CA)	~33%	~33%
Spanish (ES)	~33%	~21%
English (EN)	~33%	~46%

Intent Distribution

Intent	Count	Percentage
Discover	600	88.0%
Quantify	82	12.0%

Resolvability Distribution

Type	Count	Percentage	Description
Direct	529	77.6%	Fully mappable to schema
Adapted	15	2.2%	Requires interpretation
Partial	138	20.2%	Cannot fully express (ranking, aggregation)

Component Distribution

Component	Frequency
Thematic content	92.8%
Organization type	39.9%
Organization location	17.7%
Programme (funding)	17.6%
Time expressions	10.7%
Project location	10.4%
Year (specific)	7.8%
Organization name	7.3%

Examples

Example 1: Catalan Query (Discover)

Original query: "Projectes on la UPC és coordinadora en l'àmbit de la ciberseguretat"

{
  "doc_type": "projects",
  "filters": {
    "programme": null,
    "funding_level": null,
    "year": null,
    "location": null,
    "location_level": null
  },
  "organisations": [
    {
      "type": "university",
      "name": "UPC",
      "location": null,
      "location_level": null
    }
  ],
  "semantic_query": "ciberseguretat",
  "query_rewrite": "Llista de projectes de la UPC sobre ciberseguretat",
  "meta": {
    "id": "TRAIN_488",
    "source": "synthetic",
    "lang": "CA",
    "original_query": "Projectes on la UPC és coordinadora en l'àmbit de la ciberseguretat",
    "intent": "Discover",
    "style": "Concise",
    "components": ["Scope", "Organisation Name", "Content", "Role Qualifier"],
    "resolvability": "Partial",
    "notes": "No es pot filtrar pel rol de 'coordinadora'"
  }
}

Example 2: Spanish Query with Filters

Original query: "proyectos de inteligencia artificial financiados por H2020 desde 2019"

{
  "doc_type": "projects",
  "filters": {
    "programme": "Horizon 2020",
    "funding_level": null,
    "year": ">=2019",
    "location": null,
    "location_level": null
  },
  "organisations": [],
  "semantic_query": "inteligencia artificial",
  "query_rewrite": "Proyectos sobre inteligencia artificial del programa H2020 desde 2019",
  "meta": {
    "lang": "ES",
    "intent": "Discover",
    "resolvability": "Direct"
  }
}

Example 3: English Quantify Query

Original query: "how many universities participated in quantum computing projects?"

{
  "doc_type": "projects",
  "filters": {
    "programme": null,
    "funding_level": null,
    "year": null,
    "location": null,
    "location_level": null
  },
  "organisations": [
    {
      "type": "university",
      "name": null,
      "location": null,
      "location_level": null
    }
  ],
  "semantic_query": "quantum computing",
  "query_rewrite": "Count of universities participating in quantum computing projects",
  "meta": {
    "lang": "EN",
    "intent": "Quantify",
    "resolvability": "Partial",
    "notes": "Aggregation (count) cannot be expressed in schema"
  }
}

Usage

Loading the Dataset

from datasets import load_dataset

dataset = load_dataset("SIRIS-Lab/impuls-query-parsing")

# Access splits
train_data = dataset["train"]
test_data = dataset["test"]

# Example
print(train_data[0]["meta"]["original_query"])
print(train_data[0]["semantic_query"])

For Training with Transformers

from datasets import load_dataset

dataset = load_dataset("SIRIS-Lab/impuls-query-parsing")

def format_for_training(example):
    # Format as instruction-following
    return {
        "instruction": "Convert this query to structured JSON for R&D project search.",
        "input": example["meta"]["original_query"],
        "output": json.dumps(example, ensure_ascii=False, indent=2)
    }

formatted = dataset.map(format_for_training)

Data Collection

Training Data

The training set was synthetically generated using:

• Controlled vocabularies (funding programmes, organization names, locations)
• Thematic keywords extracted from real R&D project titles and abstracts
• Domain-specific templates mirroring realistic user queries
• Balanced language distribution across Catalan, Spanish, and English

Test Data

The test set contains real queries from domain experts:

• Collected from researchers and R&I policy analysts
• Elicited through structured questionnaires asking for typical information needs
• Manually annotated with gold-standard JSON structures

Intended Use

This dataset is designed for:

• Training query parsing models for R&D project search systems
• Evaluating multilingual NLU capabilities for Catalan, Spanish, and English
• Benchmarking structured output generation from natural language
• Research on semantic parsing for specialized domains

Limitations

• Domain-specific: Focused on R&D project search; may not generalize to other domains
• Schema constraints: Some query types (ranking, complex aggregations) cannot be fully represented
• Synthetic training data: Training examples are template-generated, which may limit diversity
• Language balance: Test set has more English queries than training distribution

Citation

@misc{impuls-query-parsing-2024,
  author = {SIRIS Academic},
  title = {IMPULS Query Parsing Dataset: Multilingual Queries for R&D Semantic Search},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/SIRIS-Lab/impuls-query-parsing}}
}

Acknowledgments

• Barcelona Supercomputing Center (BSC) - AINA project infrastructure
• Generalitat de Catalunya - Funding and RIS3-MCAT platform
• AINA Project - AINA Challenge 2024 framework

License

Apache 2.0

Links

• Query Parser Model: SIRIS-Lab/impuls-salamandra-7b-query-parser
• Project Repository: github.com/sirisacademic/aina-impulse
• SIRIS Academic: sirisacademic.com