SIRIS-Lab/impuls-wikidata-kb

IMPULS R&D Knowledge Base

A multilingual knowledge base of 4,265 R&D concepts derived from Wikidata, designed for query expansion in scientific and research project search systems.

Dataset Description

This knowledge base 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 KB contains scientific and technological concepts with:

• Multilingual labels in Catalan, Spanish, English, and Italian
• Aliases/synonyms for each language
• Definitions where available
• Hierarchical relationships (instance_of, subclass_of) linking to Wikidata

Use Cases

• Query Expansion: Expand search queries with synonyms and related terms across languages
• Multilingual Search: Find equivalent terms across CA/ES/EN/IT
• Concept Navigation: Traverse hierarchical relationships for broader/narrower terms
• Named Entity Linking: Link mentions to Wikidata identifiers

Dataset Structure

Format

JSONL (JSON Lines) - one concept per line.

Schema

{
  "keyword": "machine learning",
  "wikidata_id": "Q2539",
  "languages": {
    "ca": {
      "label": "aprenentatge automàtic",
      "description": "branca de la intel·ligència artificial",
      "also_known_as": ["aprenentatge de màquines", "ML"]
    },
    "es": {
      "label": "aprendizaje automático",
      "description": "rama de la inteligencia artificial",
      "also_known_as": ["aprendizaje de máquina", "ML"]
    },
    "en": {
      "label": "machine learning",
      "description": "branch of artificial intelligence",
      "also_known_as": ["ML", "statistical learning"]
    },
    "it": {
      "label": "apprendimento automatico",
      "description": "ramo dell'intelligenza artificiale",
      "also_known_as": []
    }
  },
  "instance_of": [
    {"id": "Q11660", "label": "artificial intelligence"}
  ],
  "subclass_of": [
    {"id": "Q11660", "label": "artificial intelligence"},
    {"id": "Q816264", "label": "computational learning theory"}
  ]
}

Field Descriptions

Field	Type	Description
keyword	string	Primary English keyword
wikidata_id	string	Wikidata entity ID (Q-number)
languages	object	Multilingual labels, descriptions, and aliases
languages.{lang}.label	string	Primary label in that language
languages.{lang}.description	string	Short description/definition
languages.{lang}.also_known_as	array	Alternative names/synonyms
instance_of	array	Wikidata instance_of relations
subclass_of	array	Wikidata subclass_of relations (for hierarchy traversal)

Statistics

Metric	Value
Total concepts	4,265
With Catalan labels	~4,200
With Spanish labels	~4,250
With English labels	4,265
With Italian labels	~4,100
With subclass_of relations	~3,590
Unique parent concepts	~770 (in KB)

Domain Coverage

The KB focuses on R&D-relevant concepts including:

• Technology: AI, blockchain, IoT, robotics, quantum computing
• Science: biotechnology, nanotechnology, materials science
• Health: medical devices, diagnostics, pharmaceuticals
• Energy: renewables, hydrogen, energy storage
• Environment: climate, sustainability, circular economy
• Industry: manufacturing, automation, Industry 4.0

Examples

Example 1: Technology Concept

{
  "keyword": "blockchain",
  "wikidata_id": "Q20514253",
  "languages": {
    "ca": {
      "label": "cadena de blocs",
      "description": "estructura de dades distribuïda",
      "also_known_as": ["blockchain"]
    },
    "es": {
      "label": "cadena de bloques",
      "description": "base de datos distribuida",
      "also_known_as": ["blockchain"]
    },
    "en": {
      "label": "blockchain",
      "description": "distributed database technology",
      "also_known_as": ["block chain", "distributed ledger"]
    }
  },
  "subclass_of": [
    {"id": "Q8513", "label": "database"}
  ]
}

Example 2: Health Concept

{
  "keyword": "patient",
  "wikidata_id": "Q181600",
  "languages": {
    "ca": {
      "label": "pacient",
      "description": "",
      "also_known_as": []
    },
    "es": {
      "label": "paciente",
      "description": "persona que recibe tratamiento para un problema de salud",
      "also_known_as": ["pacientes", "enfermo"]
    },
    "en": {
      "label": "patient",
      "description": "person who takes a medical treatment",
      "also_known_as": ["patients", "medical patient", "human patient"]
    }
  },
  "instance_of": [
    {"id": "Q214339", "label": "role"}
  ],
  "subclass_of": [
    {"id": "Q12722854", "label": "sick person"},
    {"id": "Q852835", "label": "customer"}
  ]
}

Usage

Loading the Dataset

from datasets import load_dataset

dataset = load_dataset("SIRIS-Lab/impuls-wikidata-kb")
kb = dataset["train"]

print(f"Loaded {len(kb)} concepts")

Query Expansion Example

def find_concept(kb, query):
    """Find concept by keyword or label."""
    query_lower = query.lower()
    for concept in kb:
        if concept["keyword"].lower() == query_lower:
            return concept
        for lang in ["en", "es", "ca"]:
            if concept["languages"].get(lang, {}).get("label", "").lower() == query_lower:
                return concept
    return None

def get_expansions(concept):
    """Get all labels and aliases for a concept."""
    expansions = set()
    for lang_data in concept["languages"].values():
        if lang_data.get("label"):
            expansions.add(lang_data["label"])
        for alias in lang_data.get("also_known_as", []):
            expansions.add(alias)
    return expansions

# Example
concept = find_concept(kb, "machine learning")
if concept:
    print(f"Wikidata ID: {concept['wikidata_id']}")
    print(f"Expansions: {get_expansions(concept)}")
    # Output: {'machine learning', 'ML', 'aprenentatge automàtic', 'aprendizaje automático', ...}

Building a Lookup Index

def build_kb_index(kb):
    """Build wikidata_id -> concept index for fast parent lookup."""
    return {concept["wikidata_id"]: concept for concept in kb}

kb_index = build_kb_index(kb)

# Get parent concepts
concept = find_concept(kb, "deep learning")
for parent in concept.get("subclass_of", []):
    parent_concept = kb_index.get(parent["id"])
    if parent_concept:
        print(f"Parent: {parent_concept['keyword']}")

Data Collection

The knowledge base was built by:

1. Seed Selection: Identifying R&D-relevant concepts from project databases (RIS3CAT, OpenAIRE, CORDIS)
2. Wikidata Extraction: Querying Wikidata API for each concept's labels, aliases, and relations
3. Multilingual Enrichment: Ensuring coverage across CA/ES/EN/IT
4. Hierarchy Validation: Filtering subclass_of relations to include only parents present in the KB
5. Quality Control: Manual review of key domain concepts

Integration with IMPULS

This KB is used by the IMPULS Query Parser for:

• Query Expansion: Adding multilingual synonyms to search queries
• Cross-lingual Search: Finding Spanish projects with Catalan queries
• Concept Navigation: Broadening searches via parent concepts

Limitations

• Domain Focus: Optimized for R&D/scientific concepts; general vocabulary coverage is limited
• Language Coverage: Best coverage in English; some concepts may lack labels in other languages
• Temporal Snapshot: Based on Wikidata as of late 2024; may not reflect recent additions
• Hierarchy Depth: Only direct parents (subclass_of) are included; transitive closure not computed

Citation

@misc{impuls-wikidata-kb-2024,
  author = {SIRIS Academic},
  title = {IMPULS R&D Knowledge Base: Multilingual Wikidata Concepts for Query Expansion},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/SIRIS-Lab/impuls-wikidata-kb}}
}

Acknowledgments

• Wikidata - Source knowledge graph
• Barcelona Supercomputing Center (BSC) - AINA project infrastructure
• Generalitat de Catalunya - Funding and RIS3-MCAT platform

License

Apache 2.0

Related Resources

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