What is in the KGQA Benchmark Datasets? Survey on Challenges in Datasets for Question Answering on Knowledge Graphs
| Autor: | Nadine Steinmetz, Kai-Uwe Sattler |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Information retrieval
Computer Networks and Communications Computer science 02 engineering and technology computer.file_format Field (computer science) Knowledge graph Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering Question answering Benchmark (computing) SPARQL 020201 artificial intelligence & image processing computer Natural language Information Systems Simple (philosophy) |
| Zdroj: | Journal on Data Semantics. 10:241-265 |
| ISSN: | 1861-2040 1861-2032 |
| Popis: | Question Answering based on Knowledge Graphs (KGQA) still faces difficult challenges when transforming natural language (NL) to SPARQL queries. Simple questions only referring to one triple are answerable by most QA systems, but more complex questions requiring complex queries containing subqueries or several functions are still a tough challenge within this field of research. Evaluation results of QA systems therefore also might depend on the benchmark dataset the system has been tested on. For the purpose to give an overview and reveal specific characteristics, we examined currently available KGQA datasets regarding several challenging aspects. This paper presents a detailed look into the datasets and compares them in terms of challenges a KGQA system is facing. |
| Databáze: | OpenAIRE |
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