Leveraging Predicate-Argument Structures for Knowledge Extraction and Searchable Representation Using RDF
| Autor: | Rita Butkienė, Tomas Vileiniskis |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry semantic role labeling Representation (systemics) Semantic search computer.file_format Predicate (mathematical logic) computer.software_genre RDF knowledge graphs Semantic role labeling Knowledge extraction Knowledge graph semantic search Artificial intelligence Argument (linguistics) business computer Natural language processing |
| Zdroj: | International Journal of Knowledge Engineering. :30-34 |
| ISSN: | 2382-6185 |
| Popis: | Predicate-argument structures are best known as means to represent shallow semantics behind natural language sentences by employing semantic role labeling (SRL) technique. The latter serves as foundation for complex tasks like question answering, text summarization, plagiarism detection and others. In this paper we show how SRL and semantic web technology can be used to build a knowledge graph from open-domain natural language texts with the main goal of enabling semantically-flavored information retrieval on top of the resulting knowledge base. In particular, we propose a domain-agnostic ontology schema capable of capturing event-oriented knowledge and a modification of breadth-first search graph traversal algorithm for serving users information needs. Finally, we evaluate behavior of the whole framework by annotating part of WikiQA dataset and use the constructed knowledge graph to judge information retrieval effectiveness which shows promising results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|