Semantic Role Labeling for Knowledge Graph Extraction from Text
This work addresses knowledge graph extraction for natural language processing applications, but it is incremental as it builds on existing semantic role labeling techniques.
The paper tackles the problem of extracting knowledge graphs from text by introducing TakeFive, a semantic role labeling method that transforms text into frame-oriented knowledge graphs, achieving competitive precision, recall, and F1 scores compared to existing methods like SEMAFOR and FRED, with further improvements when combined with FRED.
This paper introduces TakeFive, a new semantic role labeling method that transforms a text into a frame-oriented knowledge graph. It performs dependency parsing, identifies the words that evoke lexical frames, locates the roles and fillers for each frame, runs coercion techniques, and formalises the results as a knowledge graph. This formal representation complies with the frame semantics used in Framester, a factual-linguistic linked data resource. The obtained precision, recall and F1 values indicate that TakeFive is competitive with other existing methods such as SEMAFOR, Pikes, PathLSTM and FRED. We finally discuss how to combine TakeFive and FRED, obtaining higher values of precision, recall and F1.