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. Keywords: Semantic Role Labeling, Frame Semantics, Framester, Dependency Parsing, Role Oriented Knowledge Graphs 1. Introduction Most knowledge in linked data and knowledge graphs is of a relational nature: people participating in events, products having prices, artifacts with parts, works of art produced by artists, beers sold at a bar, etc. For that reason, a good part of integration and interoperability ends up consisting in aligning relations among heterogeneous schemas and data. This limit makes interoperability difficult.

This work performs verb sense induction and clustering based on observed syntactic distributions in a large corpus. VerbNet is a hierarchical clustering of verbs and a useful semantic resource. We address the main drawbacks of VerbNet, by proposing a Bayesian model to build VerbNet-like clusters automatically and with full coverage. Relative to the prior state of the art, we improve accuracy on verb sense induction by over 20% absolute F1. We then propose a new model, inspired by the positive pointwise mutual information (PPMI). Our PPMI-based mixture model permits an extremely efficient sampler, while improving performance. Our best model shows a 4.5% absolute F1 improvement over the best non-PPMI model, with over an order of magnitude less computation time. Though this model is inspired by clustering verb senses, it may be applicable in other situations where multiple items are being sampled as a group.