Utilizing richer information, such as structural and syntactic details, can enhance Natural Language Processing (NLP) tasks like Open Information Extraction (Open IE), particularly for languages with limited resources like Portuguese. Knowledge Graphs (KGs) offer a robust solution by unifying diverse annotations and enabling the application of Graph Machine Learning (Graph ML). This paper presents an advanced framework for Portuguese Open IE, integrating KGs and Graph ML with Large Language Model (LLM) augmentation. Our framework employs a three-stage process: (1) initial Knowledge Graph (KG) construction from text, followed by (2) Predicate Extraction and (3) Subject/Object Extraction, both leveraging GraphSAGE models. Large Language Models (LLMs) (DeepSeek) are used for augmentation when Graph ML predictions are absent or for refining/validating extractions. We present two versions of a system that was evaluated on a Portuguese dataset. Automatic evaluation (word-based) for the best version of the system yielded an F1-score of 64.9% for Predicate extraction and 89.7% for Subject/Object extraction. The final end-to-end performance of the system is an F1-score of 58.2%. A human evaluation was conducted on 51 Portuguese sentences (yielding 100 triples) by two annotators, achieving a substantial agreement (Cohen’s Kappa of 0.67). The system extracted an average of 1.84 triples per sentence, with 53.9% deemed correct. Notably, this version significantly reduced invalid/wrong extractions to 6.6% from 31.7% in the previous version, demonstrating improved Precision while maintaining the ability to extract multiple meaningful triples.

The recent evolution of Carl, an intelligent mobile robot, is presented. The paper focuses on robust spoken language understanding (SLU) and on knowledge representation and reasoning (KRR). Robustness in SLU is achieved through the combination of deep and shallow parsing, tolerating non-grammatical utterances. The KRR module supports the integration of information coming from different interlocutors and is capable of handling contradictory facts. The knowledge representation language is based on semantic networks. Question answering is based on deductive as well as inductive inference. A preliminary evaluation of the efficiency of the SLU/KRR system, for the purpose of knowledge acquisition, is presented.

This paper describes the architecture and current capabilities of Carl, a prototype of an intelligent service robot, designed having in mind such tasks as serving food in a reception or acting as a host in an organization. The approach that has been followed in the design of Carl is based on an explicit concern with the integration of the major dimensions of intelligence, namely communication, action, reasoning and learning. The paper focuses on the multi-modal human-robot communication capabilities of Carl, since these have been significantly improved during the last year.

The development of robots capable of accepting instructions in terms of familiar concepts to the user is still a challenge. For these robots to emerge it’s essential the development of natural language interfaces, since this is regarded as the only interface acceptable for a machine which expected to have a high level of interactivity with Man. Our group has been involved for several years in the development of a mobile intelligent robot, named Carl, designed having in mind such tasks as serving food in a reception or acting as a host in an organization. The approach that has been followed in the design of Carl is based on an ex-plicit concern with the integration of the major dimensions of intelligence, namely Communication, Action, Reasoning and Learning. This paper focuses on the multi-modal human-robot language communication capabilities of Carl, since these have been significantly improved during the last year. 1.