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 Distributed Software Development has become an option for software companies to expand their perspective and work with dispersed teams, exploiting the advantages brought by this approach. However, this way of developing software enables new challenges to arise, such as the inexistence of a formal, normalized model of a project’s data and artifacts accessible to all the individuals involved, which makes it harder for them to communicate, understand each other and what is specified on the project’s artifacts. This paper proposes a knowledge base called DKDOnto, a domain-specific ontology for distributed development, aiming to help projects with a common vocabulary, allowing to assist better the distributed software development process.

The federation of social networks aims at integrating users by means of a decentralized structure, enabling the interoperability among multiple social networks in a transparent way. Despite a few isolated initiatives in federating open social networks, there is no adoption of any standard, which hinders the emergence of new, effective federated systems. To understand the difficulties in the development and standardization of federated services, we have conducted research on existing specifications and implementations of interoperability among social networks. We have developed a federation proof of concept within the Noosfero platform, implementing a subset of the Diaspora protocol to federate users and public content, in addition to complementary specifications, such as Salmon and WebFinger. In this work, we introduce our results to federate Noosfero with Diaspora networks, pointing the required steps before further development. We aim to implement the Diaspora protocol within Noosfero, finishing its specification and improving its documentation, encouraging more projects to adopt this protocol.

Group ranking is a necessary process used to find the best participant from a group. Group ranking has many applications, including online marketing, personal interests matching and proposal ranking. In an online virtual environment, participants want to do group ranking without leaking any of their private information. In this work, we generalize this scenario as a privacy preserving group ranking problem and formulate the privacy requirements of this problem. We propose a fully distributed privacy preserving group ranking framework and prove its security in the honest but curious model. The core of our framework is a novel multiparty sorting protocol, which guarantees that an adversary cannot link the private information to its owner's identity as long as the owner's final ranking is hidden from the adversary. Our protocol is efficient in computational overhead and communication rounds compared to existing works, as demonstrated by our analysis and simulation.

In online surveys, many people are not willing to provide true answers due to privacy concerns. Thus, anonymity is important for online message collection. Existing solutions let each member blindly shuffle the submitted messages by using the IND-CCA2 secure cryptosystem. In the end, all messages are randomly shuffled and no one knows the message order. However, the heavy computational overhead and linear communication rounds make it only useful for small groups. In this paper, we propose an efficient anonymous message submission protocol aimed at a practical group size. Our protocol is based on a simplified secret sharing scheme and a symmetric key cryptosystem. We propose a novel method to let all members secretly aggregate their messages into a message vector such that a member knows nothing about other members' message positions.We provide a theoretical proof showing that our protocol is anonymous under malicious attacks. We then conduct a thorough analysis of our protocol, showing that our protocol is computationally more efficient than existing solutions and results in a constant communication rounds with a high probability.