PSYCHIC: A Neuro-Symbolic Framework for Knowledge Graph Question-Answering Grounding

The Scholarly Question Answering over Linked Data (Scholarly QALD) at The International Semantic Web Conference (ISWC) 2023 challenge presents two sub-tasks to tackle question answering (QA) over knowledge graphs (KGs). We answer the KGQA over DBLP (DBLP-QUAD) task by proposing a neuro-symbolic (NS) framework based on PSYCHIC, an extractive QA model capable of identifying the query and entities related to a KG question. Our system achieved a F1 score of 00.18% on question answering and came in third place for entity linking (EL) with a score of 71.00%.Comment: 10 pages, 3 figures, 2 tables, accepted for the Scholarly-QALD challenge at the International Semantic Web Conference (ISWC) 202

The Path to Autonomous Learners

In this paper, we present a new theoretical approach for enabling domain knowledge acquisition by intelligent systems. We introduce a hybrid model that starts with minimal input knowledge in the form of an upper ontology of concepts, stores and reasons over this knowledge through a knowledge graph database and learns new information through a Logic Neural Network. We study the behavior of this architecture when handling new data and show that the final system is capable of enriching its current knowledge as well as extending it to new domains

A ML-LLM pairing for better code comment classification

10 pages, 2 figures, 2 tables, accepted for the Information Retrieval in Software Engineering track at the Forum for Information Retrieval Evaluation 2023International audienceThe "Information Retrieval in Software Engineering (IRSE)" at FIRE 2023 shared task introduces code comment classification, a challenging task that pairs a code snippet with a comment that should be evaluated as either useful or not useful to the understanding of the relevant code. We answer the code comment classification shared task challenge by providing a two-fold evaluation: from an algorithmic perspective, we compare the performance of classical machine learning systems and complement our evaluations from a data-driven perspective by generating additional data with the help of large language model (LLM) prompting to measure the potential increase in performance. Our best model, which took second place in the shared task, is a Neural Network with a Macro-F1 score of 88.401% on the provided seed data and a 1.5% overall increase in performance on the data generated by the LLM

PSYCHIC: A Neuro-Symbolic Framework for Knowledge Graph Question-Answering Grounding

10 pages, 3 figures, 2 tables, accepted for the Scholarly-QALD challenge at the International Semantic Web Conference (ISWC) 2023International audienceThe Scholarly Question Answering over Linked Data (Scholarly QALD) at The International Semantic Web Conference (ISWC) 2023 challenge presents two sub-tasks to tackle question answering (QA) over knowledge graphs (KGs). We answer the KGQA over DBLP (DBLP-QUAD) task by proposing a neuro-symbolic (NS) framework based on PSYCHIC, an extractive QA model capable of identifying the query and entities related to a KG question. Our system achieved a F1 score of 00.18% on question answering and came in third place for entity linking (EL) with a score of 71.00%

Well-Written Knowledge Graphs: Most Effective RDF Syntaxes for Triple Linearization in End-to-End Extraction of Relations from Texts (Student Abstract)

International audienceSeq-to-seq generative models recently gained attention forsolving the relation extraction task. By approaching this prob-lem as an end-to-end task, they surpassed encoder-based-onlymodels. Little research investigated the effects of the ouputsyntaxes on the training process of these models. Moreover,a limited number of approaches were proposed for extractingready-to-load knowledge graphs following the RDF standard.In this paper, we consider that a set of triples can be linearizedin many different ways, and we evaluate the combined effectof the size of the language models and different RDF syntaxeson the task of relation extraction from Wikipedia abstracts

Apprentissage d'extracteurs RDF à partir du langage naturel et de graphes de connaissances - application à Wikidata et aux Données ouvertes et liées

International audienceWhether automatically extracted from article structured elements or produced by bots or crowdsourcing, the open and linked data published by DBpedia and Wikidata now offer rich and complementary views of the textual descriptions found in Wikipedia. However, the text of Wikipedia articles still contains a lot of information that it would be interesting to extract for improving the coverage and quality of those bases. Until recently, relation extraction questions were solved by multiple-step processes. The latest improvement in deep learning and the development of large language models have shown their abilities in many downstream and complex tasks, and directly impacted the information extraction field. However, using and restricting these approaches to a given knowledge domain is still an open question. We are presenting here in more details our research questions, and how we are drawing a first overview of the current literature at the intersections of the knowledge graphs and language models fields.Qu'elles soit issues d'éléments structurés d'un article ou le résultat d'une production participatives, les données ouvertes et liées publiées par DBpedia et Wikidata offrent aujourd'hui un point de vue complémentaire vis à vis des descriptions en plein texte de Wikipédia. De plus, le texte de Wikipédia contient un grand nombre d'informations qui pourraient être investies dans l'amélioration de la couverture et de la qualité de ces bases de connaissances. Jusqu'à récemment, la question de l'extraction de relations était résolue à travers une chaîne de processus d'extraction (extraction d'entités nommées, recherche d'URI pour présenter celle-ci, extraction de relations, clustering des relations). Récemment, le développement de large modèles de langue a démontré qu'il était alors possible de réaliser des tâches complexes, et d'investir ces modèles pré-entraînes pour résoudre des problématiques de traitement de la langue plus spécifique. Ce qui impacte aujourd'hui, directement le domaine de l'extraction d'information. Cependant, utiliser et restreindre ces approches à un domaine de connaissance spécifique, est aujourd'hui encore une question ouverte. Nous présentons dans ce poster plus en détail nos questions de recherches, ainsi que l'approche que nous avons suivis pour investir l'état de la littérature sur la question : à l'intersection des graphes de connaissances et du traitement automatique de la langue

The Financial Document Causality Detection Shared Task (FinCausal 2020)

We present the FinCausal 2020 Shared Task on Causality Detection in Financial Documents and the associated FinCausal dataset, and discuss the participating systems and results. Two sub-tasks are proposed: a binary classification task (Task 1) and a relation extraction task (Task 2). A total of 16 teams submitted runs across the two Tasks and 13 of them contributed with a system description paper. This workshop is associated to the Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020), held at The 28th International Conference on Computational Linguistics (COLING'2020), Barcelona, Spain on September 12, 2020

Safe management of bodies of deceased persons with suspected or confirmed COVID-19: a rapid systematic review

Introduction Proper strategies to minimise the risk of infection in individuals handling the bodies of deceased persons infected with 2019 novel coronavirus (2019-nCoV) are urgently needed. The objective of this study was to systematically review the literature to scope and assess the effects of specific strategies for the management of the bodies. Methods We searched five general, three Chinese and four coronavirus disease (COVID-19)–specific electronic databases. We searched registries of clinical trials, websites of governmental and other relevant organisations, reference lists of the included papers and relevant systematic reviews, and Epistemonikos for relevant systematic reviews. We included guidance documents providing practical advice on the handling of bodies of deceased persons with suspected or confirmed COVID-19. Then, we sought primary evidence of any study design reporting on the efficacy and safety of the identified strategies in coronaviruses. We included evidence relevant to contextual factors (ie, acceptability). A single reviewer extracted data using a pilot-tested form and graded the certainty of the evidence using the GRADE approach. A second reviewer verified the data and assessments. Results We identified one study proposing an uncommon strategy for autopsies for patients with severe acute respiratory syndrome. The study provided very low-certainty evidence that it reduced the risk of transmission. We identified 23 guidance documents providing practical advice on the steps of handling the bodies: preparation, packing, and others and advice related to both the handling of the dead bodies and the use of personal protective equipment by individuals handling them. We did not identify COVID-19 evidence relevant to any of these steps. Conclusion While a substantive number of guidance documents propose specific strategies, we identified no study providing direct evidence for the effects of any of those strategies. While this review highlights major research gaps, it allows interested entities to build their own guidance. https://creativecommons.org/licenses/by/4.0