Towards Rare Disease Knowledge Graph Learning from Social Posts of Patients

Frisoni, Giacomo; Moro, Gianluca; Carbonaro, Antonella

doi:10.1007/978-3-030-62066-0_44

Cited by 5 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DDEGK is highly flexible and can be easily applied to new datasets, tasks, and domains, possibly even on generic labeled graphs. For instance, one of the domains on which we pour more expectations is analyzing social posts shared by patients [ 122 , 123 , 124 , 125 ], e.g., detect conversational threads [ 126 ], aggregate and quantify the number of times an adverse drug reaction or symptom manifestation is reported under certain conditions. Four other applications that can benefit from event-enriched knowledge modeling are text classification [ 127 ], long document summarization [ 128 ], tutoring [ 129 , 130 ] and recommender [ 131 , 132 ] systems.…”

Section: Discussionmentioning

confidence: 99%

Unsupervised Event Graph Representation and Similarity Learning on Biomedical Literature

Frisoni

Moro

Carlassare

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

The automatic extraction of biomedical events from the scientific literature has drawn keen interest in the last several years, recognizing complex and semantically rich graphical interactions otherwise buried in texts. However, very few works revolve around learning embeddings or similarity metrics for event graphs. This gap leaves biological relations unlinked and prevents the application of machine learning techniques to promote discoveries. Taking advantage of recent deep graph kernel solutions and pre-trained language models, we propose Deep Divergence Event Graph Kernels (DDEGK), an unsupervised inductive method to map events into low-dimensional vectors, preserving their structural and semantic similarities. Unlike most other systems, DDEGK operates at a graph level and does not require task-specific labels, feature engineering, or known correspondences between nodes. To this end, our solution compares events against a small set of anchor ones, trains cross-graph attention networks for drawing pairwise alignments (bolstering interpretability), and employs transformer-based models to encode continuous attributes. Extensive experiments have been done on nine biomedical datasets. We show that our learned event representations can be effectively employed in tasks such as graph classification, clustering, and visualization, also facilitating downstream semantic textual similarity. Empirical results demonstrate that DDEGK significantly outperforms other state-of-the-art methods.

show abstract

Section: Discussionmentioning

confidence: 99%

Unsupervised Event Graph Representation and Similarity Learning on Biomedical Literature

Frisoni

Moro

Carlassare

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nevertheless, KGs are not the only source of graph data. Graph extraction from text corpora has long been studied in NLP, originating from preliminary research in latent topic modeling [85,86], word relevance estimation [87], deep metric learning [88], and algebraicdriven semantic relationships [89][90][91][92][93]. Nowadays, semantic parsing provides an efficient and simple method to automatically convert natural language utterances into logical forms.…”

Section: Link Prediction On Semantic Parsing Graphsmentioning

confidence: 99%

Comprehensive Analysis of Knowledge Graph Embedding Techniques Benchmarked on Link Prediction

et al. 2022

View full text Add to dashboard Cite

In knowledge graph representation learning, link prediction is among the most popular and influential tasks. Its surge in popularity has resulted in a panoply of orthogonal embedding-based methods projecting entities and relations into low-dimensional continuous vectors. To further enrich the research space, the community witnessed a prolific development of evaluation benchmarks with a variety of structures and domains. Therefore, researchers and practitioners face an unprecedented challenge in effectively identifying the best solution to their needs. To this end, we propose the most comprehensive and up-to-date study to systematically assess the effectiveness and efficiency of embedding models for knowledge graph completion. We compare 13 models on six datasets with different sizes, domains, and relational properties, covering translational, semantic matching, and neural network-based encoders. A fine-grained evaluation is conducted to compare each technique head-to-head in terms of standard metrics, training and evaluation times, memory consumption, carbon footprint, and space geometry. Our results demonstrate the high dependence between performance and graph types, identifying the best options for each scenario. Among all the encoding strategies, the new generation of translational models emerges as the most promising, bringing out the best and most consistent results across all the datasets and evaluation criteria.

show abstract

“…By doing so, for example, a doctor could instantly have access to the number of times patients have complained of a particular symptom or the number of times that a drug has caused a specific side effect, normalizing many lexical and syntactic variations in the text. Alternatively to the analysis of biomedical literature and medical reports, a burgeoning research thread could come from EE on messages shared by patients and caregivers within social communities [315]- [317].…”

Section: Applications and Future Research Directionsmentioning

confidence: 99%

A Survey on Event Extraction for Natural Language Understanding: Riding the Biomedical Literature Wave

Frisoni¹,

Moro

Carbonaro

2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

Motivation: The scientific literature embeds an enormous amount of relational knowledge, encompassing interactions between biomedical entities, like proteins, drugs, and symptoms. To cope with the everincreasing number of publications, researchers are experiencing a surge of interest in extracting valuable, structured, concise, and unambiguous information from plain texts. With the development of deep learning, the granularity of information extraction is evolving from entities and pairwise relations to events. Events can model complex interactions involving multiple participants having a specific semantic role, also handling nested and overlapping definitions. After being studied for years, automatic event extraction is on the road to significantly impact biology in a wide range of applications, from knowledge base enrichment to the formulation of new research hypotheses. Results: This paper provides a comprehensive and up-to-date survey on the link between event extraction and natural language understanding, focusing on the biomedical domain. First, we establish a flexible event definition, summarizing the terminological efforts conducted in various areas. Second, we present the event extraction task, the related challenges, and the available annotated corpora. Third, we deeply explore the most representative methods and present an analysis of the current state-of-the-art, accompanied by performance discussion. To help researchers navigate the avalanche of event extraction works, we provide a detailed taxonomy for classifying the contributions proposed by the community. Fourth, we compare solutions applied in biomedicine with those evaluated in other domains, identifying research opportunities and providing insights for strategies not yet explored. Finally, we discuss applications and our envisions about future perspectives, moving the needle on explainability and knowledge injection.INDEX TERMS Biomedical text mining, event extraction, natural language understanding, semantic parsing.

show abstract

Towards Rare Disease Knowledge Graph Learning from Social Posts of Patients

Cited by 5 publications

References 20 publications

Unsupervised Event Graph Representation and Similarity Learning on Biomedical Literature

Unsupervised Event Graph Representation and Similarity Learning on Biomedical Literature

Comprehensive Analysis of Knowledge Graph Embedding Techniques Benchmarked on Link Prediction

A Survey on Event Extraction for Natural Language Understanding: Riding the Biomedical Literature Wave

Contact Info

Product

Resources

About