arXiv research

Paper semantifies bioassay text using neural networks.

This paper evaluates knowledge graph completion models under the open-world assumption, revealing unexpected behavior of metrics.

OGB provides diverse graph datasets for robust ML research.

Pykg2vec is an open-source Python library for learning the representations of the entities and relations in knowledge graphs. Pykg2vec's flexible and modular software architecture currently implements 16 state-of-the-art knowledge graph embedding algorithms, and is designed to easily incorporate new algorithms. The goa…

FinReflectKG builds a comprehensive financial knowledge graph from SEC filings, improving extraction quality.

Developing new ideas and algorithms in the fields of graph processing and relational learning requires public datasets. While Wikidata is the largest open source knowledge graph, involving more than fifty million entities, it is larger than needed in many cases and even too large to be processed easily. Still, it is a …

Machine learning on graph structured data has attracted much research interest due to its ubiquity in real world data. However, how to efficiently represent graph data in a general way is still an open problem. Traditional methods use handcraft graph features in a tabular form but suffer from the defects of domain expe…

Survey of methods to incorporate external knowledge into stock price prediction.

Neural-symbolic model improves link prediction in knowledge graphs.

In a real-world setting, visual recognition systems can be brought to make predictions for images belonging to previously unknown class labels. In order to make semantically meaningful predictions for such inputs, we propose a two-step approach that utilizes information from knowledge graphs. First, a knowledge-graph r…

This paper shows that a simple baseline based on a Bag-of-Words (BoW) representation learns surprisingly good knowledge graph embeddings. By casting knowledge base completion and question answering as supervised classification problems, we observe that modeling co-occurences of entities and relations leads to state-of-…

This work benchmarks neural embeddings for link prediction in evolving knowledge graphs.

Paper proposes KE-GCN for better graph embedding.

The scientific literature is a rich source of information for data mining with conceptual knowledge graphs; the open science movement has enriched this literature with complementary source code that implements scientific models. To exploit this new resource, we construct a knowledge graph using unsupervised learning me…

With the large volume of new information created every day, determining the validity of information in a knowledge graph and filling in its missing parts are crucial tasks for many researchers and practitioners. To address this challenge, a number of knowledge graph completion methods have been developed using low-dime…

Survey of neurosymbolic AI methods for reasoning over knowledge graphs.

FinDKG uses LLMs to detect financial trends from news articles.

CK simplifies ML model deployment and reproducibility with open APIs and DevOps.

Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge. In one prominent example, a causal health knowledge graph could learn relationships between diseases and symptoms and then serve as a diagnostic tool to be refined with additional clinical input. Prior…

RAW-Explainer generates interpretable subgraph explanations for link predictions in knowledge graphs.

A new system recommends specific knowledge concepts in MOOCs based on student interests.

Study benchmarks embedding-based entity alignment methods for KGs.

Graphs evolve; GNNs learn from past tasks.

Survey on using knowledge graphs for better recommender systems.

PyKale bridges interdisciplinary ML with Python, enabling accurate predictions.

KGRL uses reinforcement learning with knowledge graphs for better interactive recommendation.

This paper defines resilience in knowledge graph embeddings and surveys existing works.

A modular framework for knowledge distillation simplifies experiments and reproducibility.

Survey of AI in finance covering models, strategies, and knowledge systems.

Link prediction in large knowledge graphs has received a lot of attention recently because of its importance for inferring missing relations and for completing and improving noisily extracted knowledge graphs. Over the years a number of machine learning researchers have presented various models for predicting the prese…

A new model for graph clustering using curvature spaces.

ConEx learns complex embeddings for knowledge graphs, improving link prediction.

To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existi…

Study combines intra-risk and contagion risk for SME bankruptcy prediction.

Certain research strands can yield "forbidden knowledge". This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics researc…

Study reveals similarities in knowledge flows between pharmaceutical and AI industries.

Proposes statistical inference for dependency knowledge graphs from EHR data.

Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems. In this paper, we consider knowledge graphs as the source of side info…

CHILI datasets tackle inorganic nanomaterials, advancing graph machine learning.

The AI2 Reasoning Challenge (ARC), a new benchmark dataset for question answering (QA) has been recently released. ARC only contains natural science questions authored for human exams, which are hard to answer and require advanced logic reasoning. On the ARC Challenge Set, existing state-of-the-art QA systems fail to s…

There is an emerging trend of embedding knowledge graphs (KGs) in continuous vector spaces in order to use those for machine learning tasks. Recently, many knowledge graph embedding (KGE) models have been proposed that learn low dimensional representations while trying to maintain the structural properties of the KGs s…

The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research. Current architectures only take care of semantic and contextual information for a given query and fail to completely account for syntactic and external knowledge which are cru…

Proposes a method to derive knowledge graphs from EHR data.

Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structur…

PGMax automates PGM inference on GPUs, improving quality and speed.

The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community. The snap increase in the amount and breadth of related research has come at the price of little systematization of knowledge and attention to earlier li…

This paper debiases KG embeddings by filtering sensitive attributes.

Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry. Here we introduce a powerful new approach for learning generative models over graphs, which can capture both …