Search Results for author:
Found 34 papers, 21 papers with code
Knowledge Graphs
In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data.
Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases
Extending the idea of graph databases (graph DBs), NGDB consists of a Neural Graph Storage and a Neural Graph Engine.
Complex Query Answering with Neural Link Predictors
Finally, we demonstrate that it is possible to explain the outcome of our model in terms of the intermediate solutions identified for each of the complex query atoms.
Ranked #1 on
Complex Query Answering
on NELL995
Message Passing for Complex Question Answering over Knowledge Graphs
We propose a novel approach for complex KGQA that uses unsupervised message passing, which propagates confidence scores obtained by parsing an input question and matching terms in the knowledge graph to a set of possible answers.
Explainable AI for Bioinformatics: Methods, Tools, and Applications
Explainable artificial intelligence (XAI) aims to overcome the opaqueness of black-box models and provide transparency in how AI systems make decisions.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network
Existing data-driven prediction approaches for DDIs typically rely on a single source of information, while using information from multiple sources would help improve predictions.
Inductive Entity Representations from Text via Link Prediction
However, the extent to which these representations learned for link prediction generalize to other tasks is unclear.
Ranked #1 on
Inductive knowledge graph completion
on WN18RR-ind
Inductive knowledge graph completion
Inductive Link Prediction
+8
Query Embedding on Hyper-relational Knowledge Graphs
Besides that, we propose a method to answer such queries and demonstrate in our experiments that qualifiers improve query answering on a diverse set of query patterns.
Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network
Evaluations against several baseline embedding models, e. g., Word2Vec and GloVe yield up to 92. 30%, 82. 25%, and 90. 45% F1-scores in case of document classification, sentiment analysis, and hate speech detection, respectively during 5-fold cross-validation tests.
Measuring Semantic Coherence of a Conversation
Conversational systems have become increasingly popular as a way for humans to interact with computers.
Message Passing Query Embedding
The generality of our method allows it to encode a more diverse set of query types in comparison to previous work.
Convolutional Embedded Networks for Population Scale Clustering and Bio-ancestry Inferencing
The study of genetic variants can help find correlating population groups to identify cohorts that are predisposed to common diseases and explain differences in disease susceptibility and how patients react to drugs.
DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images
Amid the coronavirus disease(COVID-19) pandemic, humanity experiences a rapid increase in infection numbers across the world.
A Machine With Human-Like Memory Systems
Inspired by the cognitive science theory, we explicitly model an agent with both semantic and episodic memory systems, and show that it is better than having just one of the two memory systems.
Prompting as Probing: Using Language Models for Knowledge Base Construction
ProP implements a multi-step approach that combines a variety of prompting techniques to achieve this.
A Machine with Short-Term, Episodic, and Semantic Memory Systems
Inspired by the cognitive science theory of the explicit human memory systems, we have modeled an agent with short-term, episodic, and semantic memory systems, each of which is modeled with a knowledge graph.
BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs
We train models using a biomedical KG containing approximately 2 million triples, and evaluate the performance of the resulting entity embeddings on the tasks of link prediction, and drug-protein interaction prediction, comparing against methods that do not take attribute data into account.
OncoNetExplainer: Explainable Predictions of Cancer Types Based on Gene Expression Data
In this paper, we propose a new approach called OncoNetExplainer to make explainable predictions of cancer types based on GE data.
QAGCN: Answering Multi-Relation Questions via Single-Step Implicit Reasoning over Knowledge Graphs
Multi-relation question answering (QA) is a challenging task, where given questions usually require long reasoning chains in KGs that consist of multiple relations.
Knowledge Representation on the Web revisited: Tools for Prototype Based Ontologies
In this paper we present a practical implementation of a different kind of knowledge representation based on Prototypes.
A First Experiment on Including Text Literals in KGloVe
Graph embedding models produce embedding vectors for entities and relations in Knowledge Graphs, often without taking literal properties into account.
Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases
A natural way to represent the data generated by this large amount of sensors, which are not acting measuring independent variables, and the interaction of the different devices is by using a graph data model.
Transferring knowledge from monitored to unmonitored areas for forecasting parking spaces
Smart cities around the world have begun monitoring parking areas in order to estimate available parking spots and help drivers looking for parking.
Structured Query Construction via Knowledge Graph Embedding
At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query.
Privacy Attacks on Network Embeddings
In that setting, we analyze whether after the removal of the node from the network and the deletion of the vector representation of the respective node in the embedding significant information about the link structure of the removed node is still encoded in the embedding vectors of the remaining nodes.
Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification
Structured querying on such incomplete graphs will result in incomplete sets of answers, even if the correct entities exist in the graph, since one or more edges needed to match the pattern are missing.
Secure Evaluation of Knowledge Graph Merging Gain
During the protocol, the intersection between the two knowledge graphs is determined in a privacy preserving fashion.
Updating Embeddings for Dynamic Knowledge Graphs
To utilize information from Knowledge Graphs, many state-of-the-art machine learning approaches use embedding techniques.
Scaling R-GCN Training with Graph Summarization
The amount of gradient information that needs to be stored during training for real-world graphs is often too large for the amount of memory available on most GPUs.
SlotGAN: Detecting Mentions in Text via Adversarial Distant Learning
We present SlotGAN, a framework for training a mention detection model that only requires unlabeled text and a gazetteer.
Geometric Relational Embeddings: A Survey
Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions.
Hierarchical Multi-label Classification
Knowledge Graph Completion
+1
Approximate Answering of Graph Queries
We will first provide an overview of the different query types which can be supported by these methods and datasets typically used for evaluation, as well as an insight into their limitations.
DONUT-hole: DONUT Sparsification by Harnessing Knowledge and Optimizing Learning Efficiency
This paper introduces DONUT-hole, a sparse OCR-free visual document understanding (VDU) model that addresses the limitations of its predecessor model, dubbed DONUT.
GNN2R: Weakly-Supervised Rationale-Providing Question Answering over Knowledge Graphs
Most current methods for multi-hop question answering (QA) over knowledge graphs (KGs) only provide final conclusive answers without explanations, such as a set of KG entities that is difficult for normal users to review and comprehend.
