Search Results for author: Michael Cochez

Found 33 papers, 19 papers with code

SlotGAN: Detecting Mentions in Text via Adversarial Distant Learning

no code implementations spnlp (ACL) 2022 Daniel Daza, Michael Cochez, Paul Groth

We present SlotGAN, a framework for training a mention detection model that only requires unlabeled text and a gazetteer.

Approximate Answering of Graph Queries

no code implementations12 Aug 2023 Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan, Max Berrendorf, Daniel Daza, Mikhail Galkin, Pasquale Minervini, Mathias Niepert, Hongyu Ren

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.

Knowledge Graphs

BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs

1 code implementation6 Jun 2023 Daniel Daza, Dimitrios Alivanistos, Payal Mitra, Thom Pijnenburg, Michael Cochez, Paul Groth

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.

Entity Embeddings Knowledge Graphs +1

Geometric Relational Embeddings: A Survey

no code implementations24 Apr 2023 Bo Xiong, Mojtaba Nayyeri, Ming Jin, Yunjie He, Michael Cochez, Shirui Pan, Steffen Staab

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

Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases

1 code implementation26 Mar 2023 Hongyu Ren, Mikhail Galkin, Michael Cochez, Zhaocheng Zhu, Jure Leskovec

Extending the idea of graph databases (graph DBs), NGDB consists of a Neural Graph Storage and a Neural Graph Engine.

Link Prediction Logical Reasoning +1

Adapting Neural Link Predictors for Data-Efficient Complex Query Answering

no code implementations29 Jan 2023 Erik Arakelyan, Pasquale Minervini, Daniel Daza, Michael Cochez, Isabelle Augenstein

We propose to address these problems via CQD$^{\mathcal{A}}$, a parameter-efficient score \emph{adaptation} model optimised to re-calibrate neural link prediction scores for the complex query answering task.

Complex Query Answering Link Prediction

A Machine with Short-Term, Episodic, and Semantic Memory Systems

2 code implementations5 Dec 2022 Taewoon Kim, Michael Cochez, Vincent François-Lavet, Mark Neerincx, Piek Vossen

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.

Q-Learning Reinforcement Learning (RL)

QAGCN: A Graph Convolutional Network-based Multi-Relation Question Answering System

no code implementations3 Jun 2022 Ruijie Wang, Luca Rossetto, Michael Cochez, Abraham Bernstein

Reasoning-based methods with complex reasoning mechanisms, such as reinforcement learning-based sequential decision making, have been regarded as the default pathway for this task.

Decision Making Knowledge Graphs +1

A Machine With Human-Like Memory Systems

1 code implementation4 Apr 2022 Taewoon Kim, Michael Cochez, Vincent Francois-Lavet, Mark Neerincx, Piek Vossen

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.

OpenAI Gym

Scaling R-GCN Training with Graph Summarization

no code implementations5 Mar 2022 Alessandro Generale, Till Blume, Michael Cochez

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.

Knowledge Graphs

Updating Embeddings for Dynamic Knowledge Graphs

no code implementations22 Sep 2021 Christopher Wewer, Florian Lemmerich, Michael Cochez

To utilize information from Knowledge Graphs, many state-of-the-art machine learning approaches use embedding techniques.

Knowledge Graphs Link Prediction

Query Embedding on Hyper-relational Knowledge Graphs

1 code implementation ICLR 2022 Dimitrios Alivanistos, Max Berrendorf, Michael Cochez, Mikhail Galkin

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.

Knowledge Graphs Link Prediction +2

Secure Evaluation of Knowledge Graph Merging Gain

1 code implementation26 Feb 2021 Leandro Eichenberger, Michael Cochez, Benjamin Heitmann, Stefan Decker

During the protocol, the intersection between the two knowledge graphs is determined in a privacy preserving fashion.

Knowledge Graphs Privacy Preserving

Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification

no code implementations22 Feb 2021 Ruud van Bakel, Teodor Aleksiev, Daniel Daza, Dimitrios Alivanistos, Michael Cochez

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.

Binary Classification Classification +4

Complex Query Answering with Neural Link Predictors

3 code implementations ICLR 2021 Erik Arakelyan, Daniel Daza, Pasquale Minervini, Michael Cochez

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.

Complex Query Answering

Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network

1 code implementation11 Apr 2020 Md. Rezaul Karim, Bharathi Raja Chakravarthi, John P. McCrae, Michael Cochez

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.

Classification Document Classification +4

DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images

1 code implementation9 Apr 2020 Md. Rezaul Karim, Till Döhmen, Dietrich Rebholz-Schuhmann, Stefan Decker, Michael Cochez, Oya Beyan

Amid the coronavirus disease(COVID-19) pandemic, humanity experiences a rapid increase in infection numbers across the world.

COVID-19 Diagnosis

Knowledge Graphs

2 code implementations4 Mar 2020 Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, José Emilio Labra Gayo, Sabrina Kirrane, Sebastian Neumaier, Axel Polleres, Roberto Navigli, Axel-Cyrille Ngonga Ngomo, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, Antoine Zimmermann

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.

Knowledge Graphs

Message Passing Query Embedding

1 code implementation6 Feb 2020 Daniel Daza, Michael Cochez

The generality of our method allows it to encode a more diverse set of query types in comparison to previous work.

Entity Embeddings Knowledge Graphs +2

Privacy Attacks on Network Embeddings

no code implementations23 Dec 2019 Michael Ellers, Michael Cochez, Tobias Schumacher, Markus Strohmaier, Florian Lemmerich

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.

Network Embedding

OncoNetExplainer: Explainable Predictions of Cancer Types Based on Gene Expression Data

1 code implementation9 Sep 2019 Md. Rezaul Karim, Michael Cochez, Oya Beyan, Stefan Decker, Christoph Lange

In this paper, we propose a new approach called OncoNetExplainer to make explainable predictions of cancer types based on GE data.

Feature Importance

Structured Query Construction via Knowledge Graph Embedding

no code implementations6 Sep 2019 Ruijie Wang, Meng Wang, Jun Liu, Michael Cochez, Stefan Decker

At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query.

Knowledge Graph Embedding Knowledge Graphs

Message Passing for Complex Question Answering over Knowledge Graphs

1 code implementation19 Aug 2019 Svitlana Vakulenko, Javier David Fernandez Garcia, Axel Polleres, Maarten de Rijke, Michael Cochez

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.

Knowledge Graphs Question Answering

Transferring knowledge from monitored to unmonitored areas for forecasting parking spaces

no code implementations7 Aug 2019 Andrei Ionita, André Pomp, Michael Cochez, Tobias Meisen, Stefan Decker

Smart cities around the world have begun monitoring parking areas in order to estimate available parking spots and help drivers looking for parking.

Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network

1 code implementation4 Aug 2019 Md. Rezaul Karim, Michael Cochez, Joao Bosco Jares, Mamtaz Uddin, Oya Beyan, Stefan Decker

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.

BIG-bench Machine Learning Knowledge Graph Embeddings +2

A First Experiment on Including Text Literals in KGloVe

no code implementations31 Jul 2018 Michael Cochez, Martina Garofalo, Jérôme Lenßen, Maria Angela Pellegrino

Graph embedding models produce embedding vectors for entities and relations in Knowledge Graphs, often without taking literal properties into account.

BIG-bench Machine Learning Graph Embedding +1

Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases

no code implementations31 Jul 2018 Martina Garofalo, Maria Angela Pellegrino, Abdulrahman Altabba, Michael Cochez

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.

BIG-bench Machine Learning Feature Engineering +2

Measuring Semantic Coherence of a Conversation

2 code implementations17 Jun 2018 Svitlana Vakulenko, Maarten de Rijke, Michael Cochez, Vadim Savenkov, Axel Polleres

Conversational systems have become increasingly popular as a way for humans to interact with computers.

Knowledge Graphs

Convolutional Embedded Networks for Population Scale Clustering and Bio-ancestry Inferencing

4 code implementations30 May 2018 Md. Rezaul Karim, Michael Cochez, Achille Zappa, Ratnesh Sahay, Oya Beyan, Dietrich-Rebholz Schuhmann, Stefan Decker

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.

Clustering feature selection +1

Knowledge Representation on the Web revisited: Tools for Prototype Based Ontologies

no code implementations16 Jul 2016 Michael Cochez, Stefan Decker, Eric Prud'hommeaux

In this paper we present a practical implementation of a different kind of knowledge representation based on Prototypes.

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