Search Results for author: Abraham Bernstein

Found 10 papers, 0 papers with code

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

Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks

no code implementations18 Feb 2021 Bibek Paudel, Abraham Bernstein

Most existing personalization systems promote items that match a user's previous choices or those that are popular among similar users.

Implementations in Machine Ethics: A Survey

no code implementations21 Jan 2020 Suzanne Tolmeijer, Markus Kneer, Cristina Sarasua, Markus Christen, Abraham Bernstein

Increasingly complex and autonomous systems require machine ethics to maximize the benefits and minimize the risks to society arising from the new technology.


Cross-Cutting Political Awareness through Diverse News Recommendations

no code implementations3 Sep 2019 Bibek Paudel, Abraham Bernstein

The suggestions generated by most existing recommender systems are known to suffer from a lack of diversity, and other issues like popularity bias.

Recommendation Systems

A Comparative Survey of Recent Natural Language Interfaces for Databases

no code implementations21 Jun 2019 Katrin Affolter, Kurt Stockinger, Abraham Bernstein

Each of the systems is evaluated using a curated list of ten sample questions to show their strengths and weaknesses.

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

no code implementations21 Mar 2019 Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei zhang, Abraham Bernstein, Huajun Chen

We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently.

Entity Embeddings Knowledge Graphs +1

Interaction Embeddings for Prediction and Explanation in Knowledge Graphs

no code implementations12 Mar 2019 Wen Zhang, Bibek Paudel, Wei zhang, Abraham Bernstein, Huajun Chen

Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications.

Knowledge Graph Embedding Knowledge Graphs +1

FedMark: A Marketplace for Federated Data on the Web

no code implementations20 Aug 2018 Tobias Grubenmann, Abraham Bernstein, Dmitry Moor, Sven Seuken

The problem is that it is not clear how publishers of commercial data can monetize their data in this new setting.


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