Search Results for author:
Found 14 papers, 4 papers with code
FedMark: A Marketplace for Federated Data on the Web
The problem is that it is not clear how publishers of commercial data can monetize their data in this new setting.
Databases
Loss Aversion in Recommender Systems: Utilizing Negative User Preference to Improve Recommendation Quality
Negative user preference is an important context that is not sufficiently utilized by many existing recommender systems.
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications.
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
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.
A Comparative Survey of Recent Natural Language Interfaces for Databases
Each of the systems is evaluated using a curated list of ten sample questions to show their strengths and weaknesses.
Cross-Cutting Political Awareness through Diverse News Recommendations
The suggestions generated by most existing recommender systems are known to suffer from a lack of diversity, and other issues like popularity bias.
Implementations in Machine Ethics: A Survey
Increasingly complex and autonomous systems require machine ethics to maximize the benefits and minimize the risks to society arising from the new technology.
Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks
Most existing personalization systems promote items that match a user's previous choices or those that are popular among similar users.
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.
NLQxform: A Language Model-based Question to SPARQL Transformer
In recent years, scholarly data has grown dramatically in terms of both scale and complexity.
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.
NLQxform-UI: A Natural Language Interface for Querying DBLP Interactively
In recent years, the DBLP computer science bibliography has been prominently used for searching scholarly information, such as publications, scholars, and venues.
Fast and Adaptive Questionnaires for Voting Advice Applications
Our findings indicate that employing the IDEAL model both as encoder and decoder, combined with a PosteriorRMSE method for question selection, significantly improves the accuracy of recommendations, achieving 74% accuracy after asking the same number of questions as in the condensed version.
Entity Prediction in Knowledge Graphs with Joint Embeddings
Our method leverages joint embedding models, hence does not require entities or relations to be named explicitly.
