Results 111 to 120 of about 30,602 (303)

Convolutional 2D Knowledge Graph Embeddings

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
Link prediction for knowledge graphs is the task of predicting missing relationships between entities. Previous work on link prediction has focused on shallow, fast models which can scale to large knowledge graphs. However, these models learn less expressive features than deep, multi-layer models — which potentially limits performance ...
Dettmers, Tim, Minervini, Pasquale, Stenetorp, Pontus, Riedel, Sebastian   +3 more
openaire   +4 more sources

TorusE: Knowledge Graph Embedding on a Lie Group [PDF]

, 2018
Knowledge graphs are useful for many artificial intelligence (AI) tasks. However, knowledge graphs often have missing facts. To populate the graphs, knowledge graph embedding models have been developed.
Ebisu, Takuma, Ichise, Ryutaro
core   +1 more source

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

Knowledge Transfer for Out-of-Knowledge-Base Entities: Improving Graph-Neural-Network-Based Embedding Using Convolutional Layers

IEEE Access, 2020
Knowledge base completion (KBC) aims to predict missing information in a knowledge base. Most existing embedding-based KBC models assume that all test entities are available at training time.
Zhongqin Bi, Tianchen Zhang, Ping Zhou, Yongbin Li   +3 more
doaj   +1 more source

The PRIMA Thesaurus for Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
The PRIMA Thesaurus is a structured vocabulary designed to improve how materials science data is described and shared. Developed with input from multiple experts, it enables clear documentation of research workflows, data exchange, and reuse across platforms.
Rossella Aversa, Alexey Boubnov, Dario De Angelis, Catriona Eschke, Maximilian Kabbe, Felix Kraus, Mirco Panighel, Richard Thelen, Davide Valentinis   +8 more
wiley   +1 more source

Semi-Supervised Entity Alignment via Knowledge Graph Embedding with Awareness of Degree Difference [PDF]

, 2019
Entity alignment associates entities in different knowledge graphs if they are semantically same, and has been successfully used in the knowledge graph construction and connection.
Shichao Pei, Zhang, Xiangliang, Pei, Shichao, Lu Yu, Yu, Lu, Robert Hoehndorf, Xiangliang Zhang, Hoehndorf, Robert   +7 more
core   +1 more source

Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure [PDF]

, 2020
Graph embedding has shown its effectiveness to represent graph information and capture deep relationships in graph data. Most recent graph embedding methods focus on attributed graphs, since they preserve both structure and content information in the ...
Jiang, Jing, Shirui Pan, Long, Guodong, Chun Wang, Pan, Shirui, Bo Han, Niu, Gang, Wang, Chun, Jing Jiang, Gang Niu, Guodong Long, Han, Bo   +11 more
core   +1 more source

A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann, Sarath Menon, Raphael Röske, Johannes Steinhülb, Michael Selzer, Jan Janssen, Tilmann Hickel, Ruth Schwaiger, Jörg Neugebauer   +8 more
wiley   +1 more source

Anomalous behavior detection based on optimized graph embedding representation in social networks

Journal of King Saud University: Computer and Information Sciences
Anomalous behaviors in social networks can lead to privacy leaks and the spread of false information. In this paper, we propose an anomalous behavior detection method based on optimized graph embedding representation. Specifically, the user behavior logs
Ling Xing, Shiyu Li, Qi Zhang, Honghai Wu, Huahong Ma, Xiaohui Zhang   +5 more
doaj   +1 more source

Ontology‐Aligned Structuring and Reuse of Multimodal Materials Data and Workflows Toward Automatic Reproduction

Advanced Engineering Materials, EarlyView.
Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari, Abril Azocar Guzman, Sarath Menon, Stefan Sandfeld, Tilmann Hickel, Markus Stricker   +5 more
wiley   +1 more source