Results 41 to 50 of about 8,113 (297)

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

TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph [PDF]

, 2023
Multi-hop logical reasoning over knowledge graph (KG) plays a fundamental role in many artificial intelligence tasks. Recent complex query embedding (CQE) methods for reasoning focus on static KGs, while temporal knowledge graphs (TKGs) have not been ...
Zhou, Gengxian, E, Haihong, Xu, Chengjin, Hu, Tianyi, Li, Ningyuan, Luo, Haoran, Sun, Mingzhi, Lin, Xueyuan, Su, Fenglong   +8 more
core   +1 more source

Enhancing Temporal Knowledge Graph for Future Event Prediction with Long-Term Dense Graph

Tsinghua Science and Technology
Temporal knowledge graph (TKG) reasoning has emerged as a pivotal approach in event prediction. An important yet challenging task in TKG reasoning is to predict future events by extrapolating from historical events and their correlations.
Bin Chen, Jin Wu, Xin Liu, Fan Zhou, Guangchun Luo   +4 more
doaj   +1 more source

DigiChrom: A Domain Ontology for Semantic Representation of Trivalent Chromium Platings and Its Large Language Model‐Based Alignment With Multiple Mid‐Level Ontologies

Advanced Engineering Materials, EarlyView.
Digitalizing electroplating requires both domain knowledge and interoperability. This work introduces PlatOn, a domain ontology for trivalent chromium plating and coating characterization, and a hybrid pipeline that aligns it to a mid‐level reference ontology by combining eight similarity metrics with language model reasoning. Expert‐validated mappings
Janik Harter, Elena Garcia Trelles, Christoph Schweizer, Thomas Seifert, Christoph Baumer, Mahmoud Elkady, Katja Feige, Daniel Grimme, Edgar Hoffmann, Andreas Waibel, Anke Walter   +10 more
wiley   +1 more source

Geometry Interaction Embeddings for Interpolation Temporal Knowledge Graph Completion

Mathematics
Knowledge graphs (KGs) have become a cornerstone for structuring vast amounts of information, enabling sophisticated AI applications across domains. The progression to temporal knowledge graphs (TKGs) introduces time as an essential dimension, allowing ...
Xuechen Zhao, Jinfeng Miao, Fuqiang Yang, Shengnan Pang   +3 more
doaj   +1 more source

Semantic Modeling in Materials Science and Engineering With Platform MaterialDigital Core Ontology 3.0

Advanced Engineering Materials, EarlyView.
The community‐driven Platform MaterialDigital Core Ontology (PMDco) 3.0 is introduced as a Basic Formal Ontology‐aligned semantic backbone for the processing–structure–properties paradigm in Materials Science and Engineering. Modular engineering, automated releases, and validation workflows are highlighted and key semantic patterns for materials ...
Markus Schilling, Philipp von Hartrott, Jörg Waitelonis, Thomas Hanke, Henk Birkholz, Hossein Beygi Nasrabadi, Khashayar Razghandi, Kamilla Zaripova, Felix Thonagel, Fabian Neuhaus, Martin Glauer, Lars Vogt, Harald Sack, Lutz Mädler, Bernd Bayerlein, Chris Eberl   +15 more
wiley   +1 more source

Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning [PDF]

Temporal Knowledge Graph Reasoning (TKGR) is the process of utilizing temporal information to capture complex relations within a Temporal Knowledge Graph (TKG) to infer new knowledge.
Wang, J, Hu, Y, Liew, AWC, Luo, L, Yin, B, Wei, W, Sun, K, Pan, S   +7 more
core   +4 more sources

RPHF-GNN: Recurrent Perception of History-Future Graph Neural Networks for Temporal Knowledge Graph Reasoning

IEEE Access
TKG (Temporal Knowledge Graph) reasoning has become a hot research topic in recent years. its purpose is to predict the future by modeling historical information. However, existing research has primarily focused on comprehending the patterns and rules of
Siling Feng, Zimin Ye, Qian Liu, Mengxing Huang   +3 more
doaj   +1 more source

When Poor Exciton Dissociation Limits Photocurrents in Organic Solar Cells: Why Low Offset Non‐Fullerene Acceptor Blends Can't Be Efficient

Advanced Materials, EarlyView.
The energetic offset between the donor and the acceptor components in organic photoactive layers is central to the tradeoff between photovoltage and photocurrent losses. This Perspective covers the most important issues surrounding this topic in non‐fullerene acceptor blends, from the difficulty of accurately determining state energies and driving ...
Dieter Neher, Manasi Pranav
wiley   +1 more source

Temporal Knowledge Graph Forecasting with Neural ODE [PDF]

, 2022
There has been an increasing interest in inferring future links on temporal knowledge graphs (KG). While links on temporal KGs vary continuously over time, the existing approaches model the temporal KGs in discrete state spaces. To this end, we propose a
Tresp, Volker, Han, Zhen, Ding, Zifeng, Gu, Yujia, Ma, Yunpu   +4 more
core   +1 more source