Suchergebnisse - "Michael Cochez"

DisambiguART: A Neural-based Inference Model for Knowledge Graph Disambiguation.

Autoren: Subagdja, Budhitama Shanthoshigaa, D. Tan, Ah-Hwee

Quelle: ACM Transactions on Knowledge Discovery from Data; Jul2025, Vol. 19 Issue 6, p1-29, 29p

Schlagwörter: KNOWLEDGE graphs, ARTIFICIAL neural networks, DATA mining, CONTEXTUALISM (Philosophy), SELF-organizing systems, CONTEXTUAL analysis

Full Triple Matcher: Integrating All Triple Elements between Heterogeneous Knowledge Graphs.

Autoren: Yamamoto, Victor Eiti Takeda, Hideaki

Quelle: ACM Transactions on the Web; Nov2025, Vol. 19 Issue 4, p1-32, 32p

Schlagwörter: KNOWLEDGE graphs, DATA integration, MATCHING theory, DATA quality

Editorial of the Special Issue on Deep Learning and Knowledge Graphs.

Autoren: Hitzler, Pascal Janowicz, Krzysztof Alam, Mehwish et al.

Quelle: Semantic Web (1570-0844); 2022, Vol. 13 Issue 3, p293-297, 5p

Schlagwörter: KNOWLEDGE graphs, DEEP learning, NATURAL language processing, KNOWLEDGE representation (Information theory), SCIENTIFIC literature, SEMANTIC Web

Fact Checking Knowledge Graphs - A Survey.

Autoren: Qudus, Umair Röder, Michael Saleem, Muhammad et al.

Quelle: ACM Computing Surveys; Jan2026, Vol. 58 Issue 1, p1-36, 36p

Schlagwörter: FACT checking, KNOWLEDGE graphs, EVALUATION methodology

Fitting Distances by Tree Metrics Minimizing the Total Error within a Constant Factor.

Autoren: Cohen-Addad, Vincent Das, Debarati Kipouridis, Evangelos et al.

Quelle: Journal of the ACM; Apr2024, Vol. 71 Issue 2, p1-41, 41p

Schlagwörter: POLYNOMIAL time algorithms, TREE graphs, APPROXIMATION algorithms

Beyond Decision: Android Malware Description Generation through Profiling Malicious Behavior Trajectory.

Autoren: Wu, Chunlian Chen, Sen Li, Jiaming et al.

Quelle: ACM Transactions on Software Engineering & Methodology; Sep2025, Vol. 34 Issue 7, p1-39, 39p

Schlagwörter: MALWARE, STATISTICAL accuracy, KNOWLEDGE graphs, USER-centered system design

Reviews & Products: ANDROID (Operating system)

Graph Structures for Knowledge Representation and Reasoning

Autoren: Michael Cochez Madalina Croitoru Pierre Marquis et al.

Resource Type: eBook.

Schlagworte: Application software, Computer networks, Artificial intelligence, Knowledge representation (Information theory)--Congresses, Data structures (Computer science)--Congresses, Logic, Symbolic and mathematical

Categories: COMPUTERS / Artificial Intelligence / General, COMPUTERS / Business & Productivity Software / General, COMPUTERS / Computer Science, COMPUTERS / Networking / General

Scholarly knowledge graphs through structuring scholarly communication: a review.

Autoren: Verma, Shilpa Bhatia, Rajesh Harit, Sandeep et al.

Quelle: Complex & Intelligent Systems; Feb2023, Vol. 9 Issue 1, p1059-1095, 37p

Schlagwörter: KNOWLEDGE graphs, SCHOLARLY communication, SCIENTIFIC literature, DATA mining, MACHINE learning, NATURAL language processing

A review of explainable artificial intelligence in supply chain management using neurosymbolic approaches.

Autoren: Kosasih, Edward Elson Papadakis, Emmanuel Baryannis, George et al.

Quelle: International Journal of Production Research; Feb2024, Vol. 62 Issue 4, p1510-1540, 31p

Schlagwörter: SUPPLY chain management, ARTIFICIAL intelligence, SUPPLY chain disruptions, SUPPLY chains, THEMATIC analysis

Beyond scaleup: Knowledge‐aware parsimony learning from deep networks.

Autoren: Yao, Quanming Zhang, Yongqi Wang, Yaqing et al.

Quelle: AI Magazine; Mar2025, Vol. 46 Issue 1, p1-15, 15p

Schlagwörter: DEEP learning, PARSIMONIOUS models, ARTIFICIAL intelligence, LOGIC, SUSTAINABILITY

Combining Machine Learning and Semantic Web: A Systematic Mapping Study.

Autoren: BREIT, ANNA WALTERSDORFER, LAURA EKAPUTRA, FAJAR J. et al.

Quelle: ACM Computing Surveys; 2023 Suppl14s, Vol. 55, p1-41, 41p

Schlagwörter: ARTIFICIAL intelligence, MACHINE learning, SEMANTIC Web, KNOWLEDGE graphs, DEEP learning, KNOWLEDGE representation (Information theory)

XLORE 3: A Large-Scale Multilingual Knowledge Graph from Heterogeneous Wiki Knowledge Resources.

Autoren: Zeng, Kaisheng Jin, Hailong Lv, Xin et al.

Quelle: ACM Transactions on Information Systems; Nov2024, Vol. 42 Issue 6, p1-47, 47p

Neural-Symbolic Methods for Knowledge Graph Reasoning: A Survey.

Autoren: Cheng, Kewei Ahmed, Nesreen K. Rossi, Ryan A. et al.

Quelle: ACM Transactions on Knowledge Discovery from Data; Nov2024, Vol. 18 Issue 9, p1-44, 44p

Schlagwörter: KNOWLEDGE graphs, MATHEMATICAL logic, RESEARCH personnel

From document-centric to data-centric public service provision.

Autoren: Konstantinidis, Ioannis Kapantai, Eleni Michailidis, Alexios et al.

Quelle: Digital Government: Research & Practice; Sep2024, Vol. 5 Issue 3, p1-27, 27p

Schlagwörter: PUBLIC services, DIGITAL transformation, TECHNOLOGICAL innovations, PUBLIC architecture, KNOWLEDGE graphs

Neighbor-Enhanced Representation Learning for Link Prediction in Dynamic Heterogeneous Attributed Networks.

Autoren: Wei, Xiangyu Wang, Wei Zhang, Chongsheng et al.

Quelle: ACM Transactions on Knowledge Discovery from Data; Sep2024, Vol. 18 Issue 8, p1-25, 25p

Schlagwörter: GRAPH neural networks, MACHINE learning, HEURISTIC, NEIGHBORHOODS

Completeness, Recall, and Negation in Open-world Knowledge Bases: A Survey.

Autoren: Razniewski, Simon Arnaout, Hiba Ghosh, Shrestha et al.

Quelle: ACM Computing Surveys; Jun2024, Vol. 56 Issue 6, p1-42, 42p

Schlagwörter: ARTIFICIAL neural networks, LANGUAGE models, QUESTION & answer websites, SCIENTIFIC knowledge, NATURAL language processing, KNOWLEDGE base, QUESTION answering systems, ASPIRATORS

Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces.

Autoren: Cao, Jiahang Fang, Jinyuan Meng, Zaiqiao et al.

Quelle: ACM Computing Surveys; Jun2024, Vol. 56 Issue 6, p1-42, 42p

Schlagwörter: ARTIFICIAL neural networks, MACHINE learning, SCIENTIFIC literature, NATURAL language processing, LANGUAGE models, TRIANGLES

A Comprehensive Survey on Automatic Knowledge Graph Construction.

Autoren: LINGFENG ZHONG JIA WU QIAN LI et al.

Quelle: ACM Computing Surveys; Apr2024, Vol. 56 Issue 4, p1-62, 62p

Schlagwörter: ARTIFICIAL neural networks, GENERATIVE artificial intelligence, GRAPH neural networks, LANGUAGE models, KNOWLEDGE graphs, DEEP learning, USER-generated content, GRAPH algorithms, LATENT variables

Urban Knowledge Graph Aided Mobile User Profiling.

Autoren: YU LIU ZHILUN ZHOU YONG LI et al.

Quelle: ACM Transactions on Knowledge Discovery from Data; Jan2024, Vol. 18 Issue 1, p1-30, 30p

Schlagwörter: KNOWLEDGE graphs, ARTIFICIAL intelligence, WEB-based user interfaces

BioBLP: a modular framework for learning on multimodal biomedical knowledge graphs.

Autoren: Daza, Daniel Alivanistos, Dimitrios Mitra, Payal et al.

Quelle: Journal of Biomedical Semantics; 12/8/2023, Vol. 14 Issue 1, p1-19, 19p

Schlagwörter: KNOWLEDGE graphs, MOLECULAR graphs, AMINO acid sequence, SCIENTIFIC discoveries