On Verifying Complex Properties using Symbolic Shape Analysis

By Thomas Wies, Viktor Kunčak, Karen Zee, Andreas Podelski, Martin C. Rinard. arXiv.org, 2006

450 citations recorded by the source corpus.

Note: MPI-I-2006-2-1 (Max-Planck Institute for Computer Science) Reference LARA-REPORT-2006-002 URL: http://arxiv.org/abs/cs.PL/0609104 Record created on 2007-08-21, modified on 2017-05-12

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1f49f3f50b5bc1c81c0677eb88c50d3d17e4705e

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Citations in the corpus

Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs (2015, methodology): “Learning social knowledge graphs has potential applications in user modeling, recommendation, and knowledge-based search [ Sig-urbj¨ornsson and Van Zwol, 2008; Kasneci et al. , 2008; Tang et al. , 2008 ] .”
Efficient Subgraph Matching on Billion Node Graphs (2012, background): “Subgraph matching is one of the most fundamental operators in many applications that handle graphs, including protein-protein interaction networks [15, 36], knowledge bases [19, 27], and program analysis [34, 33].”
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network (2017, background): “These KBs are useful resources in many applications such as semantic searching and ranking (Kasneci et al., 2008; Schuhmacher and Ponzetto, 2014; Xiong et al., 2017), question answering (Zhang et al., 2016; Hao et al., 2017) and machine reading (Yang and Mitchell, 2017).”
DELIS Dynamically Evolving , Large-scale Information Systems Integrated Project Member of the FET Proactive Initiative Complex Systems DELIS-TR-594 Yago : A Large Ontology from Wikipedia and WordNet (2007, methodology): “In addition, there are emerging trends towards entity- and fact-oriented Web search and community management [16, 28, 33, 36, 52, 87, 99, 109, 111], which can build on rich knowledge bases.”
Knowledge Graph and Corpus Driven Segmentation and Answer Inference for Telegraphic Entity-seeking Queries (2014, background): “The NLP/QA community has traditionally assumed that question utterances are grammatically well-formed, from which precise clause structure, ground constants, variables, and connective relations can be inferred via semantic parsing (Kasneci et al., 2008; Pound et al., 2012; Yahya et al., 2012; Berant”
YAGO: A Large Ontology from Wikipedia and WordNet (2008, methodology): “[6,12,13,16,21,32,35,37,38], which can build on rich knowledge bases.”