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A Semantic Approach for Automated Rule Compliance Checking in Construction Industry

Dongming Guo, Erling Onstein, Angela Daniela La Rosa
2021 IEEE Access  
The implementation of supplementing extra data or inferring new knowledge from existing BIM data is called semantic enrichment, which generally requires experts' knowledge and interventions.  ...  implement to link extracted rule information with enriched BIM data. (2) Semantic Enriched BIM Knowledge Base is to enrich BIM model with external supplementary data and functions.  ...  DONGMING GUO received the bachelor and master's degree in computer science from the Southwest Jiaotong University, China.  ... 
doi:10.1109/access.2021.3108226 fatcat:5cdjckggqfcahgfqlvot4exc3u

Comparative Effectiveness of Knowledge Graphs- and EHR Data-Based Medical Concept Embedding for Phenotyping [article]

Junghwan Lee, Cong Liu, Jae Hyun Kim, Alex Butler, Ning Shang, Chao Pang, Karthik Natarajan, Patrick Ryan, Casey Ta, Chunhua Weng
2020 medRxiv   pre-print
Recall@500% and Precision@500% based on a single seed concept of MCE learned using the enriched knowledge graph were 0.64 and 0.13, compared to Recall@500% and Precision@500% of MCE learned using the hierarchical  ...  Knowledge-graphs were obtained from the Observational Medical Outcomes Partnership (OMOP) common data model.  ...  and enriched knowledge graph.  ... 
doi:10.1101/2020.07.14.20151274 fatcat:7fq4aan3pze27ojns4bqm5oi7a

Rule Induction and Reasoning over Knowledge Graphs [chapter]

Daria Stepanova, Mohamed H. Gad-Elrab, Vinh Thinh Ho
2018 Lecture Notes in Computer Science  
We put a particular emphasis on the problems of learning exception-enriched rules from highly biased and incomplete data.  ...  Learning rules from KGs is a crucial task for KG completion, cleaning and curation.  ...  The aim of this article is to survey the current research on rule learning from knowledge graphs.  ... 
doi:10.1007/978-3-030-00338-8_6 fatcat:xvbbxrcdd5efvhqy5is5gfpbge

Rule Learning from Knowledge Graphs Guided by Embedding Models [chapter]

Vinh Thinh Ho, Daria Stepanova, Mohamed H. Gad-Elrab, Evgeny Kharlamov, Gerhard Weikum
2018 Lecture Notes in Computer Science  
Rules over a Knowledge Graph (KG) capture interpretable patterns in data and various methods for rule learning have been proposed.  ...  So it is difficult to learn high-quality rules from the KG alone, and scalability dictates that only a small set of candidate rules is generated.  ...  15 An interesting future direction is to extend our work to more complex non-monotonic rules with higher-arity predicates, aggregates and existential variables or disjunctions in rule heads.  ... 
doi:10.1007/978-3-030-00671-6_5 fatcat:bikowhokvbes5na3ij7dtdrjyy

On the Need to Bootstrap Ontology Learning with Extraction Grammar Learning [chapter]

Georgios Paliouras
2005 Lecture Notes in Computer Science  
The proposed approach is a bootstrapping process that combines ontology and grammar learning, in order to semi-automate the knowledge acquisition process.  ...  , information extraction, grammar induction and ontology enrichment is presented.  ...  In [43] concept lattices are constructed from data with the use of a knowledge acquisition method known as 'ripple-down rules'.  ... 
doi:10.1007/11524564_8 fatcat:444chquckvck7n6mluzi4gaje4

Reflective Metagraph Rewriting as a Foundation for an AGI "Language of Thought" [article]

Ben Goertzel
2021 arXiv   pre-print
IInformally, MeTTa is Hyperon's lowest-level "language of thought" -- the meta-language in which algorithms for learning more particular knowledge representations, will operate, and in which these algorithms  ...  is a distributed metagraph knowledge store (the Atomspace).  ...  rewrite rule p : L r − → R consists of a pattern graph L, a replacement graph R and a partial graph homomorphism r between L and R.  ... 
arXiv:2112.08272v1 fatcat:2smnxnjcbvgl7o3xd3daxgx7de

Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes [article]

Sumit Pai, Luca Costabello
2021 arXiv   pre-print
Numeric values associated to edges of a knowledge graph have been used to represent uncertainty, edge importance, and even out-of-band knowledge in a growing number of scenarios, ranging from genetic data  ...  Experiments with publicly available numeric-enriched knowledge graphs show that our method outperforms traditional numeric-unaware baselines as well as the recent UKGE model.  ...  Nevertheless, such models are not designed to learn from numeric values associated to edges of a knowledge graph.  ... 
arXiv:2105.08683v1 fatcat:7ebd46aqs5cwtljuurwbuqunsy

Maintenance of Discovered Knowledge [chapter]

Michal Pěchouček, Olga Štěpánková, Petr Mikšovský
1999 Lecture Notes in Computer Science  
Different machine learning methodologies can support necessary knowledge-base revision. This process has to be studied along two independent dimensions.  ...  The paper addresses the well-known bottleneck of knowledge based system design and implementation -the issue of knowledge maintenance and knowledge evolution throughout lifecycle of the system.  ...  Inductive logic programming (ILP) enriched the repository of available machine learning methods in the 90ties.  ... 
doi:10.1007/978-3-540-48247-5_61 fatcat:sgfkjzsu3ffg3iok46jxprryky

PairRE: Knowledge Graph Embeddings via Paired Relation Vectors [article]

Linlin Chao, Jianshan He, Taifeng Wang, Wei Chu
2021 arXiv   pre-print
Moreover, We set a new state-of-the-art on two knowledge graph datasets of the challenging Open Graph Benchmark.  ...  Distance based knowledge graph embedding methods show promising results on link prediction task, on which two topics have been widely studied: one is the ability to handle complex relations, such as N-to  ...  These methods enrich the expressiveness of knowledge graph methods with relatively low cost.  ... 
arXiv:2011.03798v3 fatcat:stgf3t2akjfx7p2c32ljr7fwea

KGClean: An Embedding Powered Knowledge Graph Cleaning Framework [article]

Congcong Ge, Yunjun Gao, Honghui Weng, Chong Zhang, Xiaoye Miao, Baihua Zheng
2020 arXiv   pre-print
The quality assurance of the knowledge graph is a prerequisite for various knowledge-driven applications.  ...  KGClean first learns data representations by TransGAT, an effective knowledge graph embedding model, which gathers the neighborhood information of each data and incorporates the interactions among data  ...  KGClean employs the knowledge graph embedding model to automatically learn causalities, which could be considered as rules that can guide value cleaning in a knowledge graph.  ... 
arXiv:2004.14478v1 fatcat:nkbt75j46jfevbxyf5xzght27m

Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer [article]

Hryhorii Chereda, Annalen Bleckmann, Kerstin Menck, Júlia Perera-Bel, Philip Stegmaier, Florian Auer, Frank Kramer, Andreas Leha, Tim Beißbarth
2020 biorxiv/medrxiv   pre-print
Gene expression data can be assigned to the vertices of these graphs. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge.  ...  Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models.  ...  The rules redistribute the relevance from layer to layer starting from output until the input is reached.  ... 
doi:10.1101/2020.08.05.238519 fatcat:7bgnbhlssvdhtajw6vxd73wdjy

Auto-Construction of Course Knowledge Graph based on Course Knowledge

Zhu Peng, Zhong Wei, Yao Xianming
2019 International Journal of Performability Engineering  
Additionally, this paper studies the application of course knowledge graph visualized navigation, providing new methods of course knowledge information construction and decreasing the learning costs for  ...  This paper presents research on the auto-construction of course knowledge graphs based on knowledge graphs.  ...  In applied research on knowledge graphs, learning path recommendation, learning results assessment, and audience transfer learning assistance could also be conducted.  ... 
doi:10.23940/ijpe.19.08.p23.22282236 fatcat:x3qg35ve5bfsjd3hjt4hmexlnm

Finding Flaws from Password Authentication Code in Android Apps [chapter]

Siqi Ma, Elisa Bertino, Surya Nepal, Juanru Li, Diethelm Ostry, Robert H. Deng, Sanjay Jha
2019 Lecture Notes in Computer Science  
Instead of creating detection templates/rules manually, GLACIATE automatically and accurately learns the common authentication flaws from a relatively small training dataset, and then identifies whether  ...  To detect the implementation flaws in password authentication code, we propose GLACIATE, a fully automated tool combining machine learning and program analysis.  ...  Detection Rules Mining GLACIATE learns a rule template from each learning cluster.  ... 
doi:10.1007/978-3-030-29959-0_30 fatcat:goawwvkvobcfhcfq7or2j7vy54

The role of information modeling and automated technologies in the design and construction of high-rise buildings

Vera Cherkina, Elena Petrenko, Maxim Kirichenko, Pavel Samarin, S. Ignateva, M. Shamtsyan
2020 E3S Web of Conferences  
Some problems can be solved through semantic enrichment, in which professional knowledge is used to infer missing information from the target point of view of intelligent tools that receive data from models  ...  While none of these attempts at semantic enrichment using domain-specific knowledge beyond the five main topological relationships, the graphs presented and the use cases they demonstrated with these representations  ... 
doi:10.1051/e3sconf/202021503007 fatcat:u6t5mn76jrcxvovhe64kobqlgq

Building Information Modelling, Artificial Intelligence and Construction Tech

Rafael Sacks, Mark Girolami, Ioannis Brilakis
2020 Developments in the Built Environment  
Building Information Modelling (BIM) itself can be traced to a landmark paper from 1975; ideas for artificially intelligent design and code checking tools date from the mid-1980s; and construction robots  ...  Combined, optimal use of topological rule inferencing and machine learning modules for semantic enrichment 2.  ...  Combined, optimal use of topological rule inferencing and machine learning modules for semantic enrichment 2.  ... 
doi:10.1016/j.dibe.2020.100011 fatcat:z66glw5bcrhelec6pm3ynvomq4
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