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Reflections on: Deep Learning for Noise-Rolerant RDFS Reasoning

Bassem Makni, James A. Hendler
2019 International Semantic Web Conference  
Since the 2001 envisioning of the Semantic Web (SW), the main research focus in SW reasoning has been on the soundness and completeness of reasoners.  ...  While these reasoners assume the veracity of input data, the reality is that the Web of data is inherently noisy.  ...  As [11] focuses only on noisy type assertions, two additional datasets were created with noisy property assertions for the purpose of this research.  ... 
dblp:conf/semweb/MakniH19 fatcat:th3rbfwpi5agrj7u4gnhuq2lyi

Detection of Relation Assertion Errors in Knowledge Graphs

André Melo, Heiko Paulheim
2017 Proceedings of the Knowledge Capture Conference on - K-CAP 2017  
In this paper, we investigate the problem of error detection in relation assertions of knowledge graphs, and we propose an error detection method which relies on path and type features used by a classifier  ...  Although the link prediction problem, where missing relation assertions are predicted, has been widely researched, error detection did not receive as much attention.  ...  ACKNOWLEDGMENTS The work presented in this paper has been partly supported by the Ministry of Science, Research and the Arts Baden-Württemberg in the project SyKo 2 W 2 (Synthesis of Completion and Correction  ... 
doi:10.1145/3148011.3148033 dblp:conf/kcap/MeloP17 fatcat:rgcdkkk32fhspji5i7fnwrykei

Noisy Label Learning for Security Defects [article]

Roland Croft, M. Ali Babar, Huaming Chen
2022 arXiv   pre-print
It results in uncertainty, introduces labeling noise in the datasets and affects conclusion validity.  ...  In this paper, we observe that it is infeasible to obtain a noise-free security defect dataset in practice.  ...  Hence, we are constrained to NLL techniques suitable for detecting semantic label noise. However, we found in RQ3 that the investigated NLL techniques failed to detect these semantic trends.  ... 
arXiv:2203.04468v2 fatcat:bjcscjvqfja75nclnzp3saktqa

Embedding Learning with Triple Trustiness on Noisy Knowledge Graph

Yu Zhao, Huali Feng, Patrick Gallinari
2019 Entropy  
Through extensive experiments on three datasets, we demonstrate that our proposed model can learn better embeddings than all baselines on noisy KGs.  ...  In addition, we present a cross-entropy based loss function for model optimization. In experiments, we evaluate our models on KG noise detection, KG completion and classification.  ...  Melo and Paulheim [26] investigate the problem of error detection in relation assertions of knowledge graphs, and propose an error detection method which relies on path and type features used by a classifier  ... 
doi:10.3390/e21111083 fatcat:bw4rahjiyfeutjfuysxo5ig3di

Matching Patient Records to Clinical Trials Using Ontologies [chapter]

Chintan Patel, James Cimino, Julian Dolby, Achille Fokoue, Aditya Kalyanpur, Aaron Kershenbaum, Li Ma, Edith Schonberg, Kavitha Srinivas
2007 Lecture Notes in Computer Science  
This paper describes a large case study that explores the applicability of ontology reasoning to problems in the medical domain.  ...  Our key insight is that matching patients to clinical trials can be formulated as a problem of semantic retrieval.  ...  Acknowledgements We gratefully acknowledge the help that Bishwaranjan Bhattacharjee provided in tuning our database queries.  ... 
doi:10.1007/978-3-540-76298-0_59 fatcat:sxitrf34anakhajowd4etvammu

LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content [article]

Shreya Gupta, Parantak Singh, Megha Sundriyal, Md Shad Akhtar, Tanmoy Chakraborty
2021 arXiv   pre-print
On our dataset too, LESA outperforms existing baselines by 1 claim-F1 point on the in-domain experiments and 2 claim-F1 points on the general-domain experiments.  ...  Experimental results show that LESA improves upon the state-of-the-art performance across six benchmark claim datasets by an average of 3 claim-F1 points for in-domain experiments and by 2 claim-F1 points  ...  Acknowledgement We would like to thank Rituparna and LCS2 members for helping in data annotation.  ... 
arXiv:2101.11891v1 fatcat:kefl7kms45e2be2ldzafiu4hra

Multi-source named entity typing for social media

Reuth Vexler, Einat Minkov
2016 Proceedings of the Sixth Named Entity Workshop  
Evaluation in the challenging domain of social media shows that multi-source learning improves performance compared with rule-based KB lookups, boosting typing results for some semantic categories.  ...  Typed lexicons that encode knowledge about the semantic types of an entity name, e.g., that 'Paris' denotes a geolocation, product, or person, have proven useful for many text processing tasks.  ...  FB types. 47 semantic tags in total were extracted in this fashion for the string 'eagles'.  ... 
doi:10.18653/v1/w16-2702 dblp:conf/aclnews/VexlerM16 fatcat:bbambkuanfbrrc7a3f3usnsiwi

A scale-free network view of the UMLS to learn terminology translations

Chintan O Patel, James J Cimino
2007 Studies in Health Technology and Informatics  
We characterize the concept hubs into "informational" and "noisy" concept hubs and provide an automated method to detect them.  ...  Using gold standard mappings from SNOMED-CT to ICD9CM, we found an average 20-fold reduction in the number of candidate mappings while achieving comparable recall and ranking results.  ...  Acknowledgements This work was supported in part by the NLM grant R01LM07593.  ... 
pmid:17911805 fatcat:kbnpvksuurbdtgdasqv454voye

VISIR

Sreyasi Nag Chowdhury, Niket Tandon, Hakan Ferhatosmanoglu, Gerhard Weikum
2018 Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining - WSDM '18  
CBIR now gains semantic expressiveness by advances in deep-learning-based detection of visual labels. TBIR benefits from query-and-click logs to automatically infer more informative labels.  ...  This paper addresses the above limitations by semantically refining and expanding the labels suggested by learning-based object detection.  ...  Also, assertions in these knowledge bases are often contradictory and noisy.  ... 
doi:10.1145/3159652.3159693 dblp:conf/wsdm/ChowdhuryTFW18 fatcat:4frchlofofd63o4fdo3utn7zoa

Grounding language acquisition by training semantic parsers using captioned videos

Candace Ross, Andrei Barbu, Yevgeni Berzak, Battushig Myanganbayar, Boris Katz
2018 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing  
We develop a semantic parser that is trained in a grounded setting using pairs of videos captioned with sentences.  ...  For this task, we collected a new dataset for grounded language acquisition.  ...  In addition to the token-constants pairs, there exists a list of pairs of syntactic and semantic types along with placeholders for constants; in the case for chair, a useful type might be λv.  ... 
doi:10.18653/v1/d18-1285 dblp:conf/emnlp/RossBBMK18 fatcat:6h5v4yet5bgk7k3upgkceselqq

How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval [article]

Rodrigo Toro Icarte, Jorge A. Baier, Cristian Ruz, Alvaro Soto
2017 arXiv   pre-print
In our experiments, we show that ConceptNet can improve performance on a common benchmark dataset.  ...  Key to our performance is the use of the ESPGAME dataset to select visually relevant relations from ConceptNet.  ...  Indeed, poor results in [Le et al., 2013] and [Xie and He, 2013] can be attributed to a non-negligible rate of noisy relations in CN.  ... 
arXiv:1705.08844v1 fatcat:q7ilmnh7vnhqtiflv43p4dhide

How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval

Rodrigo Toro Icarte, Jorge A. Baier, Cristian Ruz, Alvaro Soto
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
In our experiments, we show that ConceptNet can improve performance on a common benchmark dataset.  ...  Key to our performance is the use of the ESPGAME dataset to select visually relevant relations from ConceptNet.  ...  This suggests that future works in the intersection of knowledge representation and vision may require special attention to relevance and knowledge base filtering.  ... 
doi:10.24963/ijcai.2017/178 dblp:conf/ijcai/IcarteBRS17 fatcat:dgvlaqbv6fa6xd2exfe3247yqe

A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution [article]

Tuan Lai, Heng Ji, Trung Bui, Quan Hung Tran, Franck Dernoncourt, Walter Chang
2021 arXiv   pre-print
Combined with a simple noisy training method, our best models achieve state-of-the-art results on two datasets: ACE 2005 and KBP 2016.  ...  However, as the input for coreference resolution typically comes from upstream components in the information extraction pipeline, the automatically extracted symbolic features can be noisy and contain  ...  The event detection performance of OneIE on the test set of ACE 2005 is 74.7 (Type-F1 score). OneIE's performance on KBP 2016 is 55.20 (Type-F1 score).  ... 
arXiv:2104.01697v1 fatcat:phirxwj5ingstpievd2loj7o5i

Logic Tensor Networks for Semantic Image Interpretation

Ivan Donadello, Luciano Serafini, Artur d'Avila Garcez
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
Semantic Image Interpretation (SII) is the task of extracting structured semantic descriptions from images.  ...  Logic Tensor Networks (LTNs) are a SRL framework which integrates neural networks with first-order fuzzy logic to allow (i) efficient learning from noisy data in the presence of logical constraints, and  ...  Very large visual recognition datasets now exist which are noisy [24] , and it is important for learning systems to become robust to noise.  ... 
doi:10.24963/ijcai.2017/221 dblp:conf/ijcai/DonadelloSG17 fatcat:2or5jvybsvewjaywlaviejhbku

Abductive Reasoning as Self-Supervision for Common Sense Question Answering [article]

Sathyanarayanan N. Aakur, Sudeep Sarkar
2019 arXiv   pre-print
In this work, we explore the ability of current question-answering models to generalize - to both other domains as well as with restricted training data.  ...  While achieving impressive results on many benchmarks, their performances appear to be proportional to the amount of training data available in the target domain.  ...  There are more than 25 assertions (semantic relations) connecting the concepts, with each assertion specifying and quantifying the semantic relationship between the two concepts The weight of each edge  ... 
arXiv:1909.03099v2 fatcat:3hnmuilu4fej7lq3cdr5rwbzha
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