An Exploration of Explainable Machine Learning Using Semantic Web Technology
The behavior of a Machine Learning (ML) algorithm is generally accepted to be a black box, i.e., it cannot be opened and understood. This paper reports on an effort to provide explanation to ML algorithms by using semantic background knowledge. A preliminary paper was found as a project seed, its ex...
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| Vydané v: | 2022 IEEE 16th International Conference on Semantic Computing (ICSC) s. 143 - 146 |
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01.01.2022
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| Abstract | The behavior of a Machine Learning (ML) algorithm is generally accepted to be a black box, i.e., it cannot be opened and understood. This paper reports on an effort to provide explanation to ML algorithms by using semantic background knowledge. A preliminary paper was found as a project seed, its experiment of ML explanation using the DL-Learner tool recreated and semi-automated. DL-Learner is a framework for supervised ML using background knowledge. DL-Learner induces class relationships that hold true for a positive example set. The work presented in this paper is a novel, semi-automated framework for testing the use of DL-Learner in ML explanation. A scene classifier pipeline was created to obtain test data. For the chosen dataset input to the ML, 32 trials were conducted, and explanations produced. Furthermore, this paper reports on the use of DL-Learner as a tool and the lessons learned from its use. DL-Learner, though temporally slow, may prove to be a novel means of ML explanation. |
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| AbstractList | The behavior of a Machine Learning (ML) algorithm is generally accepted to be a black box, i.e., it cannot be opened and understood. This paper reports on an effort to provide explanation to ML algorithms by using semantic background knowledge. A preliminary paper was found as a project seed, its experiment of ML explanation using the DL-Learner tool recreated and semi-automated. DL-Learner is a framework for supervised ML using background knowledge. DL-Learner induces class relationships that hold true for a positive example set. The work presented in this paper is a novel, semi-automated framework for testing the use of DL-Learner in ML explanation. A scene classifier pipeline was created to obtain test data. For the chosen dataset input to the ML, 32 trials were conducted, and explanations produced. Furthermore, this paper reports on the use of DL-Learner as a tool and the lessons learned from its use. DL-Learner, though temporally slow, may prove to be a novel means of ML explanation. |
| Author | Ochoa, Omar Procko, Tyler Elvira, Timothy Del Rio, Nicholas |
| Author_xml | – sequence: 1 givenname: Tyler surname: Procko fullname: Procko, Tyler email: prockot@my.erau.edu organization: Embry-Riddle Aeronautical University,United States of America – sequence: 2 givenname: Timothy surname: Elvira fullname: Elvira, Timothy email: elvirat@my.erau.edu organization: Embry-Riddle Aeronautical University,United States of America – sequence: 3 givenname: Omar surname: Ochoa fullname: Ochoa, Omar email: ochoao@erau.edu organization: Embry-Riddle Aeronautical University,United States of America – sequence: 4 givenname: Nicholas surname: Del Rio fullname: Del Rio, Nicholas email: nicholas.del_rio@afresearchlab.com organization: Air Force Research Lab,United States of America |
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| Snippet | The behavior of a Machine Learning (ML) algorithm is generally accepted to be a black box, i.e., it cannot be opened and understood. This paper reports on an... |
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| SubjectTerms | Artificial neural networks black box DL-Learner explanation Knowledge engineering Machine learning Machine learning algorithms Ontologies semantic web Semantics Training |
| Title | An Exploration of Explainable Machine Learning Using Semantic Web Technology |
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