SPARQL is a powerful query language for an evergrowing number of Semantic Web applications. Using it, however, requires familiarity with the language which is not to be expected from the general web user. This drawback has led to the development of Question-Answering (QA) systems that enable users to express their information needs in natural language. This paper presents a novel dependency-based framework for translating natural language queries into SPARQL queries, which is built on the idea of syntactic parsing. The translation process involves the following steps: extraction of the entities, extraction of the predicate, categorization of the query’s type, resolution of lexical and semantic gaps between user query and domain ontology vocabulary, and finally construction of the SPARQL query. The proposed framework was tested on our closed-domain student advisory application intended to provide students with advice and recommendations about curriculum and scheduling matters. The advantage of our approach is that it requires neither any laborious feature engineering, nor complex model mapping of a query expressed in natural language to a SPARQL query template, and thus it can be easily adapted to a variety of applications.
{"title":"Natural Language to SPARQL Query Builder for Semantic Web Applications","authors":"N. Zlatareva, Devansh Amin","doi":"10.11159/jmids.2021.006","DOIUrl":"https://doi.org/10.11159/jmids.2021.006","url":null,"abstract":"SPARQL is a powerful query language for an evergrowing number of Semantic Web applications. Using it, however, requires familiarity with the language which is not to be expected from the general web user. This drawback has led to the development of Question-Answering (QA) systems that enable users to express their information needs in natural language. This paper presents a novel dependency-based framework for translating natural language queries into SPARQL queries, which is built on the idea of syntactic parsing. The translation process involves the following steps: extraction of the entities, extraction of the predicate, categorization of the query’s type, resolution of lexical and semantic gaps between user query and domain ontology vocabulary, and finally construction of the SPARQL query. The proposed framework was tested on our closed-domain student advisory application intended to provide students with advice and recommendations about curriculum and scheduling matters. The advantage of our approach is that it requires neither any laborious feature engineering, nor complex model mapping of a query expressed in natural language to a SPARQL query template, and thus it can be easily adapted to a variety of applications.","PeriodicalId":430248,"journal":{"name":"Journal of Machine Intelligence and Data Science","volume":"127 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129894478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
– Archival maritime logs are well-preserved treasure throve of climate-related data. The analysis of these documents is instrumental to understanding historical climate trends and future predictions. Transcribing such handwritten logs depends on handwritten letter/digit recognition, which is our aim. The shortcomings of OCR (Optical Character Recognition) are manifesting in frequent confusion of digits and letters when it comes to archival handwritten documents. In this extension of conference and thesis work, two such methods are put to the test – convolutional (CNN) and long-short term memory (LSTM) neural networks (NN). A compound model of convolutional NN followed by LSTM is also considered. While all models register high accuracy, it is observed that the compound model performs faster with accuracy above the lone CNN. We also analyse dataset composition and test for size and balance.
{"title":"Comparison and Evaluation of Data Composition and Deep Learning Models in Archival Handwritten Digit Classification","authors":"Nathan LeBlanc, I. Valova","doi":"10.11159/jmids.2022.001","DOIUrl":"https://doi.org/10.11159/jmids.2022.001","url":null,"abstract":"– Archival maritime logs are well-preserved treasure throve of climate-related data. The analysis of these documents is instrumental to understanding historical climate trends and future predictions. Transcribing such handwritten logs depends on handwritten letter/digit recognition, which is our aim. The shortcomings of OCR (Optical Character Recognition) are manifesting in frequent confusion of digits and letters when it comes to archival handwritten documents. In this extension of conference and thesis work, two such methods are put to the test – convolutional (CNN) and long-short term memory (LSTM) neural networks (NN). A compound model of convolutional NN followed by LSTM is also considered. While all models register high accuracy, it is observed that the compound model performs faster with accuracy above the lone CNN. We also analyse dataset composition and test for size and balance.","PeriodicalId":430248,"journal":{"name":"Journal of Machine Intelligence and Data Science","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128786690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On-line Situational Awareness for Autonomous Driving at Roundabouts using Artificial Intelligence","authors":"Mehran Zamani Abnili, N. Azad","doi":"10.11159/jmids.2021.003","DOIUrl":"https://doi.org/10.11159/jmids.2021.003","url":null,"abstract":"","PeriodicalId":430248,"journal":{"name":"Journal of Machine Intelligence and Data Science","volume":"5 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126987103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Isidora Albijanić, Milica Milošević, Milica Maričić, V. Jeremic
{"title":"Conceptual Model for Exploring Customers’ Online Purchase Intentions","authors":"Isidora Albijanić, Milica Milošević, Milica Maričić, V. Jeremic","doi":"10.11159/jmids.2022.002","DOIUrl":"https://doi.org/10.11159/jmids.2022.002","url":null,"abstract":"","PeriodicalId":430248,"journal":{"name":"Journal of Machine Intelligence and Data Science","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132620034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using Optimal Control Theory to Improve Path Planning For Battery Energy Maximization of an Unmanned Ground Vehicle","authors":"Luke Strebe, Kooktae Lee","doi":"10.11159/jmids.2022.004","DOIUrl":"https://doi.org/10.11159/jmids.2022.004","url":null,"abstract":"","PeriodicalId":430248,"journal":{"name":"Journal of Machine Intelligence and Data Science","volume":"348 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125625654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Modelling and Analysis of a Serial Robotic Arm","authors":"Bin Wei","doi":"10.11159/jmids.2021.002","DOIUrl":"https://doi.org/10.11159/jmids.2021.002","url":null,"abstract":"","PeriodicalId":430248,"journal":{"name":"Journal of Machine Intelligence and Data Science","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128404006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
- A moment-based approximation methodology for estimating a copula density from bivariate observations is introduced. The resulting simple representation of the copula density is suitable for reporting purpose or carrying out further algebraic manipulation. Empirical copula density functions will also be determined from kernel density estimates. A technique for obtaining a joint density from marginal density estimates and a copula density is proposed as well. The Bernstein copula density approximants will be utilized for comparison purposes. The results are applied to two stocks’ closing prices and a stock’s price and its running maximum. In the latter case, the model is related to a Brownian motion process.
{"title":"Practical Representations of Copula and Joint Density Estimates","authors":"Y. Zang, S. Provost","doi":"10.11159/jmids.2023.001","DOIUrl":"https://doi.org/10.11159/jmids.2023.001","url":null,"abstract":"- A moment-based approximation methodology for estimating a copula density from bivariate observations is introduced. The resulting simple representation of the copula density is suitable for reporting purpose or carrying out further algebraic manipulation. Empirical copula density functions will also be determined from kernel density estimates. A technique for obtaining a joint density from marginal density estimates and a copula density is proposed as well. The Bernstein copula density approximants will be utilized for comparison purposes. The results are applied to two stocks’ closing prices and a stock’s price and its running maximum. In the latter case, the model is related to a Brownian motion process.","PeriodicalId":430248,"journal":{"name":"Journal of Machine Intelligence and Data Science","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128579744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Transmitter and Receiver Design for Underwater Communication","authors":"C. M. Atalay, M. Ucuncu","doi":"10.11159/jmids.2022.003","DOIUrl":"https://doi.org/10.11159/jmids.2022.003","url":null,"abstract":"","PeriodicalId":430248,"journal":{"name":"Journal of Machine Intelligence and Data Science","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133079144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"MODBUS: A Target for Covert Communication in Iot Devices","authors":"Sashaa Nagrikar, S. Alshahrani, Daryl Johnson","doi":"10.11159/jmids.2021.007","DOIUrl":"https://doi.org/10.11159/jmids.2021.007","url":null,"abstract":"","PeriodicalId":430248,"journal":{"name":"Journal of Machine Intelligence and Data Science","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116627323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Software Validation and Daubert Standard Compliance of an Open Digital Forensics Model","authors":"A. Carranza, C. DeCusatis","doi":"10.11159/jmids.2021.005","DOIUrl":"https://doi.org/10.11159/jmids.2021.005","url":null,"abstract":"","PeriodicalId":430248,"journal":{"name":"Journal of Machine Intelligence and Data Science","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121735735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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