@article {Boumehdi2022585,
	title = {Arabic Speech Recognition Independent of Vocabulary for Isolated Words},
	journal = {Lecture Notes in Networks and Systems},
	volume = {216},
	year = {2022},
	note = {cited By 3},
	pages = {585-595},
	abstract = {This paper presents a vocabulary-independent speech recognition model of isolated Arabic words. This approach uses a restricted dictionary of syllables and phonemes, and it is based on the hidden Markov model (HMM). The main objective of this contribution is to remedy the problem of insufficient vocabularies in automatic speech recognition systems. This new model has made it possible to recognize any given word even if it has never been pronounced. The model gives a very considerable recognition rate comparable to the limited vocabulary speech recognition system of isolated words. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},
	doi = {10.1007/978-981-16-1781-2_52},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115620047\&doi=10.1007\%2f978-981-16-1781-2_52\&partnerID=40\&md5=cf0f1f63d198575b4713769dc56f1701},
	author = {Boumehdi, A. and Yousfi, A.}
}
@conference {Boumehdi2022,
	title = {Comparison of New Approaches of Semi-syllable Units for Speech Recognition of Any Arabic Word},
	booktitle = {Journal of Physics: Conference Series},
	volume = {2337},
	number = {1},
	year = {2022},
	note = {cited By 0},
	abstract = {This study uses the same semi-syllable units already defined in our previous work; a semi-syllable is a simple Arabic letter followed by its diacritic information. This work experiment with three configurations. In the first configuration, we redefine the .,3? (Soukoun is an Arabic diacritical sign representing the absence of a vowel) as a new independent phonemes that is different from the consonant itself. In the second configuration, we also redefine the geminated consonant with a new independent phoneme. In the last configuration, we combined the first two experiments. The results proved that the three configurations gave better results compared to regular semi-syllables, and the highest recognition rate was achieved on the third configuration. {\textcopyright} Published under licence by IOP Publishing Ltd.},
	keywords = {Linguistics, New approaches, Simple++, Speech recognition},
	doi = {10.1088/1742-6596/2337/1/012002},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139524298\&doi=10.1088\%2f1742-6596\%2f2337\%2f1\%2f012002\&partnerID=40\&md5=d95255b2baa9be938dbc80cdf4c31068},
	author = {Boumehdi, A. and Yousfi, A.}
}
@article {Yousfi2022205,
	title = {The Large Annotated Corpus for the Arabic Language (LACAL)},
	journal = {Studies in Computational Intelligence},
	volume = {1061},
	year = {2022},
	note = {cited By 0},
	pages = {205-219},
	abstract = {Annotated corpora has an important role in the NLP field. They are used in almost all NLP applications: automatic dictionary construction, text analysis, information retrieval, machine translation, etc. Annotated corpora are the basis for training operation in NLP systems. Without these corpora, it is difficult to build an efficient system that takes into account all variations and linguistic phenomena. In this paper, we present the annotated corpus we developed. This corpus contains more than 12 million different words labeled by different types of labels: syntactic, morphological, and semantic. This large corpus adds value to the Arabic NLP field, and will certainly improve the quality of the training phase of Arabic NLP systems. Moreover it can be a suitable corpus to test and evaluate the quality of these systems. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-14748-7_12},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139393388\&doi=10.1007\%2f978-3-031-14748-7_12\&partnerID=40\&md5=5e4ea0e7df29b510ad4dae099d9f1725},
	author = {Yousfi, A. and Boumehdi, A. and Laaroussi, S. and Makoudi, R. and Aouragh, S.L. and Gueddah, H. and Habibi, B. and Nejja, M. and Said, I.}
}
@article {Yousfi2021588,
	title = {An Efficient Holistic Schema Matching Approach},
	journal = {Communications in Computer and Information Science},
	volume = {1350},
	year = {2021},
	note = {cited By 0},
	pages = {588-601},
	abstract = {Schema matching allows a certain way of communication between heterogeneous, autonomous and distributed data sources. We choose the matching approach depending on the number of sources we wish to integrate: pairwise matching approaches for a small to a medium total number of data sources, and holistic matching approaches for a big to a huge number of data sources. Nevertheless, current matching approaches were proven to achieve a very moderate matching accuracy. Moreover, holistic matching approaches operate in a series of two-way matching steps. In this paper, we present hMatcher, an efficient holistic schema matching approach. To execute holistic schema matching, hMatcher captures frequent schema elements in the given domain prior to any matching operation. To achieve high matching accuracy, hMatcher uses a context-based semantic similarity measure. Experimental results on real-world domain show that hMatcher performs holistic schema matching properly, and outperforms current matching approaches in terms of matching accuracy. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Computer science, Computers, Context-based semantics, Current matching, Distributed data sources, Holistic schema matching, Number of sources, Pairwise matching, Real world domain, Schema matching, Semantics},
	doi = {10.1007/978-3-030-69143-1_45},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102749207\&doi=10.1007\%2f978-3-030-69143-1_45\&partnerID=40\&md5=d28b470cb44f6ca89c012259c51ec4f6},
	author = {Yousfi, A. and El Yazidi, M.H. and Zellou, A.}
}
@conference {Aouragh2021211,
	title = {A new estimate of the n-gram language model},
	booktitle = {Procedia CIRP},
	volume = {189},
	year = {2021},
	note = {cited By 2},
	pages = {211-215},
	abstract = {Modeling a natural language aims to build systems that are able to reproduce, correct and more or less predict the structure of a given language; moreover it summarizes the general knowledge related to that language. In this context, we have suggested a new language model which efficiently estimate the n-gram language model. This new model has made it possible to remedy the shortcomings of the n-gram language model. By using an automatic spelling error application, the evaluation of this model has been done, and after the comparison of the results obtained; we see that this new model give quick and precise results. These results show that this new proposed model better models the succession of words within sentences, and can therefore replace the n-gram model in Natural Language Processing (NLP) applications. {\textcopyright} 2021 Elsevier B.V.. All rights reserved.},
	keywords = {Arabic languages, Build systems, Computational linguistics, Correction of spelling error, Elsevier, General knowledge, Language modeling, Modeling languages, N-distant-max model, N-gram language models, Natural language processing systems, Natural languages, spelling errors},
	doi = {10.1016/j.procs.2021.05.111},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112416933\&doi=10.1016\%2fj.procs.2021.05.111\&partnerID=40\&md5=b893c0817afa2e87e8bd68f189d60faf},
	author = {Aouragh, S.L. and Yousfi, A. and Laaroussi, S. and Gueddah, H. and Nejja, M.}
}
@article {Yousfi2021129,
	title = {SBP: Preparation of Schema Elements to~Schema Matching},
	journal = {Lecture Notes in Electrical Engineering},
	volume = {733 LNEE},
	year = {2021},
	note = {cited By 0},
	pages = {129-144},
	abstract = {Schema pre-matching is very critical for having schema elements fully ready for schema matching. Ideally, words are first extracted from the schema elements{\textquoteright} labels, and then the semantically corresponding elements are generated accordingly. Searching for the sense of words based on their vertical and horizontal contexts, and before performing schema matching is very crucial to obtain high matching accuracy and as a result increase the amount of accurate matches and reduce the number of inaccurate matches and missed matches. Nonetheless, this problem is much more challenging than it seems. This is because complete and precise information about the meaning behind each element is often unavailable. This paper presents SBP, a Sense-Based Pre-matching approach designed for hierarchical data representations. SBP consists of two main components. First, the words sets generator generates, from each element, a set of words that fully describes its meaning. Second, the words qualifier identifies the senses of words based on both their vertical and horizontal contexts. Experimental results on real-world domains show high matching accuracy obtained when using the SBP approach. {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},
	keywords = {Electrical engineering, Hierarchical data, Mathematical techniques, Pre-matching, Real world domain, Schema matching},
	doi = {10.1007/978-981-33-4909-4_10},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107373285\&doi=10.1007\%2f978-981-33-4909-4_10\&partnerID=40\&md5=8abeb96c3db9e0d4b54e753de8bc994e},
	author = {Yousfi, A. and El Yazidi, M.H. and Zellou, A.}
}
@conference {Boumehdi2020145,
	title = {Construction of a database for speech recognition of isolated Arabic words},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {145-150},
	abstract = {Automatic speech recognition for the Arabic language is a field that is in a current remarkable development and still attracts many researchers who try to improve year after year the recognition rate. Many works nowadays have been focused on automatic speech recognition (ASR) for the Arabic language. The paper presents the significance of the ASR systems built in the past few years. This work also aims to introduce a new Arabic database for isolated word by defining a new concept of phonetic units: semi-syllable units. Thus the corpus contains a collection of semi-syllable audio files as well as their corresponding transcription files. This database will help us in future works. {\textcopyright} 2020 ACM.},
	keywords = {Arabic database, Arabic languages, Audio files, Automatic speech recognition, Database systems, Intelligent systems, Isolated words, Phonetic units, Speech recognition},
	doi = {10.1145/3419604.3419752},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096426261\&doi=10.1145\%2f3419604.3419752\&partnerID=40\&md5=257aeddd47cbb6849d761b06d43eaef2},
	author = {Boumehdi, A. and Yousfi, A.}
}
@conference {Yousfi2020,
	title = {CSSM: A context-based semantic similarity measure},
	booktitle = {2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science, ICECOCS 2020},
	year = {2020},
	note = {cited By 1},
	abstract = {Semantic similarity is very critical in applications that manipulate data across heterogeneous, autonomous and distributed data sources. It helps connect these data sources. Nevertheless, current semantic similarity approaches were proven to achieve a very moderate accuracy. Furthermore, they are merely applicable when we wish to determine the semantic similarity between words. In this paper, we present CSSM, a Context-based Semantic Similarity Measure. Experimental results on Miller Charles benchmark dataset show that CSSM performs semantic similarity comparisons properly, and obtains high accuracy. {\textcopyright} 2020 IEEE.},
	keywords = {Benchmark datasets, Computer science, Computers, Context-based semantics, Control engineering, Data-sources, Distributed data sources, Electronics engineering, Electronics industry, High-accuracy, Semantic similarity, Semantic similarity approaches, Semantics},
	doi = {10.1109/ICECOCS50124.2020.9314376},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100120972\&doi=10.1109\%2fICECOCS50124.2020.9314376\&partnerID=40\&md5=3191da8d69fa40844c69e41c268c5a17},
	author = {Yousfi, A. and El Yazidi, M.H. and Zellou, A.}
}
@conference {Yousfi2020155,
	title = {HASSO: A highly-automated source selection and ordering system based on data quality factors},
	booktitle = {2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020},
	year = {2020},
	note = {cited By 0},
	pages = {155-164},
	abstract = {Big data integration gives access to a large number of data sources through a unified user interface. Answers include high-quality data, medium-quality data, and low-quality data. Selecting a subset of high accurate and consistent data sources and ordering them appropriately is critical to obtain as many high-quality answers as possible right after querying few data sources. However, the process of selecting and ordering data sources can be quite complicated and can present several challenges. The main challenge faced during that process is identifying the most adequate data quality factors to consider. In this paper, we present HASSO, a Highly-Automated Source Selection and Ordering System based on data quality factors. To produce consistent and high accurate answers, HASSO identifies, for each data source, its domain, data consistency and data accuracy using the schema matches. To maximize the total number of complete and non-redundant answers returned right after querying a small number of data sources, HASSO orders data sources in terms of their data overlap and in a decreasing order of their overall coverage. Experimental results in real-world domains show that HASSO produces high-quality answers at high speed. {\textcopyright} 2020 IEEE.},
	keywords = {Data accuracy, Data consistency, Data integration, High quality data, Information systems, Information use, Low quality datum, Number of datum, Order systems, Real world domain, Source selection, User interfaces},
	doi = {10.1109/ICACSIS51025.2020.9263243},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099753285\&doi=10.1109\%2fICACSIS51025.2020.9263243\&partnerID=40\&md5=b3fbfb1a2a633f6f2b8dce8ca9bc392f},
	author = {Yousfi, A. and Yazidi, M.H.E. and Zellou, A.}
}
@conference {Yousfi202085,
	title = {Spm: Preparing hierarchical data structures for schema matching},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {85-90},
	abstract = {Schema pre-matching approaches prepare different schema elements for the matching step. Ideally words are extracted from schema elements{\textquoteright} labels and the semantic correspondences are generated. Identifying the sense of words based on their contexts and before schema matching is critical for increasing the total amount of true matches and decreasing significantly the total amount of both missed matches and false matches. However this problem is very challenging since we often do not have complete and precise information about the meaning behind each element. In this paper we present SPM a Sense-based Pre-Matching approach. SPM has two key modules. First the Sets of Words Generator module generates from every schema element a set of words that describes its meaning. Second the Sense Identifier module selects the most appropriate meaning of words based on their contexts. Experimental results on real-world datasets show high effectiveness achieved by SPM. {\textcopyright} 2020 ACM.},
	keywords = {False matches, Hierarchical data structure, Intelligent systems, Pre-matching, Real-world datasets, Schema matching, Semantic correspondence, Semantics},
	doi = {10.1145/3419604.3419782},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096412741\&doi=10.1145\%2f3419604.3419782\&partnerID=40\&md5=0ace2c18d2f9dd1d741d0d08acb83cfa},
	author = {Yousfi, A. and El Yazidi, M.H. and Zellou, A.}
}
@article {Yousfi20203,
	title = {Towards a Holistic Schema Matching Approach Designed for Large-Scale Schemas},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {12496 LNAI},
	year = {2020},
	note = {cited By 0},
	pages = {3-15},
	abstract = {Holistic schema matching is a fundamental challenge in the big data integration domain. Ideally, clusters of semantically corresponding elements are created and are updated as more schemas are matched. Developing a high-quality holistic schema matching approach is critical for two main reasons. First, identifying as many accurate and holistic semantic correspondences as possible right from the beginning. Second, reducing considerably the search space. Nevertheless, this problem is challenging since overlapping schema elements are not available. Identifying schema overlaps is further complicated for two main reasons: (1) there is a large number of schemas; and (2) overlaps vary for different schemas. In this paper we present HMO, a Holistic schema Matching approach based on schema Overlaps and designed for large-scale schemas. HMO can balance the search space and the quality of the holistic semantic correspondences. To narrow down the search space, HMO matches schemas based on their overlaps. To obtain high-accuracy, HMO uses an existing high-quality semantic similarity measure. Experimental results on four real-world domains show effectiveness and scalability of our matching approach. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Artificial intelligence, Data integration, High quality, High-accuracy, Holistic schema matching, Real world domain, Search spaces, Semantic correspondence, Semantic similarity measures, Semantics},
	doi = {10.1007/978-3-030-63007-2_1},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097530171\&doi=10.1007\%2f978-3-030-63007-2_1\&partnerID=40\&md5=aed75e2b584acebebdce7bf0dc2cc076},
	author = {Yousfi, A. and Yazidi, M.H.E. and Zellou, A.}
}
@article {Yousfi2020655,
	title = {xMatcher: Matching extensible markup language schemas using semantic-based techniques},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {11},
	number = {8},
	year = {2020},
	note = {cited By 8},
	pages = {655-665},
	abstract = {Schema matching is a critical step in data integration systems. Most recent schema matching systems require a manual double-check of the matching results to add missed matches and remove incorrect matches. Manual correction is labor-intensive and time-consuming, however without it the results accuracy is significantly lower. In this paper, we present xMatcher, an approach to automatically match XML schemas. Given two schemas S and S2, xMatcher identifies semantically similar schema elements between S and S2. To obtain correct matches, xMatcher first transforms S and S2 into sets of words; then, it uses a context-based measure to identify the meanings of words in their contexts; next, it captures semantic relatedness between sets of words in different schemas; finally, it uses WordNet information to calculate the similarity values between semantically related sets and matches the pairs of sets whose similarity values are greater than or equal to 0:8. The results show that xMatcher provides superior matching accuracy compared to the state of the art matching systems. Overall, our proposal can be a stepping stone towards decreasing human assistance and overcoming the weaknesses of current matching initiatives in terms of matching accuracy. {\textcopyright} 2020, Science and Information Organization.},
	keywords = {Critical steps, Data integration, Data integration system, Hypertext systems, Labor time, Matching accuracy, Matching system, Matchings, Ontology, Schema matching, Semantic relatedness, Semantic similarity, Semantics, Wordnet, XML},
	doi = {10.14569/IJACSA.2020.0110880},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091018595\&doi=10.14569\%2fIJACSA.2020.0110880\&partnerID=40\&md5=dff26b18d541472b808a6d6c85f08039},
	author = {Yousfi, A. and El Yazidi, M.H. and Zellou, A.}
}
@article {Yousfi201864,
	title = {Assessing the performance of a new semantic similarity measure designed for schema matching for mediation systems},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {11055 LNAI},
	year = {2018},
	pages = {64-74},
	doi = {10.1007/978-3-319-98443-8_7},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053199922\&doi=10.1007\%2f978-3-319-98443-8_7\&partnerID=40\&md5=18d7047f60af1c46cb3beaeb2b8d7e9a},
	author = {Yousfi, A. and Elyazidi, M.H. and Zellou, A.}
}
