Using Linguistic Analysis to Translate Arabic Natural Language Queries to SPARQL

AlAgha, Iyad

doi:10.5121/ijwest.2015.6303

Cited by 5 publications

(6 citation statements)

References 26 publications

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“…In [44] , the authors proposed an approach to map Arabic natural language queries into SPARQL format using linguistic analysis. The first step of the mapping process is Noun Phrases (NPs) extractions using language parser.…”

Section: B Question Answering System For Arabic Languagementioning

confidence: 99%

Leveraging Linked Open Data to Automatically Answer Arabic Questions

et al. 2019

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The interchangeably connected Web technologies and the advancements that accompany the semantic web content's leaps, have raised many challenges in the results' retrieval process especially for the Arabic Language. This research targets an important, yet insufficiently precedent, area in using Linked Open Data (LOD) for Automatic Question Answering systems in the Arabic Language. The significance of work presented, comes from its ability to overcome many challenges in querying Arabic content. Some of these challenges are: (a) bridging the gap between natural language and linked data by mapping users' queries to a standard semantic web query language such as SPARQL, (b) facilitating multilingual access to semantic data, and (c) maintaining the quality of data. Another challenging aspect was the lack of related work and publicly available resources for Arabic Question Answering Systems over Linked Data, despite the vastly growing Arabic corpus on the web. This paper presents a novel approach that targets Automatic Arabic Questions' Answering Systems whilst bypassing many featured challenges in the field. A hybrid approach that evaluates the effectiveness of using LOD to automatically answer Arabic questions is developed. The approach is developed to map users' questions in Modern Standard Arabic, to a standard query language for LOD (i.e. SPARQL) through: (i) extracting entities from questions and linking them over the web using Named-Entity Recognition and Disambiguation (NER/NED), and (ii) extracting properties among extracted named entities using a dependency parsing approach integrated with Wikidata ontology. To evaluate our proposed system, an Arabic questions dataset was created including: (a) Question body in Arabic language, (b) Question type, (c) SPARQL Query formulation, and (d) Question answer. Evaluation results are promising with a Precision of 84%, a Recall of 81.3%, and an F-Measure of 82.8%.

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Section: B Question Answering System For Arabic Languagementioning

confidence: 99%

Leveraging Linked Open Data to Automatically Answer Arabic Questions

et al. 2019

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“…It was just to translate the Arabic questions to SPARQL, not to other human-languages. The systems developed in [52,53] taking questions expressed in Arabic and returns the answers drawn from an ontology-based knowledge base through the ontology file was not prepared as the MT system needs. While the translation is done to the SPARQL level only, it would not work as an MT between human-languages, and it would be like the previous works that didn't translate the Arabic question sentences to any other human-languages.…”

Section:  Corpus-based Machine Translationmentioning

confidence: 99%

Machine-Translation History and Evolution: Survey for Arabic-English Translations

Alsohybe

Dahan

Ba-Alwi

2017

CJAST

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As a result of the rapid changes in information and communication technology (ICT), the world has become a small village where people from all over the world connect with each other in dialogue and communication via the Internet. Also, communications have become a daily routine activity due to the new globalization where companies and even universities become global residing cross countries' borders. As a result, translation becomes a needed activity in this connected world. ICT made it possible to have a student in one country take a course or even a degree from a different country anytime anywhere easily. The resulted communication still needs a language as a means that helps the receiver understands the contents of the sent message. People need an automated translation application because human translators are hard to find all the times, and the human translations are very expensive comparing to the translations automated process. Several types of research describe the electronic process of the Machine-Translation. In this paper, the authors are going to study some of these previous researches, and they will explore some of the needed tools Alsohybe et al.; CJAST, 23(4): 1-19, 2017; Article no.CJAST.36124 2 for the Machine-Translation. This research is going to contribute to the Machine-Translation area by helping future researchers to have a summary for the Machine-Translation groups of research and to let lights on the importance of the translation mechanism. Original Research Article

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“…Works by Thompson (2003), Zelle and Mooney (1996) used a subset of the Geo880 corpus. Regarding the transformation of natural language queries in SPARQL we compared our work with AlAgha (2015).…”

Section: Motivation and Goalsmentioning

confidence: 99%

“…The work in (AlAgha, 2015) associates phrases in natural language with RDF triples, as in our approach. Through a linguistic analysis, their model extracts relations and associates them to triples.…”

Section: Translating From Linguistic Analysismentioning

confidence: 99%

“…Accuracy AlAgha (2015) 58.61 Querix Kaufmann et al (2006) 77.67 Our method 78.40 Although we mention the work Kaufmann et al (2006), that also makes use of the Geo880 dataset, we do not make the comparison with it because it does not use the original set of Geo880 queries in their tests. With respect to the syntactical errors, we can see in our tests that the better the model in general, the lower the error rate of syntax.…”

Section: Lstmmentioning

confidence: 99%

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Deep neural semantic parsing: translating from natural language into SPARQL

Luz¹,

Finger²

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This is the original version of the thesis written by the candidate Fabiano Ferreira Luz submitted to the Judging Committee.I would like to thank my advisor Marcelo Finger for allowing me to explore new approaches to semantic parsing, for his support and constructive criticism of my work. I would like to thank all my friends and family who have been on my side during the development of this work. There are so many friends and loved ones that I will not even try to name just to not commit the rudeness of forgetting someone.iii iv Abstract Semantic parsing is the process of mapping a natural-language sentence into a machine-readable, formal representation of its meaning. The LSTM Encoder-Decoder is a neural architecture with the ability to map a source language into a target one. We are interested in the problem of mapping natural language into SPARQL queries, and we seek to contribute with strategies that do not rely on handcrafted rules, high-quality lexicons, manually-built templates or other handmade complex structures. In this context, we present two contributions to the problem of semantic parsing departing from the LSTM encoder-decoder. While natural language has well defined vector representation methods that use a very large volume of texts, formal languages, like SPARQL queries, suffer from lack of suitable methods for vector representation. In the first contribution we improve the representation of SPARQL vectors. We start by obtaining an alignment matrix between the two vocabularies, natural language and SPARQL terms, which allows us to refine a vectorial representation of SPARQL items. With this refinement we obtained better results in the posterior training for the semantic parsing model. In the second contribution we propose a neural architecture, that we call Encoder CFG-Decoder, whose output conforms to a given context-free grammar. Unlike the traditional LSTM encoder-decoder, our model provides a grammatical guarantee for the mapping process, which is particularly important for practical cases where grammatical errors can cause critical failures. Results confirm that any output generated by our model obeys the given CFG, and we observe a translation accuracy improvement when compared with other results from the literature. ResumoA análise semântica é o processo de mapear uma sentença em linguagem natural para uma representação formal, interpretável por máquina, do seu significado. O LSTM Encoder-Decoder é uma arquitetura de rede neural com a capacidade de mapear uma sequência de origem para uma sequência de destino. Estamos interessados no problema de mapear a linguagem natural em consultas SPARQL e procuramos contribuir com estratégias que não dependam de regras artesanais, léxico de alta qualidade, modelos construídos manualmente ou outras estruturas complexas feitas à mão.Neste contexto, apresentamos duas contribuições para o problema de análise semântica partindo da arquitetura LSTM Encoder-Decoder. Enquanto para a linguagem natural existem métodos de representação vetorial bem definidos que u...

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Using Linguistic Analysis to Translate Arabic Natural Language Queries to SPARQL

Cited by 5 publications

References 26 publications

Leveraging Linked Open Data to Automatically Answer Arabic Questions

Leveraging Linked Open Data to Automatically Answer Arabic Questions

Machine-Translation History and Evolution: Survey for Arabic-English Translations

Deep neural semantic parsing: translating from natural language into SPARQL

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