Azhary: An Arabic Lexical Ontology

Ishkewy, Hossam; Harb, Hany M.; Farahat, Hassan

doi:10.5121/ijwest.2014.5405

Cited by 28 publications

(18 citation statements)

References 11 publications

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“…In general, the studies that addressed the support of Arabic language on the SW can be divided into four categories [7]: 1) the development of Arabic ontologies [18,20,21], 2) Employing ontologies to improve Arabic named entities extraction [4,36], 3) Ontology based representation of Islamic knowledge [2,19,24] and 4) supporting cross-language information retrieval and search [17,33]. Although an increasing number of efforts have started to use ontologies to enhance information retrieval from Arabic data [29,31], the use of ontologies was almost limited to query expansion, and results were retrieved from unstructured data on the Web.…”

Section: Support For Arabic Language On the Swmentioning

confidence: 99%

AR2SPARQL: An Arabic Natural Language Interface for the Semantic Web

AlAgha¹,

Abu-Taha²

2015

IJCA

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With the growing interest in supporting the Arabic language on the Semantic Web (SW), there is an emerging need to enable Arab users to query ontologies and RDF stores without being challenged with the formal logic of the SW. In the domain of English language, several efforts provided Natural Language (NL) interfaces to enable ordinary users to query ontologies using NL queries. However, none of these efforts were designed to support the Arabic language which has different morphological and semantic structures.As a step towards supporting Arabic Question Answering (QA) on the SW, this work presents AR2SPARQL, a NL interface that takes questions expressed in Arabic and returns answers drawn from an ontology-based knowledge base. The core of AR2SPARQL is the approach we propose to translate Arabic questions into triples which are matched against RDF data to retrieve an answer. The system uses both linguistic and semantic features to resolve ambiguity when matching words to the ontology content. To overcome the limited support for Arabic Natural Language Processing (NLP), the system does not make intensive use of sophisticated linguistic methods. Instead, it relies more on the knowledge defined in the ontology and the grammar rules we define to capture the structures of Arabic questions and to construct an adequate RDF representations. AR2SPARQL has been tested with two different datasets and results have shown that it achieves a good retrieval performance in terms of precision and recall.

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Section: Support For Arabic Language On the Swmentioning

confidence: 99%

AR2SPARQL: An Arabic Natural Language Interface for the Semantic Web

AlAgha¹,

Abu-Taha²

2015

IJCA

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“…This can be attributed to the lack of ontologies expressed in Arabic and the complexities associated with the NLP of Arabic text [3]. In general, there are four different categories of research concerning the Arabic language and the Semantic Web [11]: 1) the development of Arabic ontologies [12][13][14], 2) Using ontologies for improving Arabic named entities extraction [15,16], 3) Ontology based modelling of Islamic knowledge [17][18][19], and 4) supporting cross-language information retrieval [20,21]. In parallel with these efforts, little attention was paid to enable Arab users to query Arabic ontologies through NL interfaces.…”

Section: Related Workmentioning

confidence: 99%

Using Linguistic Analysis to Translate Arabic Natural Language Queries to SPARQL

AlAgha¹

2015

IJWesT

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The logic-based machine-understandable framework of the Semantic Web often challenges naive users when they try to query ontology-based knowledge bases. Existing research efforts have approached this problem by introducing Natural Language (NL) interfaces to ontologies. These NL interfaces have the ability to construct SPARQL queries based on NL user queries. However, most efforts were restricted to queries expressed in English, and they often benefited from the advancement of English NLP tools. However, little research has been done to support querying the Arabic content on the Semantic Web by using NL queries. This paper presents a domain-independent approach to translate Arabic NL queries to SPARQL by leveraging linguistic analysis. Based on a special consideration on Noun Phrases (NPs), our approach uses a language parser to extract NPs and the relations from Arabic parse trees and match them to the underlying ontology. It then utilizes knowledge in the ontology to group NPs into triple-based representations. A SPARQL query is finally generated by extracting targets and modifiers, and interpreting them into SPARQL. The interpretation of advanced semantic features including negation, conjunctive and disjunctive modifiers is also supported. The approach was evaluated by using two datasets consisting of OWL test data and queries, and the obtained results have confirmed its feasibility to translate Arabic NL queries to SPARQL.

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“…Each word in arabic has a certain pattern and morpheme parts which distinct words from each other. Arabic derivational and declension processes allow a wide range of verbs and nouns variation [2], Arabic linguistic resources are computerized as Arramooz lexicon [3], Almaany dictionary [4], and Azhary lexical ontology [5].…”

Section: Introductionmentioning

confidence: 99%

Support of Arabic Sign Language Machine Translation based on Morphological processing

Asjea¹,

Ismail²,

Khawatmi³

2019

IJCA

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This paper presents a morphological processing system as a part of arabic text to arabic sign language machine translation system. This morphological processing depends on Farasa analyzer tool, Stanford model and Arramooz lexicon. The characteristics of sign language are achieved to get intermediate arabic sign language sentences. Then these sentences are searched in a sign language dictionary word by word to display the related signs images if available, or to display letters of word using finger spelling alphabet images. The proposed system is tested on many non-vowelized arabic sentences, and good results and high accuracy are obtained.

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Azhary: An Arabic Lexical Ontology

Abstract: Abstract

Cited by 28 publications

References 11 publications

AR2SPARQL: An Arabic Natural Language Interface for the Semantic Web

AR2SPARQL: An Arabic Natural Language Interface for the Semantic Web

Using Linguistic Analysis to Translate Arabic Natural Language Queries to SPARQL

Support of Arabic Sign Language Machine Translation based on Morphological processing

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