Abstract
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%.
Highlights
The web has become the most important resource for digital knowledge
QUESTION ANSWERING SYSTEM FOR ARABIC LANGUAGE we review the research based on different techniques used for Arabic question answering systems
To extract the predicates/properties out of the input question and link them to the DBpedia corresponding predicates/properties, our research proposes three novel techniques as follows: 1) BASELINE APPROACH In this approach, the RDF triple is built based on the expected Range and Domain of the property
Summary
The web has become the most important resource for digital knowledge. There is a trend to restructuring the web into representing data rather than representing documents. A huge amount of semantic data is available on the web using semantic web meta-data (i.e. in Resource Description Framework (RDF) and Web Ontology Language (OWL) formats). The Semantic Web as the new vision of the web 3.0, builds on the idea of enriching the web-links with meaningful properties describing linked documents’ relationships [1]. Semantic Web is based on structured data and well-defined relationships representing. Meanings of data.The main goal of the semantic web is to help machines understand what is represented. In the case of Question Answering Systems (QASs), structured data needs to be explored to return precise and short answers
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