Mining valuable hidden knowledge from large-scale data relies on the support of reasoning technology. Knowledge graphs, as a new type of knowledge representation, have gained much attention in natural language processing. Knowledge graphs can effectively organize and represent knowledge so that it can be efficiently utilized in advanced applications. Recently, reasoning over knowledge graphs has become a hot research topic, since it can obtain new knowledge and conclusions from existing data. Herein we review the basic concept and definitions of knowledge reasoning and the methods for reasoning over knowledge graphs. Specifically, we dissect the reasoning methods into three categories: rule-based reasoning, distributed representation-based reasoning and neural network-based reasoning. We also review the related applications of knowledge graph reasoning, such as knowledge graph completion, question answering, and recommender systems. Finally, we discuss the remaining challenges and research opportunities for knowledge graph reasoning.

A review: Knowledge reasoning over knowledge graph

As more and more Arabic textual information becomes available through the Web in homes and businesses, via Internet and Intranet services, there is an urgent need for technologies and tools to process the relevant information. Named Entity Recognition NER is an Information Extraction task that has become an integral part of many other Natural Language Processing NLP tasks, such as Machine Translation and Information Retrieval. Arabic NER has begun to receive attention in recent years. The characteristics and peculiarities of Arabic, a member of the Semitic languages family, make dealing with NER a challenge. The performance of an Arabic NER component affects the overall performance of the NLP system in a positive manner. This article attempts to describe and detail the recent increase in interest and progress made in Arabic NER research. The importance of the NER task is demonstrated, the main characteristics of the Arabic language are highlighted, and the aspects of standardization in annotating named entities are illustrated. Moreover, the different Arabic linguistic resources are presented and the approaches used in Arabic NER field are explained. The features of common tools used in Arabic NER are described, and standard evaluation metrics are illustrated. In addition, a review of the state of the art of Arabic NER research is discussed. Finally, we present our conclusions. Throughout the presentation, illustrative examples are used for clarification.

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A survey of arabic named entity recognition and classification

This paper presents a wide literature review of natural language processing for dialectical Arabic. Four main research areas were identified and the dialect coverage in research work was outlined. The paper can be used as a quick reference to identify relevant contributions that address a specific NLP aspect for a specific dialect.

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Natural Language Processing for Dialectical Arabic: A Survey

This article summarizes the research on computational methods to detect academic plagiarism by systematically reviewing 239 research papers published between 2013 and 2018. To structure the presentation of the research contributions, we propose novel technically oriented typologies for plagiarism prevention and detection efforts, the forms of academic plagiarism, and computational plagiarism detection methods. We show that academic plagiarism detection is a highly active research field. Over the period we review, the field has seen major advances regarding the automated detection of strongly obfuscated and thus hard-to-identify forms of academic plagiarism. These improvements mainly originate from better semantic text analysis methods, the investigation of non-textual content features, and the application of machine learning. We identify a research gap in the lack of methodologically thorough performance evaluations of plagiarism detection systems. Concluding from our analysis, we see the integration of heterogeneous analysis methods for textual and non-textual content features using machine learning as the most promising area for future research contributions to improve the detection of academic plagiarism further.

https://dl.acm.org/doi/pdf/10.1145/3345317

Academic Plagiarism Detection: A Systematic Literature Review

This paper describes the first steps towards developing a methodology for testing and evaluating the performance of Machine Reading systems through Question Answering and Reading Comprehension Tests. This was the attempt of the QA4MRE challenge which was run as a Lab at CLEF 2011. This year a major innovation was introduced, as the traditional QA task was replaced by a new Machine Reading task whose intention was to ask questions which required a deep knowledge of individual short texts and in which systems were required to choose one answer, by analysing the corresponding test document in conjunction with the background collections provided by the organization. Beside the main task, also one pilot task was offered, namely, Processing Modality and Negation for Machine Reading. This task was aimed at evaluating whether systems were able to understand extra-propositional aspects of meaning like modality and negation. This paper describes the preparation of the data sets, the creation of the background collections to allow systems to acquire the required knowledge, the metric used for the evaluation of the systems' submissions, and the results of this first attempt. Twelve groups participated in the task submitting a total of 62 runs in three languages: English, German and Romanian.

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Overview of QA4MRE at CLEF 2011: Question Answering for Machine Reading Evaluation

Built on the basis of the methods developed for Princeton WordNet and EuroWordNet, Arabic WordNet (AWN) has been an interesting project which combines WordNet structure compliance with Arabic particularities. In this paper, some AWN shortcomings related to coverage and usability are addressed. The use of AWN in question/answering (Q/A) helped us to deeply evaluate the resource from an experience-based perspective. Accordingly, an enrichment of AWN was built by semi-automatically extending its content. Indeed, existing approaches and/or resources developed for other languages were adapted and used for AWN. The experiments conducted in Arabic Q/A have shown an improvement of both AWN coverage as well as usability. Concerning coverage, a great amount of named entities extracted from YAGO were connected with corresponding AWN synsets. Also, a significant number of new verbs and nouns (including Broken Plural forms) were added. In terms of usability, thanks to the use of AWN, the performance for the AWN-based Q/A application registered an overall improvement with respect to the following three measures: accuracy (+9.27 % improvement), mean reciprocal rank (+3.6 improvement) and number of answered questions (+12.79 % improvement).

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On the evaluation and improvement of Arabic WordNet coverage and usability

Arabic is one of the languages which are less concerned by researchers in the field of Question Answering. The paper presents core modules of a new Arabic Question Answering system called IDRAAQ. These modules aim at enhancing the quality of retrieved passages with respect to a given question. Experiments have been conducted in the framework of the main task of QA4MRE@CLEF 2012 that includes this year the Arabic language. Two runs were submitted. Both runs only use reading test documents to answer questions. The difference between the two runs exists in the answer validation process which is more relaxed in the second run. The Passage Retrieval (PR) module of our system presents multi-levels of processing in order to improve the quality of returned passage and thereafter the performances of the whole system. The PR module of IDRAAQ is based on keyword-based and structure-based levels that respectively consist in: (i) a Query Expansion (QE) process relying on Arabic WordNet semantic relations; (ii) a Distance Density N-gram Model based passage retrieval system. The latter level uses passages retrieved on the basis of QE queries and re-ranks them according to a structure-based similarity score. Named Entities are recognized by means of a mapping between the YAGO ontology and Arabic WordNet. The experiments that we conducted show that with respect to the accuracy and c@1 measure, IDRAAQ registered encouraging performances in particular with factoid questions. The same experiments allowed us to identify the lacks of the system especially when processing non factoid questions and at the Answer Validation stage. The IDRAAQ system, which is still under construction, will integrate a Conceptual Graph-based passage re-ranking introducing a semantic level to its PR module.

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IDRAAQ: New Arabic Question Answering system based on Query Expansion and Passage Retrieval

This paper describes an approach for improving the re- ranking of passages for Arabic Question/Answering (Q/A). This approach implements a process performing a semantic Query Expansion (QE) based on the Arabic WordNet (AWN) ontology with a structure-based Passage Retrieval (PR) based on the Distance Density n-gram model. Experiments with a set of translated CLEF and TREC questions have shown that the accuracy, the Mean Reciprocal Rank and the number of answered questions have been significantly improved using our approach. These experiments have been conducted in an open domain by querying the web as document collection. An analysis of the obtained performances is discussed in this paper.

http://www.ieee.ma/IJICT/IJICT-SI-Bouzoubaa-3.3/5%20-%20Abouenour.pdf

An evaluated semantic query expansion and structure-based approach for enhancing Arabic question/answering

With the expansion of the content available on the web, Question/Answering (Q/A) systems have become, among other searching tools, a focus of researchers and users as well. For the Arabic language very few works have been done in this field. In this paper, we focus on the improvement of Q/A through a Query Expansion (QE) process. Our approach is based on the ontology that we have built using Arabic WordNet. Indeed, we designed a QE process from the semantic relations existing among the concepts of our ontology. The preliminary experiments that we have conducted show that the accuracy of getting the answer expected was improved by our QE approach.

http://www.dsic.upv.es/~prosso/resources/AbouenourEtAl_ACIT08.pdf

Improving q/a using arabic wordnet

is the first shared task that addresses the evaluation of plagiarism detection methods for Arabic texts. It has two sub- tasks, namely external plagiarism detection and intrinsic plagiarism detection. A total of 8 runs have been submitted and tested on the standardized corpora developed for the track. This overview paper describes these evaluation corpora, discusses the participants' methods, and highlights their building blocks that could be language dependent.

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Lahsen Abouenour

Papers

On the evaluation and improvement of Arabic WordNet coverage and usability

IDRAAQ: New Arabic Question Answering system based on Query Expansion and Passage Retrieval

An evaluated semantic query expansion and structure-based approach for enhancing Arabic question/answering

Improving q/a using arabic wordnet

Overview of the AraPlagDet PAN@FIRE2015 shared task on Arabic plagiarism detection