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This special issue features the selected works of authors who have presented papers at the 2022 iteration of the Joint Conference on Digital Libraries (JCDL) in Cologne, Germany. The motto of the conference was "Bridging Worlds" and was run as a fully hybrid event. Ten papers covering all aspects of Digital Libraries, namely Natural Language Processing, Information Retrieval, User Behavior, Scholarly Communication, Classification, Information Extraction are included in this issue. [ABSTRACT FROM AUTHOR]

A rapid growth has occurred for the act of plagiarism with the aid of Internet explosive growth wherein a massive volume of information offered with effortless use and access makes plagiarism the process of taking someone else’s work (represented by ideas, or even words) and representing it as other's own work easy to be performed. For ensuring originality, detecting plagiarism has been massively necessitated in various areas so that the people who aim to plagiarize ought to offer considerable effort for introducing works centered on their research. In this paper, work has been proposed for improving the detection of textual plagiarism through proposing a model for candidate retrieval phase. The model proposed for retrieving candidates has adopted the vector space method VSM as a retrieval model and centered on representing documents as vectors consisting of average term weights and considering them as queries for retrieval instead of representing them as vectors of term weight. The detailed comparison task comes as the second phase wherein fuzzy semantic based string similarity has been applied. Experiments have been conducted using PAN-PC-10 as an evaluation dataset for evaluating the proposed system. As the problem statement in this paper is restricted to detect extrinsic plagiarism and works on English documents, experiments have been performed on the portion dedicated to extrinsic detection and on documents in English language only. For evaluating performance of the proposed model for retrieving candidates, Precision, Recall, and F-measure have been used as an evaluation metrics. The overall performance of the proposed system has been assessed through the use of the five standard PAN measures Precision, Recall, F-measure, Granularity and . The experimental results have clarified that the proposed model for retrieving candidates has a positive impact on the overall performance of the system and the system outperforms the other state-of-the-art methods. They clarified that the proposed model has detected about 80% of the plagiarism cases and about 90% of the detections were correct. The proposed model has the ability to detect literal plagiarism in addition to cases containing paraphrasing. Performance comparison has clarified that the proposed system is either comparable or outperforms the other baseline systems in terms of the five evaluation metrics. [ABSTRACT FROM AUTHOR]

Automatic themes-based classification of Quran verses is the process of classifying verses to predefined categorizes or themes. It is an essential task for all Muslims and people interested in studying the Quran. Quran themes-based classification could be used in many natural language processing (NLP) fields such as search engines, data mining, question–answering systems, and information retrieval applications. This paper presents an ensemble multi-label classification model that automatically identifies and classifies the Quran verses based on themes/topics. The model is composed of four phases: pre-processing, data vectorization, binary relevance classifier, and voting module. Firstly, the verses of the second chapter of the Quran (Al-Baqarah) are tokenized and normalized. Then, the topics of these verses are manually labeled based on "Mushaf Al-Tajweed" classification. Secondly, verses are converted into features' vectors using term frequency-inverse document frequency (TF-IDF) and word2vec techniques. Word2vec is used to consider the semantic meaning of Quranic words and to improve performance. Also, they are trained on a collected classic Arabic corpus of 200 million words. Then, the binary relevance multi-label classification technique is applied using three different classifiers: logistic regression, support vector machine, and random forest, which categorize verses into 393 topics/tags. Finally, the voting module is applied by picking the tags with the maximum prediction probability among the three classifiers. The results of the three classifiers and the ensemble model are compared against "Mushaf Al-Tajweed." The ensemble model outperforms the three classifiers. Its average hamming loss, recall, precision, and F1-Score are 0.224, 81%, 75%, and 77%, respectively. [ABSTRACT FROM AUTHOR]

Medical data in online groups and social media contain valuable information, which is provided by both healthcare professionals and patients. In fact, patients can talk freely and share their personal experiences. These resources are a valuable opportunity for health professionals who can access patients' opinions, as well as discussions between patients. Recently, the data processing of the health community and, how to extract knowledge is a significant technical challenge. There are many online group and forums that users can discuss on healthcare issues. Therefore, we can examine these text documents for discovering knowledge and evaluating patients' behavior based on their opinions and discussions. For example, there are many questions and answering groups on Twitter or Facebook. Given the importance of the research, in this paper, we present a semantic framework based on topic model (LDA) and Random forest(RF) to predict and retrieval latent topics of healthcare text-documents from an online forum. We extract our healthcare records (patient-questions) from patient.info website as a real dataset. Experiments on our dataset show that social media forums could help for detecting significant patient safety problems on healthcare issues. [ABSTRACT FROM AUTHOR]

Stemming is the basic operation in Natural language processing (NLP) to remove derivational and inflectional affixes without performing a morphological analysis. This practice is essential to extract the root or stem. In NLP domains, the stemmer is used to improve the process of information retrieval (IR), text classifications (TC), text mining (TM) and related applications. In particular, Urdu stemmers utilize only uni-gram words from the input text by ignoring bigrams, trigrams, and n-gram words. To improve the process and efficiency of stemming, bigrams and trigram words must be included. Despite this fact, there are a few developed methods for Urdu stemmers in the past studies. Therefore, in this paper, we proposed an improved Urdu stemmer, using hybrid approach divided into multi-step operation, to deal with unigram, bigram, and trigram features as well. To evaluate the proposed Urdu stemming method, we have used two corpora; word corpus and text corpus. Moreover, two different evaluation metrics have been applied to measure the performance of the proposed algorithm. The proposed algorithm achieved an accuracy of 92.97% and compression rate of 55%. These experimental results indicate that the proposed system can be used to increase the effectiveness and efficiency of the Urdu stemmer for better information retrieval and text mining applications. [ABSTRACT FROM AUTHOR]

Word Sense Disambiguation (WSD) is a fundamental task useful for Information Retrieval, Information Extraction, web search, and indexing, among others. In the literature there exist several works dedicated to generic WSD task, but in recent years domain-specific WSD has attracted the attention of several researchers. In this sense, this paper describes an approach for domain-specific WSD by selecting the predominant sense (synset from WordNet) of ambiguous words. To achieve it the method uses two corpora: the domain-specific test corpus (containing target ambiguous words) and a domain-specific auxiliary corpus (obtained by using relevant words from the domain-specific test corpus ). The approach has four main stages: (1) auxiliary corpus generation; (2) related features extraction (from the auxiliary corpus); (3) test features extraction (from the test corpus); and (4) features integration . The proposed approach has been tested on domain-specific corpora (Sports and Finance) and on one balanced corpus, BNC. Even though our WSD approach showed some limitations when dealing with the general-domain corpus, the obtained results for domain-specific corpora, which are our main interest, were better than those reported in previous works. [ABSTRACT FROM AUTHOR]

• Propose a hybrid Transformer approach for Chinese NER. • Take the latent words as the additional features to improve NER task. • Find an efficient strategy to fine-tune BERT with other modules. • Apply relative position encodings to improve Transformer. Named entity recognition (NER) plays an important role in many downstream tasks of natural language processing, such as knowledge extraction and information retrieval. NER of Chinese is more challenging than that of English due to lack of the explicit word boundary. Features augmentation is a potential way to improve NER model of Chinese. Pre-trained models can implicitly preserve prior knowledge with additional features. This paper proposes a hybrid Transformer approach, which first utilize the fused additional features embeddings (e.g. char embeddings, bigram embeddings, lattice embeddings and BERT embeddings) as distributed representations to augment the representation ability of model. In addition, a new training strategy named DF strategy is proposed to efficiently fine-tune Bidirectional Encoder Representations from Transformers (BERT) and other embeddings in balance. Then, the proposed model can perceive the relations of features by introducing relative position embeddings to an additional adapted Transformer encoder. Lastly, a standard Conditional Random Field is used to alleviate the obvious tag errors. The proposed model is applied to four representative Chinese datasets to investigate its performance. Experiments results show that the proposed model outperforms the other popular models in terms of accuracy. The proposed BL-BTC model can effectively improve the recognition performance of formal and informal texts. [ABSTRACT FROM AUTHOR]

The rapid proliferation of text data has lead to an increase in the use of Information Extraction (IE) techniques to automatically extract key information in a fast and effective manner. Relation Extraction (RE), a sub-task of IE focuses on extracting semantic relations from free natural language text and is crucial for further applications including Question Answering, Information Retrieval, Knowledge Base construction, Text Summarization, etc. Literature shows that supervised learning approaches were widely used in RE. However, the performance of supervised methodologies depend on the availability of domain-specific annotated datasets which is not viable for many of the domains including legal, financial, insurance etc. In recent times, Open Information Extraction (OIE) techniques address this issue, by facilitating domain-independent extraction of relations from large text corpora with no demand for domain-specific tagged data and predefined relation classes. Even though OIE systems are fast and simple to implement, they are less effective in handling complex sentences, and often produce redundant extractions. This paper proposes an efficient RE system to extract domain-specific relations from natural language text, consisting of Knowledge-based and Semi-supervised learning systems, integrated with domain ontology. We evaluated the performance of proposed work on 'judicial domain" as a use case and found that it overcomes the flaws and limitations of existing RE approaches, by achieving better results in terms of precision and recall. On further analysis, we found that the proposed system outperforms existing cutting-edge OIE systems on varying sentence length and complexity. • Automatic extraction of semantic relations from large volume of natural language text belonging to closed domain, particularly judicial domain. • Combination of knowledge based and semi-supervised learning systems to extract domain-specific relations from complex text that lacks domain-specific labelled data. • Solution to various challenges in handling complex sentences in passive voice and containing conjunctive forms. • Evaluation of state-of-the-art open information extraction approaches on judicial text with varying complexity and length. • First attempt to provide a solution for extracting domain-specific relations from judicial text. [ABSTRACT FROM AUTHOR]

Given the huge amount of heterogeneous data stored in different locations, it needs to be federated and semantically interconnected for further use. This paper introduces WINFRA, a comprehensive open-access platform for semantic web data and advanced analytics based on natural language processing (NLP) and data mining techniques (e.g., association rules, clustering, classification based on associations). The system is designed to facilitate federated data analysis, knowledge discovery, information retrieval, and new techniques to deal with semantic web and knowledge graph representation. The processing step integrates data from multiple sources virtually by creating virtual databases. Afterwards, the developed RDF Generator is built to generate RDF files for different data sources, together with SPARQL queries, to support semantic data search and knowledge graph representation. Furthermore, some application cases are provided to demonstrate how it facilitates advanced data analytics over semantic data and showcase our proposed approach toward semantic association rules. [ABSTRACT FROM AUTHOR]

Pattern matching is widely used in various fields such as information retrieval, natural language processing (NLP), data mining and network security. In Uyghur (a typical agglutinative, low-resource language with complex morphology, spoken by the ethnic Uyghur group in Xinjiang, China), research on pattern matching is also ongoing. Due to the language characteristics, the pattern matching using characters and words as basic units has insufficient performance. There are two problems for pattern matching: (1) vowel weakening and (2) morphological changes caused by suffixes. In view of the above problems, this paper proposes a Boyer–Moore-U (BM-U) algorithm and a retrievable syllable coding format based on the syllable features of the Uyghur language and the improvement of the Boyer–Moore (BM) algorithm. This algorithm uses syllable features to perform pattern matching, which effectively solves the problem of weakening vowels, and it can better match words with stem shape changes. Finally, in the pattern matching experiments based on character-encoded text and syllable-encoded text for vowel-weakened words, the BM-U algorithm precision, recall, F1-measure and accuracy are improved by 4%, 55%, 33%, 25% and 10%, 52%, 38%, 38% compared to the BM algorithm. [ABSTRACT FROM AUTHOR]