Search

Scientific retrieval systems need to be given domain search terms for searching publications, however, as natural language, search terms provided by users are often fuzzy and limited and some relevant terms are always overlooked in searching. Meanwhile, users always desire to be given domain related keywords to enlighten themselves what other terms can be used for their searching. This paper presents a concepts recommendation model in scientific paper retrieval, in which concepts are extracted from keyword in scientific papers, and some data mining algorithms are used to calculate the similarity between search terms and concepts and do recommendation for users. This model is simple and can be used with small dataset, in which all training data used is from meta data of papers that is easy to acquired. Experimental result hold good precision, which shows that this research not only simplifies searching step and improves the searching quality for users, but also lays the foundation for semantic search. [ABSTRACT FROM AUTHOR]

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]

The number of published findings in biomedicine increases continually. At the same time, specifics of the domain's terminology complicates the task of relevant publications retrieval. In the current research, we investigate influence of terms' variability and ambiguity on a paper's likelihood of being retrieved. We obtained statistics that demonstrate significance of the issue and its challenges, followed by presenting the sci.AI platform, which allows precise terms labeling as a resolution. [ABSTRACT FROM AUTHOR]

Purpose: The expansion of research across various disciplines has led to a substantial increase in published papers and journals, highlighting the necessity for reliable text mining platforms for database construction and knowledge acquisition. This abstract introduces GPDMiner(Gene, Protein, and Disease Miner), a platform designed for the biomedical domain, addressing the challenges posed by the growing volume of academic papers. Methods: GPDMiner is a text mining platform that utilizes advanced information retrieval techniques. It operates by searching PubMed for specific queries, extracting and analyzing information relevant to the biomedical field. This system is designed to discern and illustrate relationships between biomedical entities obtained from automated information extraction. Results: The implementation of GPDMiner demonstrates its efficacy in navigating the extensive corpus of biomedical literature. It efficiently retrieves, extracts, and analyzes information, highlighting significant connections between genes, proteins, and diseases. The platform also allows users to save their analytical outcomes in various formats, including Excel and images. Conclusion: GPDMiner offers a notable additional functionality among the array of text mining tools available for the biomedical field. This tool presents an effective solution for researchers to navigate and extract relevant information from the vast unstructured texts found in biomedical literature, thereby providing distinctive capabilities that set it apart from existing methodologies. Its application is expected to greatly benefit researchers in this domain, enhancing their capacity for knowledge discovery and data management. [ABSTRACT FROM AUTHOR]

Modern information retrieval systems use keywords within documents as indexing terms for search of relevant documents. As Chinese is an ideographic character-based language, the words in the texts are not delimited by white spaces. Indexing of Chinese documents is impossible without a proper segmentation algorithm. Many Chinese segmentation algorithms have been proposed in the past. Traditional segmentation algorithms cannot operate without a large dictionary or a large corpus of training data. Nowadays, the Web has become the largest corpus that is ideal for Chinese segmentation. Although most search engines have problems in segmenting texts into proper words, they maintain huge databases of documents and frequencies of character sequences in the documents. Their databases are important potential resources for segmentation. In this paper, we propose a segmentation algorithm by mining Web data with the help of search engines. On the other hand, the Romanized pinyin of Chinese language indicates boundaries of words in the text. Our algorithm is the first to utilize the Romanized pinyin to segmentation. It is the first unified segmentation algorithm for the Chinese language from different geographical areas, and it is also domain independent because of the nature of the Web. Experiments have been conducted on the datasets of a recent Chinese segmentation competition. The results show that our algorithm outperforms the traditional algorithms in terms of precision and recall. Moreover, our algorithm can effectively deal with the problems of segmentation ambiguity, new word (unknown word) detection, and stop words. [ABSTRACT FROM AUTHOR]

Traditional Chinese medicine (TCM) is a summary of the diagnosis and treatment experience formed by the working people in the long-term struggle against diseases, so it is very important to protect the intangible cultural heritage of TCM. How to extract valuable knowledge accurately and conveniently from the massive medical records of TCM is one of the important issues in the current research on the development of TCM. Due to the large amount of data of TCM medical records, many feature attributes, and diverse patterns, the existing classification technology has high computational complexity, low mining efficiency, and poor universality. Therefore, this paper proposed to quantify the medical records of TCM and obtained the main symptoms according to the improved hierarchical clustering feature selection algorithm. This paper also proposed a support vector machine (SVM) classification method using improved particle swarm algorithm to classify TCM information, which not only improves the efficiency and accuracy of TCM information classification but also discovers the potential dialectical and symptom patterns in diagnosis and treatment, so that the intangible cultural heritage protection of TCM can be developed sustainably. This paper showed that the information acquisition accuracy of the improved algorithm was very high. Before the improved algorithm was used, the accuracy of information mining for TCM was 67.90% at the highest and 65.53% at the lowest, but after using the improved algorithm, the accuracy rate of information mining for TCM was 88.02% at the highest and 82.45% at the lowest. It can be seen that using the improved algorithm to mine TCM information can quickly process effective information. [ABSTRACT FROM AUTHOR]

The retrieval of important information from a dataset requires applying a special data mining technique known as data clustering (DC). DC classifies similar objects into a groups of similar characteristics. Clustering involves grouping the data around k-cluster centres that typically are selected randomly. Recently, the issues behind DC have called for a search for an alternative solution. Recently, a nature-based optimization algorithm named Black Hole Algorithm (BHA) was developed to address the several well-known optimization problems. The BHA is a metaheuristic (population-based) that mimics the event around the natural phenomena of black holes, whereby an individual star represents the potential solutions revolving around the solution space. The original BHA algorithm showed better performance compared to other algorithms when applied to a benchmark dataset, despite its poor exploration capability. Hence, this paper presents a multi-population version of BHA as a generalization of the BHA called MBHA wherein the performance of the algorithm is not dependent on the best-found solution but a set of generated best solutions. The method formulated was subjected to testing using a set of nine widespread and popular benchmark test functions. The ensuing experimental outcomes indicated the highly precise results generated by the method compared to BHA and comparable algorithms in the study, as well as excellent robustness. Furthermore, the proposed MBHA achieved a high rate of convergence on six real datasets (collected from the UCL machine learning lab), making it suitable for DC problems. Lastly, the evaluations conclusively indicated the appropriateness of the proposed algorithm to resolve DC issues. [ABSTRACT FROM AUTHOR]

On account of prevalence of mobile devices, a variety of mobile-based services have been conducted recently. In this situation, it is possible to serve the information that fits to users' requests according to the users' context using mobile devices. Therefore, it is necessary to research data mining techniques with mobile devices. Therefore, we researched information retrieval system in mobile environment. We applied semantic data mining method with context information ontology. These techniques of semantic information retrieval can provide the information according to users' context information such as location, weather, user preference etc. exactly and rapidly anytime and anyplace. In modern society, it is essential to develop mobile information retrieval system which is efficient to users. Also, the system can provide users' current location and the location information with Google map APIs in mobile devices. Therefore, in this paper, we researched semantic data mining technique with context information ontology using mobile techniques. And we compared and evaluated our proposed model and the existing model in three areas and showed our proposed model has higher performance. [ABSTRACT FROM AUTHOR]

Since the introduction of the concept of entrepreneurial university in 1980s, the number of studies has dramatically increased, in particular since 2015. This had made the literature on the entrepreneurial university complex, fragmented and difficult to navigate. This paper provides a comprehensive review of all topics covered in the body of literature on the entrepreneurial university and identifies the most salient topics and papers within this literature, making use of text-mining techniques. Our paper employs topic modelling that reveals the underlying semantic structure of texts to identify the different underlying. Our study systematically analyses 1,110 papers over the period 1983–2020 using the Latent Dirichlet Allocation algorithm. Our analysis shows that the entrepreneurial university is fragmented around different topics that are very diverse. We find a total of 20 differentiated topics. Our study suggests that topics related to the overarching theme of academic entrepreneurship, in particular to commercialisation of research and the triple helix model are very popular within the entrepreneurial university literature. Finally, our analysis reveals that case-study type of research is losing momentum, giving path to nascent topics of research in the areas of entrepreneurial capability and university-industry alliances, which are becoming very popular within the entrepreneurial university literature. [ABSTRACT FROM AUTHOR]

This research presents an exploratory and descriptive study on the use of knowledge organization processes and systems in the context of repositories, published in journals indexed in databases between 2015 and 2020. The authors of these papers do not necessarily publish in specific events and journals in the Knowledge Organization area, but rather in the Library and Information Science arena. The study has been carried out in four steps: 1. Search, retrieval and selection of articles; 2. Development of a data codebook; 3. Identification and codification of topics; and, 4. Analysis of the data extracted. A final sample of 33 articles was defined. The methodology applied to determine the theme of journal articles is presented in detail, including the use of the Classification System for Knowledge Organization Literature (CSKOL). The illustrative data of disciplinarity and interdisciplinarity present in the articles object of this study are shown and discussed, regarding the predominance of certain ranges of CSKOL and regarding the diversity of the representative themes of the contents. It is possible to conclude that the use of CSKOL proves to be a suitable lens for analysing and understanding the literature on the field of knowledge organization in institutional repositories. It is shown that, in these 33 articles the themes of knowledge organization are combined with interdisciplinary themes from other areas of knowledge. In our opinion, this enriches and improves theoretical support for research development. [ABSTRACT FROM AUTHOR]

Geological named entity recognition (NER) is an essential prerequisite to realizing geological information extraction and information retrieval and is an actual means for accomplishing structured reconstruction of unstructured geological data. Existing geological NER methods mainly focus on coarse-grained geological entity recognition, but geological entities are fine-grained. To solve this problem, a Chinese fine-grained geological entity corpus encompassing 21 types of fine-grained labels is constructed. In addition, in this article, a fine-grained geological entity recognition model based on Bidirectional Encoder Representations from Transformer (BERT)-Flat-Lattice Transformer is designed. This paper names this method FGNER (Fine-grained Geological Named Entity Recognition) which adds geological naming rules to revise the model results to improve the recognition of complex geological entities. The fine-grained geological entity recognition method is evaluated using regional geological literature reports as experimental data. The experimental results show that the precision, recall, and F1-score of the FGNER model are 95.73%, 89.26%, and 92.05%, respectively, thus achieving better performance than baseline models, such as BERT-Conditional Random Field. [ABSTRACT FROM AUTHOR]

Forward modeling of wave scattering and radar imaging mechanisms is the key to information extraction from synthetic aperture radar (SAR) images. Like inverse graphics in the optical domain, an inherently-integrated forward-inverse approach would be promising for SAR advanced information retrieval and target reconstruction. This paper presents such an attempt at inverse graphics for SAR imagery. A differentiable SAR renderer (DSR) is developed, which reformulates the mapping and projection algorithm of the SAR imaging mechanism in the differentiable form of probability maps. First-order gradients of the proposed DSR are then analytically derived, which can be back-propagated from rendered image/silhouette to the target geometry and scattering attributes. A 3D inverse target reconstruction algorithm from SAR images is devised. Several simulation and reconstruction experiments are conducted, including targets with and without background, using synthesized data or real measured inverse SAR (ISAR) data by ground radar. Results demonstrate the efficacy of the proposed DSR and its inverse approach. [ABSTRACT FROM AUTHOR]

Data mining has become a relatively modern platform for information retrieval. The efficient data mining techniques can increase the reliability and accuracy of internal auditing for the various community even while lowering audit risk. Existing audit data mining approaches lack significant identification of hidden connections and interactions in bid data platforms. Hence, this study extends the literature survey on the signification of audit data mining in multiple applications. This survey identifies the scope of improved association algorithms in audit data mining, a rule-based machine learning approach to determine the exciting relationship among variables in large audit datasets. Therefore, a Conceptual Framework of Improved Association Algorithm (CFiAA) and its application in audit data mining is proposed. This study examines the strengths and weaknesses of the proposed CFiAA in audit mining. The proposed model has been trained using an audit data set and validates with various audit datasets. Finally, this paper presents the comparative analysis of the proposal to show its highest performance related to existing models. Thus, CFiAA scores the performance ratio of 94.5%, accuracy ratio of 92.4%, an efficiency ratio of 92.5%, F1 measure of 91.8%, error rate 32.5%, prediction ratio of 93.7%, and the precision ratio of 92.5% compared to existing models. [ABSTRACT FROM AUTHOR]

These days web-based business climate assumes a significant part to trade product information between shoppers regularly with others. Precisely foreseeing client buy designs in the web-based business marketplace is the basic place to use the information mining. To accomplish high benefit in internet business, the connection among client and product are vital. Additionally, numerous web-based business sites increment quickly and immediately and contest has become quite recently a just a click away. For that reason, the significance of remaining in the business, and further developing the benefit needs to precisely anticipate buy conduct and focus on their clients with customized administrations as per their inclinations. In this paper, an information mining model has been planned to improve the exactness of foreseeing and to observe affiliation rules for incessant thing sets. Various algorithms have been used to calculate the customer behaviour like Tree Classifier Boost Classifier, gradient Boosting Classifier, Random Forest Classifier. Planned model has been verified on three city Yangon, Mandalay, Naypyitaw Mall's sales dataset and the results shows that data uses is 94.0%. [ABSTRACT FROM AUTHOR]

Information retrieval is intended to help people who are constantly looking for information. Partitioning the video into frames is the first stage in video information retrieval. Most video frames are brief and do not provide much information about the image content. On the other hand, scene border recognition or video fragmentation into scenes provides a better understanding of the video scene by clustering images based on similar image content. This paper is about video scene identification, specifically video formation mining for template matching with deep characteristics. The study proposed and created a workflow that included phases for frame extraction, finding similarities between consecutive frames, grouping frames, identifying key frames, and seeing detection by merging the relevant frames. Python's OpenCV generates the frames. The process is evaluated using scene identification metrics. The results show that scene detection and quality are significant, as measured by several criteria. In addition, we examined and studied current recognition and analysis criteria. Furthermore, our proposed methodologies have been thoroughly tested on various public scene video datasets, and they outperform some state-of-the-art approaches. This work's findings can be used to create real-time conceptual video interpretations. [ABSTRACT FROM AUTHOR]

Clustering is a traditional data mining problem that has attracted researchers from different disciplines because its solution can be applied to many useful problems in our daily life. Since the era of big data is coming, how to "reduce the computing time" of an "effective clustering algorithm" has been a promising research issue in recent years. Thus, this paper presents an effective clustering algorithm, by using the so-called searched information to determine later search directions, and then has it implemented on Spark to accelerate its response time for analyzing large-scale datasets. Simulation results show that the proposed algorithm provides a better result than the other clustering algorithms compared in this paper because it is less sensitive to the initial solutions. The simulation results further show that cloud computing platform is capable of enhancing the performance of the proposed algorithm. • This study presents a high-performance clustering algorithm based on searched experiences. • The results show that it is able to find a better result than traditional clustering algorithms. • We apply it to Spark to show its possibility to accelerate the response time on a cloud system. [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]

BACKGROUND: Blood chemicals are routinely measured in clinical or preclinical research studies to diagnose diseases, assess risks in epidemiological research, or use metabolomic phenotyping in response to treatments. A vast volume of blood-related literature is available via the PubMed database for data mining. OBJECTIVES: We aimed to generate a comprehensive blood exposome database of endogenous and exogenous chemicals associated with the mammalian circulating system through text mining and database fusion. METHODS: Using NCBI resources, we retrieved PubMed abstracts, PubChem chemical synonyms, and PMC supplementary tables. We then employed text mining and PubChem crowdsourcing to associate phrases relating to blood with PubChem chemicals. False positives were removed by a phrase pattern and a compound exclusion list. RESULTS: A query to identify blood-related publications in the PubMed database yielded 1.1 million papers. Matching a total of 15 million synonyms from 6.5 million relevant PubChem chemicals against all blood-related publications yielded 37,514 chemicals and 851,999 publications records. Mapping PubChem compound identifiers to the PubMed database yielded 49,940 unique chemicals linked to 676,643 papers. Analysis of open-access metabolomics papers related to blood phrases in the PMC database yielded 4,039 unique compounds and 204 papers. Consolidating these three approaches summed up to a total of 41,474 achiral structures that were linked to 65,957 PubChem CIDs and to over 878,966 PubMed articles. We mapped these compounds to 50 databases such as those covering metabolites and pathways, governmental and toxicological databases, pharmacology resources, and bioassay repositories. In comparison, HMDB, the Human Metabolome Database, links 1,075 compounds to blood-related primary publications. CONCLUSION: This new Blood Exposome Database can be used for prioritizing chemicals for systematic reviews, developing target assays in exposome research, identifying compounds in untargeted mass spectrometry, and biological interpretation in metabolomics data. [ABSTRACT FROM AUTHOR]

Emerging medical imaging applications in healthcare, the number and volume of medical images is growing dramatically. Information needs of users in such circumstances, either for clinical or research activities, make the role of powerful medical image search engines more significant. In this paper, a text-based multi-dimensional medical image indexing technique is proposed in which correlation of the features-usages (according to the user's queries) is considered to provide an off-the content indexing while taking users' interestingness into account. Assuming that each medical image has some extracted features (e.g., based on the DICOM standard), correlations of the features are discovered by performing data mining techniques (i.e., quantitative association pattern discovery), on the history of users' queries as a data set. Then, based on the pairwise correlation of the features of medical images (a.k.a. Affinity), set of the all features is fragmented into subsets (using method like the vertical fragmentation of the tables in distribution of relational DBs). After that, each of these subsets of the features turn into a hierarchy of the features (by applying a hierarchical clustering algorithm on that subset), subsequently all of these distinct hierarchies together make a multi-dimensional structure of the features of medical images, which is in fact the proposed text-based (feature-based) multi-dimensional index structure. Constructing and using such text-based multi-dimensional index structure via its specific required operations, medical image retrieval process would be improved in the underlying medical image search engine. Generally, an indexing technique is to provide a logical representation of documents in order to optimize the retrieval process. The proposed indexing technique is designed such that can improve retrieval of medical images in a medical image search engine in terms of its effectiveness and efficiency. Considering correlation of the features of the image would semantically improve precision (effectiveness) of the retrieval process, while traversing them through the hierarchy in one dimension would try to optimize (i.e., minimize) the resources to have a better efficiency. The proposed text-based multi-dimensional indexing technique is implemented using the open source search engine Lucene, and compared with the built-in indexing technique available in the Lucene search engine, and also with the Terrier platform (available for the benchmarking of information retrieval systems) and other the most related indexing techniques. Evaluation results of memory usage and time complexity analysis, beside the experimental evaluations efficiency and effectiveness measures show that the proposed multi-dimensional indexing technique significantly improves both efficiency and effectiveness for a medical image search engine. [ABSTRACT FROM AUTHOR]

The study of dealing with searching information in documents within web pages is information retrieval (IR). The user needs to describe information with comments or reviews that consists of a number of words. Discovering weight of an inquiry term is helpful to decide the significance of a question. Estimation of term significance is a basic piece of most information retrieval approaches and it is commonly chosen through term frequency-inverse document frequency (TF-IDF). Also, improved TF-IDF method used to retrieve information in web documents. This paper presents comparison of TF-IDF method and improved TF-IDF method for information retrieval. Cosine similarity method calculated on both methods. The results of cosine similarity method on both methods compared on the desired threshold value. The relevance documents of TF-IDF method are more extracted than improved TF-IDF method. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to explore the recent trends of data mining method adoption in the library and information science (LIS) discipline. Bibliographic records from the data mining and LIS fields were collected respectively from the Scopus database. A dictionary of data mining method terms was constructed based on a rule-based textual analysis. Using the dictionary, this study investigated a range of prevalent data mining methods utilized in recent LIS studies. The findings of this study reveal different areas of data mining methods employed in LIS, such as big data, machine learning, text mining, information retrieval, and dimension reduction. The study also confirms the recent popularity of machine learning techniques in LIS research. [ABSTRACT FROM AUTHOR]

The application of data mining has been increasing day to day whereas the data base is also enhancing simultaneously. Hence retrieving required content from a huge data base is a critical task. This paper focus on biomedical engineering field, it concentrates on initial stage of database such as data preprocessing and cleansing to deal with noise and missing data in large biomedical data sets. The database of biomedical is huge and enhancing nature retrieving of specific content will be a critical task. Suggesting prescription with respect to identified disease based on profile analysis of specific patient is not available in current system. This paper proposes a recommendation system of prescription based on disease identification is done by combining user and professional suggestion with profile based analysis. Hence this focuses on profile based suggestions and report will be generated. The retrieving of specific suggestion from a huge database is done by hybrid feature selection algorithm. This approach focuses on enabling recommendation based on user profile and implementing Hybrid feature selection algorithm to retrieve specific content from a huge database. Hence it attains better retrieval of required content from a huge database compared to other existing approaches and suggests better recommendation with respect to user profile. [ABSTRACT FROM AUTHOR]

Interval scales are assumed by several basic descriptive statistics, such as mean and variance, and by many statistical significance tests which are daily used in IR to compare systems. Unfortunately, so far, there has not been any systematic and formal study to discover the actual scale properties of IR measures. Therefore, in this paper, we develop a theory of Information Retrieval (IR) evaluation measures, based on the representational theory of measurements, to determine whether and when IR measures are interval scales. We found that common set-based retrieval measures—namely Precision, Recall, and F-measure—always are interval scales in the case of binary relevance while this happens also in the case of multi-graded relevance only when the relevance degrees themselves are on a ratio scale and we define a specific partial order among systems. In the case of rank-based retrieval measures—namely AP, gRBP, DCG, and ERR—only gRPB is an interval scale when we choose a specific value of the parameter $p$ and define a specific total order among systems while all the other IR measures are not interval scales. Besides the formal framework itself and the proof of the scale properties of several commonly used IR measures, the paper also defines some brand new set-based and rank-based IR evaluation measures which ensure to be interval scales. [ABSTRACT FROM AUTHOR]

• A unified approach to Boolean matrix factorization is introduced in this paper • The approach allows combining rank-1 binary terms with arbitrary Boolean functions • The proposed method relies on a polynomial representation of the combining function • Two factorization algorithms are proposed based on on this representation • Comparisons with state-of-the art methods and tests on real data are presented In this paper, we propose a generalized framework for fitting Boolean matrix factorization models to binary data. In this generalized setting, the binary rank-1 components of the underlying model can be combined by any Boolean function, thus extending the standard Boolean matrix factorization model, where the combination is restricted to the logical 'OR' function. We introduce two algorithms relying on a relaxation of the binary constraints on the factors of the model and on a polynomial representation of the Boolean function that combines the rank-1 components. One of the algorithms is based on the gradient descent optimization method, while the other is based on block coordinate descent. A detailed presentation of the algorithms is given, along with numerical experiments both on simulated and real datasets. A comparison with other algorithms from the literature is presented in the standard Boolean matrix setting allowing to assess the advantages and shortcomes of the proposed methods in terms of factor retrieval and data denoising performance, convergence behavior and time complexity. [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]

With the convergence of various broadcasting systems, the amount of content available in mobile terminals including IPTV has significantly increased. In this paper, we propose a system that enables users to schedule programs considering both section video clips and full video clips based on the user detection method with similar preference. And, since the system constituting the contents can be classified according to the program, the proposed method can store a program desired by the user, and thus create and schedule a kind of individual channel. Experimental results show that the proposed method has a higher prediction accuracy; this is accomplished by comparing existing channel recommendation methods with the program recommendation methods proposed in this paper. [ABSTRACT FROM AUTHOR]

Query expansion is a method for improving retrieval performance by supplementing an original query with additional terms. This process improves the quality of search engine results and helps users to find the required information. In the recent years, different methods have been proposed in this area. In addition to such a variety of different approaches in this area and necessity of the study of their characteristics, the lack of a comprehensive classification based on candidate expansion terms extraction methods and also suitable and complete criteria to evaluate them, make the precise study, comparison and evaluation of methods for query expansion and choosing appropriate method based on need difficult for researchers. Therefore, in this paper a new useful framework is presented. In the proposed framework, in addition to the identification of three basic approaches based on the candidate expansion terms extraction methods for query expansion and expressing their properties, appropriate criteria for qualitative evaluation of these methods will be described. Next, the proposed approaches will be evaluated qualitatively based on these criteria. Using the systematic and structured framework proposed in this paper leads a useful platform for researchers to be provided for the comparative study of existing methods in the field, investigating their features specially their drawbacks to improve them and choosing appropriate method based on their needs. [ABSTRACT FROM AUTHOR]

Facing with thousands of online product reviews, consumers usually pay close attention to those valuable ones which provide more specific and credible evaluations on products. Whether a close association exists between product review quality and sales is thus examined in this paper. By employing text mining techniques on multiple review features, a review is measured as one of the following two levels: high-quality or low-quality. In doing so, aggregate quality level of product’s whole reviews is also identified. Then, a two-level econometrical analysis is conducted on the real datasets from Amazon.cn. The results reveal that aggregate quality level of positive reviews and negative reviews interactively influence sales. In the situation the aggregate quality level of positive reviews is high meanwhile that of negative reviews’ is low, product sale is the highest, while in the opposite situation product sale is the lowest. The results also reveal that consumers understand product’s value from weighting positive and negative reviews of high-quality level, which then positively relates to product sales and exerts a dynamic effect on sales by the moderating role of product selling stage and popularity. The paper innovatively integrates the quantitative and qualitative characteristics of reviews to estimate their economic effect. [ABSTRACT FROM AUTHOR]

Link similarity is widely applied in measuring the similarity between such objects as Web pages, scientific papers, and social networks. However, there are some deficiencies in the existing methods to measure it. For example, they cannot handle some semantic-similar contents. Their computation may not lead to accurate results in some cases. This paper presents a novel method to do so. It introduces the semantic similarity to calculate the similarity between two given objects, and overcomes the drawback caused by the fact that the existing methods ignore the semantic information of objects. It also gives a novel computation function to make the computing result of similarity more accurate. [ABSTRACT FROM PUBLISHER]

Many famous online social networks, e.g., Facebook and Twitter, have achieved great success in the last several years. Users in these online social networks can establish various connections via both social links and shared attribute information. Discovering groups of users who are strongly connected internally is defined as the community detection problem. Community detection problem is very important for online social networks and has extensive applications in various social services. Meanwhile, besides these popular social networks, a large number of new social networks offering specific services also spring up in recent years. Community detection can be even more important for new networks as high quality community detection results enable new networks to provide better services, which can help attract more users effectively. In this paper, we will study the community detection problem for new networks, which is formally defined as the 'New Network Community Detection' problem. New network community detection problem is very challenging to solve for the reason that information in new networks can be too sparse to calculate effective similarity scores among users, which is crucial in community detection. However, we notice that, nowadays, users usually join multiple social networks simultaneously and those who are involved in a new network may have been using other well-developed social networks for a long time. With full considerations of network difference issues, we propose to propagate useful information from other well-established networks to the new network with efficient information propagation models to overcome the shortage of information problem. An effective and efficient method, Cat (Cold stArT community detector), is proposed in this paper to detect communities for new networks using information from multiple heterogeneous social networks simultaneously. Extensive experiments conducted on real-world heterogeneous online social networks demonstrate that Cat can address the new network community detection problem effectively. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma is one of the most general malignant neoplasms in adults with high mortality. Mining relative medical knowledge from rapidly growing text data and integrating it with other existing biomedical resources will provide support to the research on the hepatocellular carcinoma. To this purpose, we constructed a knowledge graph for Hepatocellular Carcinoma (KGHC).Methods: We propose an approach to build a knowledge graph for hepatocellular carcinoma. Specifically, we first extracted knowledge from structured data and unstructured data. Since the extracted entities may contain some noise, we applied a biomedical information extraction system, named BioIE, to filter the data in KGHC. Then we introduced a fusion method which is used to fuse the extracted data. Finally, we stored the data into the Neo4j which can help researchers analyze the network of hepatocellular carcinoma.Results: KGHC contains 13,296 triples and provides the knowledge of hepatocellular carcinoma for healthcare professionals, making them free of digging into a large amount of biomedical literatures. This could hopefully improve the efficiency of researches on the hepatocellular carcinoma. KGHC is accessible free for academic research purpose at http://202.118.75.18:18895/browser/ .Conclusions: In this paper, we present a knowledge graph associated with hepatocellular carcinoma, which is constructed with vast amounts of structured and unstructured data. The evaluation results show that the data in KGHC is of high quality. [ABSTRACT FROM AUTHOR]

This paper examines the challenging problem of new user cold starts in subset labelled and extremely sparsely labelled big data. We introduce a new Isle of Wight Supply Chain (IWSC) dataset demonstrating these characteristics. We also introduce a new technique addressing these challenges, the Transitive Semantic Relationships (TSR) model, which infers potential relationships from user and item text content and few labelled examples. We perform both implicit and explicit evaluation of TSR as a recommender system and from new user cold starts we achieve a hit-rate@10 of 77% on a collection of 630 items with only 376 supply-chain consumer labels, and 67% with only 142 supply-chain supplier labels, demonstrating a high level of performance even with extremely few labels in challenging cold-start scenarios. TSR is suitable for any dataset featuring few labels and user and item content, where similarity of content indicates similar relationship forming capability. TSR can be used as a standalone recommender system or to complement existing high-performance recommender models that require more labels or do not support cold starts. [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]

Web 2.0 or social web applications such as online discussion forums, blogs and Wikipedia have improved knowledge sharing by providing an environment in which users can generate and find their favorite content in, a flexible way. With the passage of time, online discussion forums accumulate a huge amount of content and this can introduce issues of content quality and user credibility. A poor-quality answer in a discussion forum indicates the presence of unprofessional or unqualified users; therefore, a priority is to find experts or reputable users. Most of the existing expert-ranking approaches consider basic features, such as the total number of answers provided by a user, but ignore the quality and consistency of the user's answer. In this paper, expert-ranking techniques using g-index are proposed, and are applied to a StackOverflow forum dataset. Three techniques are proposed including Exp-PC, Rep-FS and Weighted Exp-PC. Exp-PC is an adaptation of g-index for ranking experts in StackOverflow forum. In Rep-FS, several features like voters reputation, vote ratio are proposed to measure users' expertise while Weighted Exp-PC computes user expertise by combining their Exp-PC and Rep-FS scores. We measure users' reputation and expertise according to both the quality of their answer and their consistency in providing quality answers. The experimental results of the proposed expert-ranking techniques, Exp-PC and Weighted Exp-PC in particular, validate that these methods identify genuine experts in a more effective way. • Proposal of Exp-PC and REP-FS techniques for ranking experts. • Exp-PC considers users consistency in providing quality answers. • Usage of large benchmark StackOverflow dataset for evaluation purposes. • Performance evaluation in terms of standard ranking performance measures. [ABSTRACT FROM AUTHOR]

A relevant and suitable content recommendation is an important and challenging task in e-learning. Relevant terms are retrieved in a recommender system that should also cope with varying user preferences over time. This paper proposes a novel recommendation system which provides suitable contents by refining the final frequent item patterns evolving from frequent pattern mining technique and then classifying the final contents using fuzzy logic into three levels. This is achieved by generating frequent item patterns after consolidating the user interest changes with an extended error margin quotient. Moreover, fuzzy rules are used in this work to enable the rule mining constraints for accommodating all types of learners while applying rules on the pattern tables. This method aims at mining the data stream preferences into equal-sized windows and caters to the varying user interest ratings over time. Experiments prove its efficiency and accuracy over existing conventional methods. [ABSTRACT FROM AUTHOR]

The paper contains a review of methodologies of a process of knowledge discovery from data and methods of data exploration (Data Mining), which are the most frequently used in mechanical engineering. The methodologies contain various scenarios of data exploring, while DM methods are used in their scope. The paper shows premises for use of DM methods in industry, as well as their advantages and disadvantages. Development of methodologies of knowledge discovery from data is also presented, along with a classification of the most widespread Data Mining methods, divided by type of realized tasks. The paper is summarized by presentation of selected Data Mining applications in mechanical engineering. [ABSTRACT FROM AUTHOR]

In this paper, we present a new framework for the fusion, representation, and analysis of multitemporal synthetic aperture radar (SAR) data. It leads to the definition of a new class of products representing an intermediate level between the classic Level-1 and Level-2 products. The proposed Level-1 $\beta$ products are particularly oriented toward nonexpert users. In fact, their principal characteristics are the interpretability and the suitability to be processed with standard algorithms. The main innovation of this paper is the design of a suitable RGB representation of data aiming to enhance the information content of the time-series. The physical rationale of the products is presented through examples, in which we show their robustness with respect to sensor, acquisition mode, and geographic area. A discussion about the suitability of the proposed products with Sentinel-1 imagery is also provided, showing the full compatibility with data acquired by the new European Space Agency sensor. Finally, we propose two applications based on the use of Kohonen's self-organizing maps dealing with classification problems. [ABSTRACT FROM PUBLISHER]

The question answering system is a major area in information retrieval field to get the appropriate answers to the user query instead of list of documents. Nowadays huge amount of documents uploaded in the Internet every day and the extraction of required information from those documents is a challenging and tedious task. In this paper, POS-tagger based question pattern analysis has applied for recognize the question type. Also introduce the semantic words based answer generator algorithm to extract semantic similar sentences for user query using Wordnet from the knowledgebase. The cuckoo search optimization algorithm has applied with the semantic words based answer generator algorithm to improve accuracy of retrieved sentences. The proposed algorithm is experimented with the benchmark datasets, obtained results are compared and found that outer performs than other optimization algorithms. [ABSTRACT FROM AUTHOR]

Graph-Based Data Mining (GBDM) is an emerging research topic nowadays, for the retrieval of the essential information from the graph database. There exist many algorithms that find frequent patterns in a given graph database. One such algorithm, GASTON uses support based on frequency to discover frequent patterns. The discovery phase in the Gaston algorithm is time-consuming, and the pages captured the interest of the users are ignored by the existing GASTON algorithm. This paper proposes an algorithm, Weighted-Gaston (W-Gaston) algorithm, by modifying the existing Gaston algorithm. Here, four interesting measures are developed based on the frequency, entropy, and the page duration, for the retrieval of the interesting sub-graphs. The proposed interesting measures include four types of support: (1) Support based on the page duration (W-Support), (2) Support based on the entropy (E-Support), (3) Support based on the page duration and the entropy (WE-Support), and (4) Support based on the frequency, page duration, and the entropy (FWE-Support). The simulation of the proposed work is done using the MSNBC and the weblog databases. The experimental results show that the proposed algorithm performed well as compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

Various problems are just rising with regard to mining in massive datasets, among which finding similar documents can be pinpointed. The Shingling method converts this problem to a set-based problem. Some of existing methods have used min-hashing to compress the results already driven from the shingling method and then have exploited LSH method to find candidate pairs for similarity search from all pairs of documents. In this paper, an apriori-based method is proposed for finding similar documents based on frequent itemset mining approach. To this end, the apriori algorithm is modified and is customized for similarity search problem. Modeling the similarity search problem as a frequent pattern mining problem, using a modified version of apriori, and dynamic selection the minimum support threshold are the most important advantages of the proposed method, which lead to its appropriate execution time and high quality results. The proposed method finds similar documents in less time than the combined method and MCVM method because it generates fewer candidate pairs for finding similar documents. Furthermore, experimental results show the high quality of the answers of the proposed methods. [ABSTRACT FROM AUTHOR]

Similarity search is an essential operation in such domains as data mining and content-based information retrieval. This simple operation causes considerable burden when the number of data records grows large, especially in big data applications. At the sacrifice of accuracy, approximate methods for finding similar ones have been developed to deliver effective services in a reasonable amount of time. Locality sensitive hashing is a class of efficient approximate similarity search techniques. Various algorithms have been proposed for locality sensitive hashing, which basically try to narrow down the candidate data set to be examined. The candidate data set does not always contain all the similar data to query and thus the search results are approximate. The increase in the size of a candidate set improves the recall of similar ones, but it deteriorates the processing speed. This paper is concerned with a method to increase the recall rate while not entailing significant cost. The method basically uses a random hyperplane partitioning technique to create buckets to which data objects are distributed. The nearest neighbors located on the other side of such hyperplanes can be false negatives when only the bucket to which query belongs is examined for finding similar neighbors. The proposed method extends the hyperplanes to occupy their vicinity so that the data objects in the vicinity of a hyperplane are treated as belonging to both sides of the hyperplane simultaneously. The over-sized buckets are further split by adding additional hyperplanes to control the bucket sizes. To improve the processing speed, the algorithm is realized in MapReduce paradigm on a Hadoop cluster. Some experiment results are presented to show its applicability. [ABSTRACT FROM AUTHOR]

One of the most useful developments for modern historians is the digitization of the newspaper archives, since these ones are appreciated information sources between scholars that want to understand issues associated with politics, culture, and society. This fact is promoting an increasing digitalization of this kind of archives, causing the appearance of "new" challenges, due to the fact lack of text recognition technologies application to digitalized historical newspapers. It makes difficult application of technologies that allow an automatic processing and retrieving events related to daily life, such as meteorological events. Taking into account this scenario, this paper addresses these challenges for discovering how terminology was used in news related to meteorological events performed in the Latin-American society from the XIX-XX centuries. For that, we gather digitalized historical newspapers, which are available from websites, from National newspaper and periodicals libraries associated with Colombia, Ecuador, México and Uruguay. Once these newspapers have been collected, we carry out a corpus linguistics through a bibliomining process, performing technical readings and the application of technologies (QDA-MinerLite and R), which allows retrieving the terminology about meteorological events related to newspapers from different considered countries. [ABSTRACT FROM AUTHOR]

Highlights • We project heterogeneous media data to a subspace to increase data discriminative capability. • We learn two couples of projections for different retrieval tasks. • The proposed approach is semi-supervised and can be easily applied to other modalities. • Experimental results demonstrate its effectiveness. Abstract With the rapid development of internet technology, mining and retrieving the information from internet accurately is an urgent problem, among which, cross media retrieval becomes a hot spot of current research. This paper proposes a cross media retrieval approach, which learns two couples of projections based on different retrieval tasks. We first learn a common subspace to project heterogeneous media data to the isomorphic subspace, to measure the similarity of the heterogeneous media data in the isomorphic subspace. Second, we build isomorphic and heterogeneous adjacent graphs to preserve the correlations of the cross media data. Then we combine the two processes together to learn a common subspace. We also consider intra-class and inter-class similarity of images or texts in the unified framework. Third, the L 2 norm is used to perform feature selection for different media data. Experimental results on three datasets demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Search users rely on result captions including titles, snippets, and URLs to decide whether they should read and click a particular result or not. Snippet usually serves as a query-dependent summary of its corresponding landing page and is therefore treated as one of the most important factors in search interaction process. Although there exist many efforts in improving snippet generation algorithms and incorporating more powerful interaction functions into snippets, little attention is paid to the effect of text highlighting in user behaviors. The highlighting of query terms in search snippets has been regarded as a matter of course and whether there exists a better way in snippet text highlighting remains uninvestigated. In this paper, we try to find out whether the default strategy of highlighting query terms employed by most commercial search engines is the best for search users. Through carefully designed experiments, we show that the retrieval efficiency can be affected by different term-highlighting strategies without changes in snippet contents. We also propose an automatic method which adopts CRF to learn to highlight terms based on word embedding, Wikipedia, and snippet content information. Experimental results show that the proposed method could predict highlighted terms selected by crowd workers with moderate performance. [ABSTRACT FROM AUTHOR]