Search

The increasing amount of data from web systems data is becoming one of the most valuable resources for information retrieval and knowledge discovery. The huge content of information makes it an important area for data mining research. To analyze the dependencies of the outcoming data, expressed as query scenarios, we present a new approach for evaluating the behavior of interactive web systems by applying different data mining techniques to solve the problem. We propose tools that take outcoming logs as input, analyze them, and provide information about web client actions. Qualitative and quantitative automatic evaluation of the data can explain the connections between the most significant parameters of the system in particular scenarios. In this paper, we propose a new method, which can be used to efficiently verify the type of client behavior of a web system or design of the system. The analysis of results demonstrates the possibility of efficient pattern search. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of extracting variable-depth "logical document hierarchy" from long documents, namely organizing the recognized "physical document objects" into hierarchical structures. The discovery of logical document hierarchy is the vital step to support many downstream applications (e.g., passage-based retrieval and high-quality information extraction). However, long documents, containing hundreds or even thousands of pages and a variable-depth hierarchy, challenge the existing methods. To address these challenges, we develop a framework, namely Hierarchy Extraction from Long Document (HELD), where we "sequentially" insert each physical object at the proper position on the current tree. Determining whether each possible position is proper or not can be formulated as a binary classification problem. To further improve its effectiveness and efficiency, we study the design variants in HELD, including traversal orders of the insertion positions, heading extraction explicitly or implicitly, tolerance to insertion errors in predecessor steps, and so on. As for evaluations, we find that previous studies ignore the error that the depth of a node is correct while its path to the root is wrong. Since such mistakes may worsen the downstream applications seriously, a new measure is developed for a more careful evaluation. The empirical experiments based on thousands of long documents from Chinese financial market, English financial market and English scientific publication show that the HELD model with the "root-to-leaf" traversal order and explicit heading extraction is the best choice to achieve the tradeoff between effectiveness and efficiency with the accuracy of 0.972 6, 0.729 1 and 0.957 8 in the Chinese financial, English financial and arXiv datasets, respectively. Finally, we show that the logical document hierarchy can be employed to significantly improve the performance of the downstream passage retrieval task. In summary, we conduct a systematic study on this task in terms of methods, evaluations, and applications. [ABSTRACT FROM AUTHOR]

The large amount of digital information that is increasingly available in the manufacturing process makes information retrieval (IR) a critical issue in this knowledge-based manufacturing era. Many IR models including the vector space model (VSM), Boolean model, fuzzy set model and probability model have been proposed in the literature. However, the performance based on these models is not satisfactory to the expectations of the end users. The reasons contributing to the end users’ dissatisfaction are the imprecise query formulation and poor document representations. Most of the models of document representation are based on conventional statistical techniques. However, during manufacturing process document retrieval, the various qualitative data and attributes in the document database could not be easily analysed by using the current statistical approaches. This paper proposes a rough-set-based approach to enrich document representation. The document classification rules are generated and the premise terms are provided by the rough-set approach. Therefore, the retrieval performance of the VSM is enhanced through support from the rough-set-based approach. A case study that includes the comparison of manufacturing process document retrieval performance through the standard VSM and the rough-set-based approach is illustrated by empirical data. This paper forms the basis for solving many other similar document-retrieval problems that occur in manufacturing industry. [ABSTRACT FROM AUTHOR]

The role of information management and retrieval in production processes has been gaining in importance in recent years. In this context, the ability to search for and quickly find the small piece of information needed from the huge amount of information available has crucial importance. One category of tools devoted to such a task is represented by search engines. Satisfying the basic needs of the Web user has led to the research of new tools that aim at helping more sophisticated users (communities, companies, interest groups) with more elaborate methods. An example is the use of clustering and classification algorithms or other specific data mining techniques. In such a context, the proper use of a thematic search engine is a crucial tool in supporting and orienting many activities. Several practical and theoretical problems arise in developing such tools, and we try to face some of these in this paper, extending previous work on Web mining. Here we consider two related problems: how to select an appropriate set of keywords for a thematic engine taking into account the semantic and linguistic extensions of the search context, and how to select and rank a subset of relevant pages given a set of search keywords. Both problems are solved using the same framework, based on a graph representation of the available information and on the search of particular node subsets of such a graph. Such subsets are effectively identified by a maximum-weight clique algorithm customized ad hoc for specific problems. The methods have been developed in the framework of a funded research project for the development of new Web search tools, they have been tested on real data, and are currently being implemented in a prototypal thematic search engine. The Web mining method presented in this paper can be applied to Web-based design and manufacturing. [ABSTRACT FROM AUTHOR]

In this paper, we present a survey of modeling and simulation approaches to describe information retrieval basics. We investigate its methods, its challenges, its models, its components and its applications. Our contribution is twofold: on the one hand, reviewing the literature on discovery some search techniques that help to get pertinent results and reach an effective search, and on the other hand, discussing the different research perspectives for study and compare more techniques used in information retrieval. This paper will also shedding the light on some of the famous AI applications in the legal field. [ABSTRACT FROM AUTHOR]

The Internet of Things has emerged as an evolving paradigm and has developed its presence in a variety of domains around us. The emergence of IoT has also emphasized the need to cater to challenges such as interoperability, smart IoT components adoption, authentication and authorization, networking, information retrieval, and several other issues. The ubiquitous nature and interconnection between various devices supported by machine learning, artificial intelligence, cloud computing, big data, and blockchain lead to a generation of large amounts of data. In order to find useful information from such data is a tedious task and involves high computation. The domain of Information Retrieval helps us to identify and manage environmental factors of data collected through sensors. The data gathered may be heterogeneous and from different sources. This demands the need for better retrieval efficiency, accuracy, and systematic models for gathering and managing the sensed data. Designing such a model with security and privacy is a major concern. The acquired knowledge from those models will be helpful for data analytics, performance-boosting, decision making, and managing the resources efficiently. A detailed study of the importance of Information Retrieval and Data Analytics in the Internet of Things is presented in this paper. [ABSTRACT FROM AUTHOR]

All pairs similarity search (APSS) is used in many web search and data mining applications. Previous work has used techniques such as comparison filtering, inverted indexing, and parallel accumulation of partial results. However, shuffling intermediate results can incur significant communication overhead as data scales up. This paper studies a scalable two-phase approach called Partition-based Similarity Search (PSS). The first phase is to partition the data and group vectors that are potentially similar. The second phase is to run a set of tasks where each task compares a partition of vectors with other candidate partitions. Due to data sparsity and the presence of memory hierarchy, accessing feature vectors during the partition comparison phase incurs significant overhead. This paper introduces a cache-conscious design for data layout and traversal to reduce access time through size-controlled data splitting and vector coalescing, and it provides an analysis to guide the choice of optimization parameters. The evaluation results show that for the tested datasets, the proposed approach can lead to an early elimination of unnecessary I/O and data communication while sustaining parallel efficiency with one order of magnitude of performance improvement and it can also be integrated with LSH for approximated APSS. [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 a world characterized by a huge diversification of goods, trademarks, specifications, prices, and purchasing outlets and methods, an increasing number of people from all over the world seeks the help of modern technologies for answering questions such as "what exactly should I buy?", "at what price?", and "from where?" This tendency has grown deeper with the geolocation of web technologies becoming more and more powerful, and computers becoming more and more mobile and better connected to the Internet. This paper presents a new methodology to identify which products are the most relevant to each individual customer preferences, display the results in an appropriate form. A combination of indexing and ontology is used in the proposed methodology in order to achieve the desired result, which is namely to create, for each customer, a "user-profile" that could describe and rank mathematically customers behaviour attitudes, and acts. Moreover, the methodology followed in this paper applies an algebraic vector model to match the available commercial offers with the acts committed by the customer while navigating the Internet, in order to determine the nearest and the most relevant offers. The results are then displayed on a web map platform. The methodology adopts a combination of spatial, statistical, and neighbourhood analyses in order to select the suitable results. The spatial and non-spatial (i.e. attributes) of each offer are published in a navigable and interactive manner on the web map. [ABSTRACT FROM AUTHOR]

As a result of the evolution of the Internet and the massive amount of data that is transmitted every second, as well as the methods for protecting and preserving it and distinguishing those who are authorized to view it, the role of cyber security has evolved to provide the best protection for information over the network. In this paper, the researcher discusses the role of data mining methods in cyber security. Data mining has several uses in security, including national security (for example, surveillance) and cyber security (e.g., virus detection). Attacks against buildings and the destruction of key infrastructure, such as power grids and telecommunications networks, are examples of national security concerns. Cybersecurity is concerned with safeguarding computer and network systems from harmful malware such as Trojan horses and viruses. In addition, data mining is being used to deliver solutions such as intrusion detection and auditing. [ABSTRACT FROM AUTHOR]

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In an Indian law system, different courts publish their legal proceedings every month for future reference of legal experts and common people. Extensive manual labor and time are required to analyze and process the information stored in these lengthy complex legal documents. Automatic legal document processing is the solution to overcome drawbacks of manual processing and will be very helpful to the common man for a better understanding of a legal domain. In this paper, we are exploring the recent advances in the field of legal text processing and provide a comparative analysis of approaches used for it. In this work, we have divided the approaches into three classes NLP based, deep learning-based and, KBP based approaches. We have put special emphasis on the KBP approach as we strongly believe that this approach can handle the complexities of the legal domain well. We finally discuss some of the possible future research directions for legal document analysis and processing. [ABSTRACT FROM AUTHOR]

Relational Database Model (RDM) has been proven to be a very useful data-storage technique. As information is stored as data in relational databases, the induction of concepts from data is a pivotal topic in the data mining field. Formal Concept Analysis (FCA) turns out to be a perfect instrument for a meaningful and conceptual exploration of the stored data. However, many databases in practice are often hybrid ones. This paper proposes an approach towards extracting concepts from hybrid relations, which preserve the RDM and allow complex values such as relations in columns for relations. The method can be described as follows: the sub-tuples/subattributes pairs are regard as new attributes and inserted into a main relation, and we further obtain a new complex relation called a target relation from which concepts can be extracted. The insertion operation is semantics-preserving and extent-preserving. In addition, this paper analyzed the structural connections among concept lattices in detail, which can be used to construct a complex concept lattice from simple ones. The method may reduce time complexity of constructing the concept lattice of a complex relation. [ABSTRACT FROM AUTHOR]

In modern society, people are heavily reliant on information available online through various channels, such as websites, social media, and web portals. Examples include searching for product prices, news, weather, and jobs. This paper focuses on an area of information extraction in e-recruitment, or job searching, which is increasingly used by a large population of users in across the world. Given the enormous volume of information related to job descriptions and users' profiles, it is complicated to appropriately match a user's profile with a job description, and vice versa. Existing information extraction techniques are unable to extract contextual entities. Thus, they fall short of extracting domain-specific information entities and consequently affect the matching of the user profile with the job description. The work presented in this paper aims to extract entities from job descriptions using a domain-specific dictionary. The extracted information entities are enriched with knowledge using Linked Open Data. Furthermore, job context information is expanded using a job description domain ontology based on the contextual and knowledge information. The proposed approach appropriately matches users' profiles/queries and job descriptions. The proposed approach is tested using various experiments on data from real life jobs' portals. The results show that the proposed approach enriches extracted data from job descriptions, and can help users to find more relevant jobs. [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]

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]

Data Science is an interdisciplinary field involving the development of processes and systems to extract knowledge and understanding from data in different formats and from different sources. Considering the large amount of data generated and managed by public safety agencies, Data Science applications in the police sector are numerous. More important are the advantages that the different applications of Data Science could provide the police on issues such as the optimization of resources, the increase of efficiency and effectiveness, the modernization and its exemplariness when compared with other institutions. In this paper we present different potential applications fields of Data Science for the police. In addition, we focus on the case of VeriPol, a tool for automatic detection of false violent robbery reports, currently under development by the Spanish National Police. In particular, we illustrate a detailed analysis of the results of a recent pilot study aimed at assessing the effectiveness of the tool. [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]

Big multimedia data is heterogeneous in essence, that is, the data may be a mixture of video, audio, text, and images. This is due to the prevalence of novel applications in recent years, such as social media, video sharing, and location based services (LBS), etc. In many multimedia applications, for example, video/image tagging and multimedia recommendation, text classification techniques have been used extensively to facilitate multimedia data processing. In this paper, we give a comprehensive review on feature selection techniques for text classification. We begin by introducing some popular representation schemes for documents, and similarity measures used in text classification. Then, we review the most popular text classifiers, including Nearest Neighbor (NN) method, Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), and Neural Networks. Next, we survey four feature selection models, namely the filter, wrapper, embedded and hybrid, discussing pros and cons of the state-of-the-art feature selection approaches. Finally, we conclude the paper and give a brief introduction to some interesting feature selection work that does not belong to the four models. [ABSTRACT FROM AUTHOR]

Background: The overall goal of the BioCreative Workshops is to promote the development of text mining and text processing tools which are useful to the communities of researchers and database curators in the biological sciences. To this end BioCreative I was held in 2004, BioCreative II in 2007, and BioCreative II.5 in 2009. Each of these workshops involved humanly annotated test data for several basic tasks in text mining applied to the biomedical literature. Participants in the workshops were invited to compete in the tasks by constructing software systems to perform the tasks automatically and were given scores based on their performance. The results of these workshops have benefited the community in several ways. They have 1) provided evidence for the most effective methods currently available to solve specific problems; 2) revealed the current state of the art for performance on those problems; 3) and provided gold standard data and results on that data by which future advances can be gauged. This special issue contains overview papers for the three tasks of BioCreative III. Results: The BioCreative III Workshop was held in September of 2010 and continued the tradition of a challenge evaluation on several tasks judged basic to effective text mining in biology, including a gene normalization (GN) task and two protein-protein interaction (PPI) tasks. In total the Workshop involved the work of twenty-three teams. Thirteen teams participated in the GN task which required the assignment of EntrezGene IDs to all named genes in full text papers without any species information being provided to a system. Ten teams participated in the PPI article classification task (ACT) requiring a system to classify and rank a PubMed® record as belonging to an article either having or not having "PPI relevant" information. Eight teams participated in the PPI interaction method task (IMT) where systems were given full text documents and were required to extract the experimental methods used to establish PPIs and a text segment supporting each such method. Gold standard data was compiled for each of these tasks and participants competed in developing systems to perform the tasks automatically. BioCreative III also introduced a new interactive task (IAT), run as a demonstration task. The goal was to develop an interactive system to facilitate a user's annotation of the unique database identifiers for all the genes appearing in an article. This task included ranking genes by importance (based preferably on the amount of described experimental information regarding genes). There was also an optional task to assist the user in finding the most relevant articles about a given gene. For BioCreative III, a user advisory group (UAG) was assembled and played an important role 1) in producing some of the gold standard annotations for the GN task, 2) in critiquing IAT systems,. 3) in providing guidance for a future more rigorous evaluation of IAT systems. Six teams participated in the IAT demonstration task and received feedback on their systems from the UAG group. Besides innovations in the GN and PPI tasks making them more realistic and practical and the introduction of the IAT task, discussions were begun on community data standards to promote interoperability and on user requirements and evaluation metrics to address utility and usability of systems. Conclusions: In this paper we give a brief history of the BioCreative Workshops and how they relate to other text mining competitions in biology. This is followed by a synopsis of the three tasks GN, PPI, and IAT in BioCreative III with figures for best participant performance on the GN and PPI tasks. These results are discussed and compared with results from previous BioCreative Workshops and we conclude that the best performing systems for GN, PPIACT and PPI-IMT in realistic settings are not sufficient for fully automatic use. This provides evidence for the importance of interactive systems and we present our vision of how best to construct an interactive system for a GN or PPI like task in the remainder of the paper. [ABSTRACT FROM AUTHOR]

One of the biggest challenges in the process of establishing modern e-learning systems is figuring out ways to leverage legacy course materials and integrating them in the new information systems. Existing exam query repositories in particular are a very valuable data source, but one which usually lacks enough metadata to help establish relationships between exam questions and corresponding learning concepts whose adoption is being evaluated. In this paper we present the continuation of our research regarding the usage of educational data mining methods able to automatically annotate pre-existing exam queries with information about learning concepts they relate to. In our novel approach we leverage both textual and visual information contained in the queries. By combining the power of natural language processing which focuses on the text of the question, and annotated data extracted from figures accompanying the questions, we are able to further refine our classification methods and achieve noticeably improved results. By identifying learning concepts more accurately we further facilitate automatic creation of exams as well as even better insight into learning concept adoption. Our approach is again applied on data gathered from a large scale university course, and the results were validated in consultation with educational domain experts. [ABSTRACT FROM AUTHOR]

Purpose – The paper aims to explore the performance of redundancy and novelty mining in the business blogosphere, which has not been studied before. Design/methodology/approach – Novelty mining techniques are implemented to single out novel information out of a massive set of text documents. This paper adopted the mixed metric approach which combines symmetric and asymmetric metrics. Findings – The results show that the novelty mining system can detect novel and redundant blogs in the dataset of business blogs with a very high accuracy. Originality/value – This paper shows that novelty mining techniques can be applied to business blogs to help organizations filter redundant information, and that the cosine and mixed metrics approaches produce better results. [ABSTRACT FROM AUTHOR]

Purpose - The aim of the paper is to put forward a structured mechanism for web search evaluation. The paper seeks to point to useful scientific research and show how information practitioners can use these methods in evaluation of search on the web for their users. Design/methodology/approach - The paper puts forward an approach which utilizes traditional laboratory-based evaluation measures such as average precision/precision at N documents, augmented with diagnostic measures such as link broken, etc., which are used to show why precision measures are depressed as well as the quality of the search engines crawling mechanism. Findings - The paper shows how to use diagnostic measures in conjunction with precision in order to evaluate web search. Practical implications - The methodology presented in this paper will be useful to any information professional who regularly uses web search as part of their information seeking and needs to evaluate web search services. Originality/value - The paper argues that the use of diagnostic measures is essential in web search, as precision measures on their own do not allow a searcher to understand why search results differ between search engines. [ABSTRACT FROM AUTHOR]

This paper presents a new information acquisition problem motivated by business applications where customer data has to be acquired with a specific modeling objective in mind. In the last two decades, there has been substantial work in two different fields—optimal experimental design and machine learning—that has addressed the issue of acquiring data in a selective manner with a specific objective in mind. We show that the problem presented here is different from the classic model-based data acquisition problems considered thus far in the literature in both fields. Building on work in optimal experimental design and in machine learning, we develop a new active learning technique for the information acquisition problem presented in this paper. We demonstrate that the proposed method performs well based on results from applying this method across 20 Web usage and machine learning data sets. [ABSTRACT FROM AUTHOR]

XML is increasingly important in data exchange and information management. A great deal of efforts have been spent in developing efficient techniques for storing, querying, indexing and accessing XML documents. In this paper we propose a new approach to clustering XML data. In contrast to previous work, which focused on documents defined by different DTDs, the proposed method works for documents with the same DTD. Our approach is to extract features from documents, modeled by ordered labeled trees, and transform the documents to vectors in a high-dimensional Euclidean space based on the occurrences of the features in the documents. We then reduce the dimensionality of the vectors by principal component analysis (PCA) and cluster the vectors in the reduced dimensional space. The PCA enables one to identify vectors with co-occurrent features, thereby enhancing the accuracy of the clustering. We also discuss an extension of our techniques to XML retrieval. Experimental results based on documents obtained from Wisconsin's XML data bank show the effectiveness and good performance of the proposed techniques. [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]

As an important preprocessing step of information retrieval and information processing, the accuracy of stop words' elimination directly influences the ultimate result of retrieval and mining. In information retrieval, stop words' elimination can compress the storage space of index, and in text mining, it can reduce the dimension of vector space enormously, save the storage space of vector space and speed up the calculation. However, Chinese patents are a kind of legal documents containing technical information and the general Chinese stop words list is not applicable for them. This paper advances two methodologies for Chinese patents. One is based on word frequency and the other on statistics. Through experiments on real patents data, these two methodologies' accuracy are compared under several corpuses with different scale, and also compared with general stop list. The experiment result indicates that both of these two methodologies can extract the stop words suitable for Chinese patents and the accuracy of Methodology based on statistics is a little higher than the one based on word frequency. [ABSTRACT FROM AUTHOR]

Bias in Artificial Intelligence (AI) is a critical and timely issue due to its sociological, economic and legal impact, as decisions made by biased algorithms could lead to unfair treatment of specific individuals or groups. Multiple surveys have emerged to provide a multidisciplinary view of bias or to review bias in specific areas such as social sciences, business research, criminal justice, or data mining. Given the ability of Semantic Web (SW) technologies to support multiple AI systems, we review the extent to which semantics can be a "tool" to address bias in different algorithmic scenarios. We provide an in-depth categorisation and analysis of bias assessment, representation, and mitigation approaches that use SW technologies. We discuss their potential in dealing with issues such as representing disparities of specific demographics or reducing data drifts, sparsity, and missing values. We find research works on AI bias that apply semantics mainly in information retrieval, recommendation and natural language processing applications and argue through multiple use cases that semantics can help deal with technical, sociological, and psychological challenges. [ABSTRACT FROM AUTHOR]

Purpose - The text aims to present the results of the analysis on the state of knowledge on Customer Education (CE) and propose future research directions in marketing area. The filtering criteria was the chance to apply the gathered information in the marketing area. Research method - The method of the systematic literature review was applied. Regarding customer education keywords, I examined two databases (SCOPUS and Web of Science). Then the results were analysed with the assistance of VOSviewer and using content analysis (qualitative research). A total number of 79 articles was analysed. Results - Customer Education (CE) is rarely examined, especially in marketing. The concept evolves from instructions to co-creation. Bibliographic analysis shows the relation of CE with CX, customer satisfaction and loyalty to the organisation when it aims for internal and external customers. Originality/value/implications/recommendations - Due to the novelty of the problem and its development in business, it is worth stimulating debate on CE in science. The paper aims to fill a gap in literature reviews on the issue and might be a voice in pointing out future research directions. Future research can gravitate toward new channels of CE, learning communities, and the relationship between CE and: customer behaviour, CX, application of AI and data mining, and usage of learning management systems (LMS). [ABSTRACT FROM AUTHOR]

Similarity search, that is, finding similar items in massive data, is a fundamental computing problem in many fields such as data mining and information retrieval. However, for large‐scale and high‐dimension data, it suffers from high computational complexity, requiring tremendous computation resources. Here, based on the low‐power self‐selective memristors, for the first time, we propose an in‐memory search (IMS) system with two innovative designs. First, by exploiting the natural distribution law of the devices resistance, a hardware locality sensitive hashing encoder has been designed to transform the real‐valued vectors into more efficient binary codes. Second, a compact memristive ternary content addressable memory is developed to calculate the Hamming distances between the binary codes in parallel. Our IMS system demonstrated a 168× energy efficiency improvement over all‐transistors counterparts in clustering and classification tasks, while achieving a software‐comparable accuracy, thus providing a low‐complexity and low‐power solution for in‐memory data mining applications. [ABSTRACT FROM AUTHOR]

It is common that different people share the same name. When it occurs in bibliography databases, it worsens the performance of information retrieval and data management. In this paper, we address the problem of name disambiguation and propose two different strategies, one classifier for each name (OCEN) and one classifier for all names (OCAN). Both strategies OCEN and OCAN are based on extreme learning machine (ELM) which shows similar or better generalization performance and faster learning speed than support vector machines (SVM) and least squares support vector machines (LS-SVM). We conduct experiments to compare the performance of ELM, SVM and LS-SVM in the two strategies. [ABSTRACT FROM AUTHOR]

This study aims to build an automatic survey generation tool, named CitationAS, based on citation content as represented by the set of citing sentences in the original articles. Firstly, we apply LDA to analyse topic distribution of citation content. Secondly, in CitationAS, we use bisecting K-means, Lingo and STC to cluster retrieved citation content. Then Word2Vec, WordNet and combination of them are applied to generate cluster labels. Next, we employ TF-IDF, MMR, as well as considering sentence location information, to extract important sentences, which are used to generate surveys. Finally, we adopt manual evaluation for the generated surveys. In experiments, we choose 20 high-frequency phrases as search terms. Results show that Lingo-Word2Vec, STC-WordNet and bisecting K-means-Word2Vec have better clustering effects. In 5 points evaluation system, survey quality scores obtained by designing methods are close to 3, indicating surveys are within acceptable limits. When considering sentence location information, survey quality will be improved. Combination of Lingo, Word2Vec, TF-IDF or MMR can acquire higher survey quality. The manual evaluation method may have a certain subjectivity. We use a simple linear function to combine Word2Vec and WordNet that may not bring out their strengths. The generated surveys may not contain some newly created knowledge of some articles which may concentrate on sentences with no citing. CitationAS tool can automatically generate a comprehensive, detailed and accurate survey according to user's search terms. It can also help researchers learn about research status in a certain field. CitaitonAS tool is of practicability. It merges cluster labels from semantic level to improve clustering results. The tool also considers sentence location information when calculating sentence score by TF-IDF and MMR. [ABSTRACT FROM AUTHOR]

Data mining is an aggressively concept in information retrieval based on different attributes from different data sources. For effective data collection from data sources with respect to relevant data, one-class learning is required to perform labeled based classification with individual training sequences on attributes. In clustering, uncertain data with different data set visualization. Uncertain One Class Clustering (UOCC) with support vector machine to explore data summarization in terms of user preference. UOCC process single attributes from reliable data streams for inconsistent data. So that in this paper we propose Clustering with Multi-Attribute Framework (CMAF) to group multiple attributes to explore uncertain data from reliable data. CMAF construct matrix with different reliable attributes based on relevant features. Proposed approach defines effective data summarization for relevant data with attributes partitioning and constructs user profile based on relative attributes. Experimental results come out for proposed approach gives better and expressive results with comparison of state of art methods. [ABSTRACT FROM AUTHOR]