• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-18
F. Dornaika

Many fundamental problems in machine learning require some form of dimensionality reduction. To this end, two different strategies were used: manifold learning and feature selection. Manifold learning (or data embedding) attempts to compute a subspace from original data by feature recombination/transformation. Feature selection aims to select the most relevant features in the original space. In this

更新日期：2021-01-19
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-18
Junzhou Zhao, Pinghui Wang, Zhouguo Chen, Jianwei Ding, John C. S. Lui, Don Towsley, Xiaohong Guan

In everyday life, we often observe unusually frequent interactions among people before or during important events, e.g., people send/receive more greetings to/from their friends on holidays than regular days. We also observe that some videos or hashtags suddenly go viral through people’s sharing on online social networks (OSNs). Do these seemingly different phenomena share a common structure? All these

更新日期：2021-01-19
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-15
Majigsuren Enkhsaikhan, Wei Liu, Eun-Jung Holden, Paul Duuring

Studies on named entity recognition (NER) often require a substantial amount of human-annotated training data. This makes technical domain-specific NER from industry data especially challenging as labelled data are scarce. Despite English as the surface language, technical jargon and writing conventions used in technical documents render the low-resource language challenges where techniques such as

更新日期：2021-01-15
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-15
Xin Hu, Jiangli Duan, Depeng Dang

Natural language question answering over knowledge graph has received widespread attention. However, the existing methods always aim to improve every phase of natural language question answering and neglect the defects; namely, not all query intentions can be identified and mapped to the correct SPARQL statement. In contrast, keyword search relies on the links among multiple keywords regardless of

更新日期：2021-01-15
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-13
Xintong Guo, Hong Gao, Zhaonian Zou

SPARQL 1.1 offers a type of navigational query for RDF systems, called regular path query (RPQ). A regular path query allows for retrieving node pairs with the paths between them satisfying regular expressions. Regular path queries are always difficult to be evaluated efficiently because of the possible large search space. Thus there has been no scalable and practical solution so far. In this paper

更新日期：2021-01-14
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-13
Jun-ying Hu, C.-J. Richard Shi, Jiang-she Zhang

At present, You only look once (YOLO) is the fastest real-time object detection system based on a unified deep neural network. During training, YOLO divides the input image to $$S \times S$$ gird cells and the only one grid cell that contains the center of an object, takes charge of detecting that object. It is not sure that the cell corresponding to the center of the object is the best choice to

更新日期：2021-01-13
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-13

Let D be a distributive lattice, and let L be a frame. In this article, we introduce the notion of L-ideals of D. We show that the set of all L-ideals of D is a distributive lattice, and some essential properties of this lattice are studied. Also, we discuss some special elements of this lattice. Moreover, we define a novel congruence relation for the concept of L-ideal of D. Finally, we study some

更新日期：2021-01-13
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-13
Jiping Zheng, Xingnan Huang, Yuan Ma

Extracting a controllable subset from a large-scale dataset so that users can fully understand the entire dataset is a significant topic for multicriteria decision making. In recent years, this problem has been widely studied, and various query models have been proposed, such as top-k, skyline, k-regret and k-coverage queries. Among these models, the k-coverage query is an ideal query method; this

更新日期：2021-01-13
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-13
Edouard Delasalles, Sylvain Lamprier, Ludovic Denoyer

Language models are at the heart of numerous works, notably in the text mining and information retrieval communities. These statistical models aim at extracting word distributions, from simple unigram models to recurrent approaches with latent variables that capture subtle dependencies in texts. However, those models are learned from word sequences only, and authors’ identities, as well as publication

更新日期：2021-01-13
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-13
Giannis Christoforidis, Pavlos Kefalas, Apostolos N. Papadopoulos, Yannis Manolopoulos

The rapid growth of users’ involvement in Location-Based Social Networks has led to the expeditious growth of the data on a global scale. The need of accessing and retrieving relevant information close to users’ preferences is an open problem which continuously raises new challenges for recommendation systems. The exploitation of points-of-interest (POIs) recommendation by existing models is inadequate

更新日期：2021-01-13
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-09
Sunipa Dev, Safia Hassan, Jeff M. Phillips

We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e.g., GloVe or word2vec). Our methods are simple and have a closed form to optimally rotate, translate, and scale to minimize root mean squared errors or maximize the average cosine similarity between two embeddings of the same vocabulary into the same dimensional

更新日期：2021-01-10
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-02
Brijnesh Jain

Linear models are a mainstay in statistical pattern recognition but do not play a role in time series classification, because they fail to account for temporal variations. To overcome this limitation, we combine linear models with dynamic time warping (dtw). We analyze the resulting warped-linear models theoretically and empirically. The three main theoretical results are (i) the Representation Theorem

更新日期：2021-01-02
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-23
Donghua Liu, Jing Li, Bo Du, Jun Chang, Rong Gao, Yujia Wu

Collaborative filtering (CF) is a common method used by many recommender systems. Traditional CF algorithms exploit users’ ratings as the sole information source to learn user preferences. However, ratings usually sparse cause a serious impact on the recommendation results. Most existing CF algorithms use ratings and textual information to alleviate the sparsity of data and then utilize matrix factorization

更新日期：2020-11-23
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-23
Lijuan Sun, Songhe Feng, Gengyu Lyu, Hua Zhang, Guojun Dai

Partial multi-label learning (PML) aims to learn from the training data where each training example is annotated with a candidate label set, among which only a subset is relevant. Despite the success of existing PML approaches, a major drawback of them lies in lacking of robustness to noisy side information. To tackle this problem, we introduce a novel partial multi-label learning with noisy side information

更新日期：2020-11-23
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-22
Liang Duan, Shuai Ma, Charu Aggarwal, Saket Sathe

Spectral clustering is one of the most popular modern clustering algorithms. It is easy to implement, can be solved efficiently, and very often outperforms other traditional clustering algorithms such as k-means. However, spectral clustering could be insufficient when dealing with most datasets having complex statistical properties, and it requires users to specify the number k of clusters and a good

更新日期：2020-11-22
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-21
José Luis Morillo-Salas, Verónica Bolón-Canedo, Amparo Alonso-Betanzos

Advances in the information technologies have greatly contributed to the advent of larger datasets. These datasets often come from distributed sites, but even so, their large size usually means they cannot be handled in a centralized manner. A possible solution to this problem is to distribute the data over several processors and combine the different results. We propose a methodology to distribute

更新日期：2020-11-22
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-16
Oleksandra Levchenko, Boyan Kolev, Djamel-Edine Yagoubi, Reza Akbarinia, Florent Masseglia, Themis Palpanas, Dennis Shasha, Patrick Valduriez

This paper presents parallel solutions (developed based on two state-of-the-art algorithms iSAX and sketch) for evaluating k nearest neighbor queries on large databases of time series, compares them based on various measures of quality and time performance, and offers a tool that uses the characteristics of application data to determine which algorithm to choose for that application and how to set

更新日期：2020-11-16
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-11
Jie Liu, Jiuyun Xu, Ruru Zhang, Stephan Reiff-Marganiec

In the field of process mining, it is worth noting that process mining techniques assume that the resulting event logs can not only continuously record the occurrence of events but also contain all event data. However, like in IoT systems, data transmission may fail due to weak signal or resource competition, which causes the company’s information system to be unable to keep a complete event log. Based

更新日期：2020-11-12
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-10
Romain Mathonat, Diana Nurbakova, Jean-François Boulicaut, Mehdi Kaytoue

It is extremely useful to exploit labeled datasets not only to learn models and perform predictive analytics but also to improve our understanding of a domain and its available targeted classes. The subgroup discovery task has been considered for more than two decades. It concerns the discovery of patterns covering sets of objects having interesting properties, e.g., they characterize or discriminate

更新日期：2020-11-12
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-07
Saadia Albane, Hachem Slimani, Hamamache Kheddouci

In this paper, we introduce a new approach based on properties of graph grammars to detect conflicts of interest (COIs) in a field represented in the form of a social network. The approach consists of specializing the adaptive star graph grammar (ASGG) of Drewes et al. (Theor Comput Sci 411:3090–3109, 2010) to express kind of subgraphs that we call $$K_4$$-type tournament graphs, corresponding to COIs

更新日期：2020-11-09
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-05
Ari Urkullu, Aritz Pérez, Borja Calvo

The stability of feature subset selection algorithms has become crucial in real-world problems due to the need for consistent experimental results across different replicates. Specifically, in this paper, we analyze the reproducibility of ranking-based feature subset selection algorithms. When applied to data, this family of algorithms builds an ordering of variables in terms of a measure of relevance

更新日期：2020-11-06
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-05
Mauricio Noris Freire, Leandro Nunes de Castro

Recommender Systems (RS) are a subclass of information filtering systems that seek to predict the rating or preference a user would give to an item. e-Recruitment is one of the domains in which RS can contribute due to presenting a list of interesting jobs to a candidate or a list of candidates to a recruiter. This study presents an up-to-date systematic review of recommender systems applied to e-Recruitment

更新日期：2020-11-06
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-03
Leopoldo Bertossi

There is a recently established correspondence between database tuples as causes for query answers in databases and tuple-based repairs of inconsistent databases with respect to denial constraints. In this work, answer-set programs that specify database repairs are used as a basis for solving computational and reasoning problems around causality in databases, including causal responsibility. Furthermore

更新日期：2020-11-03
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-10-14
Nagendra Kumar, Eshwanth Baskaran, Anand Konjengbam, Manish Singh

With the rapid growth of Twitter in recent years, there has been a tremendous increase in the number of tweets generated by users. Twitter allows users to make use of hashtags to facilitate effective categorization and retrieval of tweets. Despite the usefulness of hashtags, a major fraction of tweets do not contain hashtags. Several methods have been proposed to recommend hashtags based on lexical

更新日期：2020-10-15
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-10-10
Yanda Wang, Weitong Chen, Dechang Pi, Lin Yue

Medication recommendation is attracting enormous attention due to its promise in effectively prescribing medicines and improving the survival rate of patients. Among all challenges, drug–drug interactions (DDI) related to undesired duplication, antagonism, or alternation between drugs could lead to fatal side effects. Previous researches usually provide models with DDI knowledge to achieve DDI reduction

更新日期：2020-10-11
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-10-09
Bertrand Lebichot, Marco Saerens

The volume of data generated by internet and social networks is increasing every day, and there is a clear need for efficient ways of extracting useful information from them. As this information can take different forms, it is important to use all the available data representations for prediction; this is often referred to multi-view learning. In this paper, we consider semi-supervised classification

更新日期：2020-10-11
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-07-30
Weijiang Feng, Long Lan, Xiang Zhang, Zhigang Luo

In this paper, we propose a sequence-to-sequence affinity metric for the data association of near-online multi-object tracking. The proposed metric learns the affinity between track sequence consisting of the already associated detections and hypothesis sequence consisting of detections in the near future. With the potential hypothesis sequences, we leverage the idea that if a track sequence has a

更新日期：2020-10-04
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-09-24
Chao Wu, Qingyu Xiong, Min Gao, Qiude Li, Yang Yu, Kaige Wang

Aspect-based sentiment analysis can predict the sentiment polarity of specific aspect terms in the text. Compared to general sentiment analysis, it extracts more useful information and analyzes the sentiment more accurately in the comment text. Many previous approaches use long short-term memory networks with attention mechanisms to directly learn aspect-specific representations and model comment text

更新日期：2020-09-24
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-09-24
Avraam Tsantekidis, Anastasios Tefas

Trading strategies are constantly being employed in the financial markets in order to increase consistency, reduce human errors of judgment and boost the probability of taking profitable market positions. In this work, we attempt to transfer the knowledge of several different types of trading strategies to deep learning models. The trading strategies are applied on price data of foreign exchange trading

更新日期：2020-09-24
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-09-17
Chen Chen, Yinglong Xia, Hui Zang, Jundong Li, Huan Liu, Hanghang Tong

One-class collaborative filtering (OCCF) is a fundamental research problem in a myriad of applications where the preferences of users can only be implicitly inferred from their one-class feedback (e.g., click an ad or purchase a product). The main challenges of OCCF lie in the sparsity of user feedback and the ambiguity of unobserved preferences. To effectively address the above two challenges, side

更新日期：2020-09-18
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-09-14

Recommender systems provide personalized recommendations to the users from a large number of possible options in online stores. Matrix factorization is a well-known and accurate collaborative filtering approach for recommender system, which suffers from cold-start problem for new users and items. When new users join the system, it will take some time before they enter some ratings in the system, until

更新日期：2020-09-14
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-09-12
Mehri Davtalab, Ali Asghar Alesheikh

In recent years, point of interest (POI) recommendation has gained increasing attention all over the world. POI recommendation plays an indispensable role in assisting people to find places they are likely to enjoy. The exploitation of POIs recommendation by existing models is inadequate due to implicit correlations among users and POIs and cold start problem. To overcome these problems, this work

更新日期：2020-09-12
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-09-10
Feng Wang

The triangular fuzzy multiplicative preference relation (TFMPR) has attracted the attention of many scholars. This paper investigates the geometric consistency of TFMPR and applies it to group decision making (GDM). Firstly, by introducing two parameters, a triangular fuzzy number is transformed into an interval. According to the geometric consistency of interval multiplicative preference relation

更新日期：2020-09-10
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-09-07
Majid Ghasemi-Gol, Jay Pujara, Pedro Szekely

There is a large amount of data on the web in tabular form, such as Excel sheets, CSV files, and web tables. Often, tabular data is meant for human consumption, using data layouts that are difficult for machines to interpret automatically. Previous work uses the stylistic features of tabular cells (such as font size, border type, and background color) to classify tabular cells by their role in the

更新日期：2020-09-08
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-08-27
Yunfei Lu, Linyun Yu, Tianyang Zhang, Chengxi Zang, Peng Cui, Chaoming Song, Wenwu Zhu

The collective behavior describing spontaneously emerging social processes and events is ubiquitous in both physical society and online social media. The knowledge of collective behavior is critical in understanding and predicting social movements, fads, riots, and so on. However, detecting, quantifying, and modeling the collective behavior in cascading systems at large scale are seldom explored. In

更新日期：2020-08-28
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-08-27
Weijian Ni, Haoyu Guo, Tong Liu, Qingtian Zeng

Research teams have been well recognized as the norm in modern scientific discovery. Rather than a loose collection of researchers, a well-performing research team is composed of a number of researchers, each of them playing a particular role (i.e., principal investigator, sub-investigator or research staff) for a short- or long-term effort. Role analysis for research teams would help gain insights

更新日期：2020-08-27
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-08-26
Rodrigo Rocha Silva, Celso Massaki Hirata, Joubert de Castro Lima

In Big Data cubes with hundreds of dimensions and billions of tuples, the indexing and query operations are a challenge and the reason is the time-space exponential complexity when a full cube is computed. Therefore, solutions based on RAM may not be practical and the solutions based on hybrid memory (RAM and disk) become viable alternatives. In this paper, we propose a hybrid approach, named bCubing

更新日期：2020-08-26
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-08-25
Amit Kumar Das, Ankit Kumar Nikum, Siva Vignesh Krishnan, Dilip Kumar Pratihar

Non-traditional optimization tools have proved their potential in solving various types of optimization problems. These problems deal with either single objective or multiple/many objectives. Bonobo Optimizer (BO) is an intelligent and adaptive metaheuristic optimization algorithm inspired from the social behavior and reproductive strategies of bonobos. There is no study in the literature to extend

更新日期：2020-08-26
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-08-18
Haiyun Jiang, JunTao Liu, Sheng Zhang, Deqing Yang, Yanghua Xiao, Wei Wang

Relation extraction is one of the most important tasks in information extraction. The traditional works either use sentences or surface patterns (i.e., the shortest dependency paths of sentences) to build extraction models. Intuitively, the integration of these two kinds of methods will further obtain more robust and effective extraction models, which is, however, ignored in most of the existing works

更新日期：2020-08-19
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-08-13
Jun Ma, Yakun Wen, Liming Yang

The structural information of data contains useful prior knowledge and thus is important for designing classifiers. Extreme learning machine (ELM) has been a potential technique in handling classification problems. However, it only simply considers the prior class-based structural information and ignores the prior knowledge from statistics and geometry of data. In this paper, to capture more structural

更新日期：2020-08-14
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-08-13
Shahrouz Moaven, Jafar Habibi

The software system design phase has recently received increasing attention due to continuous growth in both the size and complexity of software systems. As a key concept of this phase, software architecture plays an important role in the software extension cycle to the extent that the success of a software project is often determined by the degree of its design efficiency. In addition, software architecture

更新日期：2020-08-14
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-08-11
Mohamed Mehdi Kandi, Shaoyi Yin, Abdelkader Hameurlain

Cloud computing has become a widely used environment for database querying. In this context, the goal of a query optimizer is to satisfy the needs of tenants and maximize the provider’s benefit. Resource allocation is an important step toward achieving this goal. Allocation methods are based on analytical formulas and statistics collected from a catalog to estimate the cost of various possible allocations

更新日期：2020-08-12
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-08-07
Chao Huang, Mingwei Lin, Zeshui Xu

The MULTIMOORA method is better than some of the existing decision making methods. However, it has not been improved to process Pythagorean fuzzy sets (PFSs). The decision results of the MULTIMOORA method greatly depend on the distance measure and score function. Although there are many studies focusing on proposing distance measures and score functions for PFSs, they still show some defects. In this

更新日期：2020-08-08
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-07-31

High utility itemset mining is an important extension of frequent itemset mining which considers unit profits and quantities of items as external and internal utilities, respectively. Since the utility function has not downward closure property, an overestimated value of utility is obtained using an anti-monotonic upper bound of utility function to prune the search space and improve the efficiency

更新日期：2020-07-31
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-07-27
Hiba Sebei, Mohamed Ali Hadj Taieb, Mohamed Ben Aouicha

Integrating social networks data in the process of promoting business and marketing applications is widely addressed by several researchers. However, regarding the isolation between social network platforms managing such data has become a challenging task facing data scientist. In this respect, the present paper is designed to put forward a special semantic data integration approach, whereby a unified

更新日期：2020-07-27
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-07-24
Ahmed Fathalla, Ahmad Salah, Kenli Li, Keqin Li, Piccialli Francesco

Recent years have witnessed the rapid development of online shopping and ecommerce websites, e.g., eBay and OLX. Online shopping markets offer millions of products for sale each day. These products are categorized into many product categories. It is crucial for sellers to correctly estimate the price of the second-hand item. State-of-the-art methods can predict the price of only one item category.

更新日期：2020-07-24
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-07-24
Panagiotis Mandros, Mario Boley, Jilles Vreeken

We consider the task of discovering functional dependencies in data for target attributes of interest. To solve it, we have to answer two questions: How do we quantify the dependency in a model-agnostic and interpretable way as well as reliably against sample size and dimensionality biases? How can we efficiently discover the exact or $$\alpha$$-approximate top-k dependencies? We address the first

更新日期：2020-07-24
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-07-22
Mohammed Kaity, Vimala Balakrishnan

The ever-increasing number of Internet users and online services, such as Amazon, Twitter and Facebook has rapidly motivated people to not just transact using the Internet but to also voice their opinions about products, services, policies, etc. Sentiment analysis is a field of study to extract and analyze public views and opinions. However, current research within this field mainly focuses on building

更新日期：2020-07-22
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-07-19
Yang You, Yuxiong He, Samyam Rajbhandari, Wenhan Wang, Cho-Jui Hsieh, Kurt Keutzer, James Demmel

Long short-term memory (LSTM) is a powerful deep learning technique that has been widely used in many real-world data-mining applications such as language modeling and machine translation. In this paper, we aim to minimize the latency of LSTM inference on cloud systems without losing accuracy. If an LSTM model does not fit in cache, the latency due to data movement will likely be greater than that

更新日期：2020-07-19
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-07-17
Shu-Kai Zhang, Cheng-Te Li, Shou-De Lin

Real-world network data can be incomplete (e.g., the social connections are partially observed) due to reasons such as graph sampling and privacy concerns. Consequently, communities detected based on such incomplete network information could be not as reliable as the ones identified based on the fully observed network. While existing studies first predict missing links and then detect communities,

更新日期：2020-07-17
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-07-17
Chunlin Li, Jianhang Tang, Youlong Luo

The application of edge clouds is becoming more and more widespread. The resource optimization is one of the important research contents of edge cloud. Generally, the edge cloud has limited computing resources and energy. Resource optimization can make tasks perform efficiently and reduce costs. Therefore, achieving high energy efficiency while ensuring a satisfying user experience is critical. This

更新日期：2020-07-17
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-07-16

Despite the extensive evolution of knowledge management (KM), the field lacks an integrated description. This situation leads to difficulties in research, teaching, and learning. To bridge this gap, this study surveys 2842 articles from top-ranked KM journals to provide a descriptive framework that guides future research in the field of knowledge management. This study also seeks to provide a comprehensive

更新日期：2020-07-16
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-07-16
Xueying Wang, Meng Jiang

The task of temporal slot filling (TSF) is to extract values of specific attributes for a given entity, called “facts”, as well as temporal tags of the facts, from text data. While existing work denoted the temporal tags as single time slots, in this paper, we introduce and study the task of Precise TSF (PTSF), that is to fill two precise temporal slots including the beginning and ending time points

更新日期：2020-07-16
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-07-13
Juan Felipe Castro, Miguel Romero, Gilberto Gutiérrez, Mónica Caniupán, Carlos Quijada-Fuentes

In this paper, we present two algorithms to obtain the convex hull of a set of points that are stored in the compact data structure called $$k^2$$-$$tree$$. This problem consists in given a set of points P in the Euclidean space obtaining the smallest convex region (polygon) containing P. Traditional algorithms to compute the convex hull do not scale well for large databases, such as spatial databases

更新日期：2020-07-13
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-07-10
Anh Phan Tuan, Bach Tran, Thien Huu Nguyen, Linh Ngo Van, Khoat Than

Analyzing texts from social media encounters many challenges due to their unique characteristics of shortness, massiveness, and dynamic. Short texts do not provide enough context information, causing the failure of the traditional statistical models. Furthermore, many applications often face with massive and dynamic short texts, causing various computational challenges to the current batch learning

更新日期：2020-07-10
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-07-09
Zahra Roozbahani, Jalal Rezaeenour, Hanif Emamgholizadeh, Amir Jalaly Bidgoly

The increasing number of researchers and scientists participating in online communities has induced big challenges for users who are looking for researchers who are interested. As a result, finding potential collaborators among the huge amount of online information is going to be even much more important in the future. Collaborator recommendation is a kind of expert recommendation in scientific fields

更新日期：2020-07-09
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-06-24
Frédéric Flouvat, Nazha Selmaoui-Folcher, Jérémy Sanhes, Chengcheng Mu, Claude Pasquier, Jean-François Boulicaut

Directed acyclic graphs (DAGs) are used in many domains ranging from computer science to bioinformatics, including industry and geoscience. They enable to model complex evolutions where spatial objects (e.g., soil erosion) may move, (dis)appear, merge or split. We study a new graph-based representation, called attributed DAG (a-DAG). It enables to capture interactions between objects as well as information

更新日期：2020-06-25
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-06-18
Md Abul Bashar, Richi Nayak, Nicolas Suzor

Supervised machine learning methods depend highly on the quality of the training dataset and the underlying model. In particular, neural network models, that have shown great success in dealing with natural language problems, require a large dataset to learn a vast number of parameters. However, it is not always easy to build a large (labelled) dataset. For example, due to the complex nature of tweets

更新日期：2020-06-19
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-06-16
Diogo Domingues Regateiro, Óscar Mortágua Pereira, Rui L. Aguiar

As data become an increasingly important asset for organizations, so does the access control policies that protect aforesaid data. Many subjects (public, researchers, etc.) are interested in accessing these data, leading to the desire for simple access control. However, some scenarios use vague concepts, such as the “researcher’s expertise”, when making access control decisions. Therefore, access control

更新日期：2020-06-16
• Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-06-16
Nahuel Verdugo, Eduardo Guzmán, Cristina Urdiales

Nowadays, many R&I institutions are presently implementing mechanisms to measure and rate their scientific production so as to comply with current legislation and to support research management and decision making. In many cases, they rely on the implementation of current research information systems (CRIS). This is a challenging task that often requires major human intervention and supervision to

更新日期：2020-06-16
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