-
Distributed processing of regular path queries in RDF graphs 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
-
Saliency-based YOLO for single target detection 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
-
L -ideals and rough sets based on L -ideals Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-13 Ali Akbar Estaji, Toktam Haghdadi, Javad Farokhi Ostad
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
-
Efficient computation of deletion-robust k -coverage queries 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
-
Deep dynamic neural networks for temporal language modeling in author communities 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
-
RELINE: point-of-interest recommendations using multiple network embeddings 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
-
Closed form word embedding alignment 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
-
Warped softmax regression for time series classification 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
-
A hybrid neural network approach to combine textual information and rating information for item recommendation 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
-
Partial multi-label learning with noisy side information 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
-
Improving spectral clustering with deep embedding, cluster estimation and metric learning 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
-
Dealing with heterogeneity in the context of distributed feature selection for classification 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
-
BestNeighbor: efficient evaluation of kNN queries on large time series databases 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
-
A repairing missing activities approach with succession relation for event logs 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
-
Anytime mining of sequential discriminative patterns in labeled sequences 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
-
A graph grammar and $$K_{4}$$ K 4 -type tournament-based approach to detect conflicts of interest in a social network 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
-
Statistical model for reproducibility in ranking-based feature selection 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
-
e-Recruitment recommender systems: a systematic review 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
-
Specifying and computing causes for query answers in databases via database repairs and repair-programs 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
-
Hashtag recommendation for short social media texts using word-embeddings and external knowledge 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
-
Adversarially regularized medication recommendation model with multi-hop memory network 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
-
An experimental study of graph-based semi-supervised classification with additional node information 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
-
Learning sequence-to-sequence affinity metric for near-online multi-object tracking 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
-
A relative position attention network for aspect-based sentiment analysis 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
-
Transferring trading strategy knowledge to deep learning models 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
-
Incremental one-class collaborative filtering with co-evolving side networks 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
-
Cross-domain recommender system using generalized canonical correlation analysis Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-09-14 Seyed Mohammad Hashemi, Mohammad Rahmati
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
-
A POI recommendation approach integrating social spatio-temporal information into probabilistic matrix factorization 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
-
Geometric consistency of triangular fuzzy multiplicative preference relation and its application to group decision making 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
-
Learning cell embeddings for understanding table layouts 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
-
Exploring the collective human behavior in cascading systems: a comprehensive framework 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
-
Automatic role identification for research teams with ranking multi-view machines 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
-
Big high-dimension data cube designs for hybrid memory systems 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
-
Multi-objective Bonobo Optimizer (MOBO): an intelligent heuristic for multi-criteria optimization 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
-
Surface pattern-enhanced relation extraction with global constraints 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
-
Fisher-regularized supervised and semi-supervised extreme learning machine 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
-
A fuzzy-AHP-based approach to select software architecture based on quality attributes (FASSA) 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
-
SLA-driven resource re-allocation for SQL-like queries in the cloud 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
-
Pythagorean fuzzy MULTIMOORA method based on distance measure and score function: its application in multicriteria decision making process 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
-
An efficient projection-based method for high utility itemset mining using a novel pruning approach on the utility matrix Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-07-31 Mohammad Karim Sohrabi
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
-
SNOWL model: social networks unification-based semantic data integration 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
-
Deep end-to-end learning for price prediction of second-hand items 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.
-
Discovering dependencies with reliable mutual information 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
-
Sentiment lexicons and non-English languages: a survey 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
-
Fast LSTM by dynamic decomposition on cloud and distributed systems 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
-
A joint optimization framework for better community detection based on link prediction in social networks 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,
-
Service cost-based resource optimization and load balancing for edge and cloud environment 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
-
A descriptive framework for the field of knowledge management Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-07-16 Yousra Harb, Emad Abu-Shanab
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
-
Precise temporal slot filling via truth finding with data-driven commonsense 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
-
Efficient computation of the convex hull on sets of points stored in a k - tree compact data structure 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
-
Bag of biterms modeling for short texts 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
-
A systematic survey on collaborator finding systems in scientific social networks 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
-
Mining evolutions of complex spatial objects using a single-attributed Directed Acyclic Graph 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
-
Regularising LSTM classifier by transfer learning for detecting misogynistic tweets with small training set 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
-
A search space optimization method for fuzzy access control auditing 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
-
Integrating researchers’ scientific production information through Ogmios 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
-
Modeling, learning, and simulating human activities of daily living with behavior trees Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-06-01 Yannick Francillette, Bruno Bouchard, Kévin Bouchard, Sébastien Gaboury
Autonomy is a key factor in the quality of life of a person. With the aging of the population, an increasing number of people suffers from a reduced level of autonomy. That compromises their capacity of performing their daily activities and causes safety issues. The new concept of ambient assisted living (AAL), and more specifically its application in smart homes for supporting elderly people, constitutes
-
Survival neural networks for time-to-event prediction in longitudinal study Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-05-21 Jianfei Zhang; Lifei Chen; Yanfang Ye; Gongde Guo; Rongbo Chen; Alain Vanasse; Shengrui Wang
Time-to-event prediction has been an important practical task for longitudinal studies in many fields such as manufacturing, medicine, and healthcare. While most of the conventional survival analysis approaches suffer from the presence of censored failures and statistically circumscribed assumptions, few attempts have been made to develop survival learning machines that explore the underlying relationship
-
Review selection based on content quality Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-05-21 Nan Tian; Yue Xu; Yuefeng Li
Consumer-generated reviews have become increasingly important in decision-making processes for customers. Meanwhile, the overwhelming quantity of review data makes it extremely difficult to find useful information from it. A considerable amount of studies have attempted to address this problem by selecting reviews that might be helpful for and preferred by users. However, the performance of existing
-
TAILOR: time-aware facility location recommendation based on massive trajectories Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-05-20 Zhixin Qi; Hongzhi Wang; Tao He; Chunnan Wang; Jianzhong Li; Hong Gao
In traditional facility location recommendations, the objective is to select the best locations which maximize the coverage or convenience of users. However, since users’ behavioral habits are often influenced by time, the temporal impacts should not be neglected in recommendation. In this paper, we study the problem of time-aware facility location recommendation problem, taking the time factor into
Contents have been reproduced by permission of the publishers.