Information Systems最新文献

PARK: Personalized academic retrieval with knowledge-graphs 个性化学术检索与知识图谱

IF 3 2区计算机科学

Information Systems Pub Date : 2025-06-03 DOI: 10.1016/j.is.2025.102574

Pranav Kasela , Gabriella Pasi , Raffaele Perego

{"title":"PARK: Personalized academic retrieval with knowledge-graphs","authors":"Pranav Kasela , Gabriella Pasi , Raffaele Perego","doi":"10.1016/j.is.2025.102574","DOIUrl":"10.1016/j.is.2025.102574","url":null,"abstract":"<div><div>Academic Search is a search task aimed to manage and retrieve scientific documents like journal articles and conference papers. Personalization in this context meets individual researchers’ needs by leveraging, through user profiles, the user related information (e.g. documents authored by a researcher), to improve search effectiveness and to reduce the information overload. While citation graphs are a valuable means to support the outcome of recommender systems, their use in personalized academic search (with, e.g. nodes as papers and edges as citations) is still under-explored.</div><div>Existing personalized models for academic search often struggle to fully capture users’ academic interests. To address this, we propose a two-step approach: first, training a neural language model for retrieval, then converting the academic graph into a knowledge graph and embedding it into a shared semantic space with the language model using translational embedding techniques. This allows user models to capture both explicit relationships and hidden structures in citation graphs and paper content. We evaluate our approach in four academic search domains, outperforming traditional graph-based and personalized models in three out of four, with up to a 10% improvement in MAP@100 over the second-best model. This highlights the potential of knowledge graph-based user models to enhance retrieval effectiveness.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"134 ","pages":"Article 102574"},"PeriodicalIF":3.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144270824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Achieving framed autonomy in AI-augmented business process management systems through automated planning 通过自动化规划在人工智能增强的业务流程管理系统中实现框架自治

IF 3 2区计算机科学

Information Systems Pub Date : 2025-06-02 DOI: 10.1016/j.is.2025.102573

Giacomo Acitelli , Anti Alman , Fabrizio Maria Maggi , Andrea Marrella

{"title":"Achieving framed autonomy in AI-augmented business process management systems through automated planning","authors":"Giacomo Acitelli , Anti Alman , Fabrizio Maria Maggi , Andrea Marrella","doi":"10.1016/j.is.2025.102573","DOIUrl":"10.1016/j.is.2025.102573","url":null,"abstract":"<div><div>AI-augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems empowered by Artificial Intelligence (AI) technology for autonomously unfolding and adapting the execution flow of business processes (BPs) within a set of potentially conflicting procedural and declarative constraints, called <em>process framing</em>. In this respect, <em>framed autonomy</em> enables an ABPMS to autonomously decide how to progress the execution of a BP, as long as the boundaries imposed by the frame are respected. Among these constraints, there could be a partial BP execution that needs to be completed, activating a different near-optimal framing that enables the BP to progress its execution. In this paper, we present an <em>automata-based technique</em> that pairs <em>constraint-based framing</em> with <em>automated planning</em> in AI to recommend, given a partial BP execution trace, the continuation of that trace that minimizes the violation cost of the conforming space defined by the process frame. We report on the results of experiments of increasing complexity to showcase our technique’s performance and scalability.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"133 ","pages":"Article 102573"},"PeriodicalIF":3.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ADAPT: Fairness & diversity for sequential group recommendations 适应：公平性和多样性的顺序组的建议

IF 3 2区计算机科学

Information Systems Pub Date : 2025-05-19 DOI: 10.1016/j.is.2025.102572

Emilia Lenzi , Kostas Stefanidis

引用次数: 0

Parameterization-free clustering with sparse data observers 使用稀疏数据观察者的无参数化聚类

IF 3 2区计算机科学

Information Systems Pub Date : 2025-05-19 DOI: 10.1016/j.is.2025.102562

Félix Iglesias Vázquez , Tanja Zseby , Arthur Zimek

{"title":"Parameterization-free clustering with sparse data observers","authors":"Félix Iglesias Vázquez , Tanja Zseby , Arthur Zimek","doi":"10.1016/j.is.2025.102562","DOIUrl":"10.1016/j.is.2025.102562","url":null,"abstract":"<div><div>Given a set of data points, clustering serves to discover groups based on pairwise similarities and the shapes drawn by the data in the feature space. In other words, it is a tool to describe data and reveal their intrinsic nature in terms of patterns or groups. In this paper, we review the methodology of clustering when used to explore a priori unknown data, i.e., we do not know how data spaces are manipulated, how algorithms are tuned, and how results are validated. Under this practical approach, we examine the advantages of SDOclust, a clustering method that stands out for its simplicity, lightness, no need for parameterization and not being subject to traditional clustering limitations. We test SDOclust and main established alternatives — HDBSCAN, <span><math><mi>k</mi></math></span>-means--, Fuzzy C-means, Hierarchical Clustering, CLASSIX, and N2D Deep Clustering — by extensive experimentation with more than 200 datasets, both real and synthetic, that have been collected from the literature on evaluation and represent different data analysis challenges. We submit only SDOclust to unfavorable testing conditions by denying it a parameter tuning phase. Nevertheless, its overall performance is excellent and positions it as one of the best general-purpose alternatives.</div><div>With deep clustering as the consolidation of a new paradigm, trends in clustering consist mainly in projecting data into spaces that are easier to dissect. Therefore, in cases where the original space does not show clustering-friendly structures and when we can assume transformation costs, SDOclust easily adapts and is a most natural choice to perform the partitioning task.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"133 ","pages":"Article 102562"},"PeriodicalIF":3.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Service composition for ltl f task specifications 用于所有任务规范的服务组合

IF 3 2区计算机科学

Information Systems Pub Date : 2025-05-17 DOI: 10.1016/j.is.2025.102571

Giuseppe De Giacomo , Marco Favorito , Luciana Silo

引用次数: 0

SMUTF: Schema Matching Using Generative Tags and Hybrid Features SMUTF：使用生成标签和混合特征的模式匹配

IF 3 2区计算机科学

Information Systems Pub Date : 2025-05-17 DOI: 10.1016/j.is.2025.102570

Yu Zhang , Di Mei , Haozheng Luo , Chenwei Xu , Richard Tzong-Han Tsai

{"title":"SMUTF: Schema Matching Using Generative Tags and Hybrid Features","authors":"Yu Zhang , Di Mei , Haozheng Luo , Chenwei Xu , Richard Tzong-Han Tsai","doi":"10.1016/j.is.2025.102570","DOIUrl":"10.1016/j.is.2025.102570","url":null,"abstract":"<div><div>We introduce <strong>SMUTF</strong> (<strong>S</strong>chema <strong>M</strong>atching <strong>U</strong>sing Generative <strong>T</strong>ags and Hybrid <strong>F</strong>eatures), a unique approach for large-scale tabular data schema matching (SM), which assumes that supervised learning does not affect performance in open-domain tasks, thereby enabling effective cross-domain matching. This system uniquely combines rule-based feature engineering, pre-trained language models, and generative large language models. In an innovative adaptation inspired by the Humanitarian Exchange Language, we deploy ”generative tags” for each data column, enhancing the effectiveness of SM. SMUTF exhibits extensive versatility, working seamlessly with any pre-existing pre-trained embeddings, classification methods, and generative models.</div><div>Recognizing the lack of extensive, publicly available datasets for SM, we have created and open-sourced the HDXSM dataset from the public humanitarian data. We believe this to be the most exhaustive SM dataset currently available. In evaluations across various public datasets and the novel HDXSM dataset, SMUTF demonstrated exceptional performance, surpassing existing state-of-the-art models in terms of accuracy and efficiency, and improving the F1 score by 11.84% and the AUC of ROC by 5.08%. Code is available at <span><span>GitHub</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"133 ","pages":"Article 102570"},"PeriodicalIF":3.0,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy-preserving record linkage using reference set based encoding: A single parameter method 使用基于引用集的编码保护隐私的记录链接：单参数方法

IF 3 2区计算机科学

Information Systems Pub Date : 2025-05-15 DOI: 10.1016/j.is.2025.102569

Sumayya Ziyad , Peter Christen , Anushka Vidanage , Charini Nanayakkara , Rainer Schnell

{"title":"Privacy-preserving record linkage using reference set based encoding: A single parameter method","authors":"Sumayya Ziyad , Peter Christen , Anushka Vidanage , Charini Nanayakkara , Rainer Schnell","doi":"10.1016/j.is.2025.102569","DOIUrl":"10.1016/j.is.2025.102569","url":null,"abstract":"<div><div>Record linkage is the process of matching records that refer to the same entity across two or more databases. In many application areas, ranging from healthcare to government services, the databases to be linked contain sensitive personal information, and hence, cannot be shared across organisations. Privacy-Preserving Record Linkage (PPRL) aims to overcome this challenge by facilitating the comparison of records that have been encoded or encrypted, thereby allowing linkage without the need of sharing any sensitive data. While various PPRL techniques have been developed, most of them do not properly address privacy concerns, such as the various vulnerabilities of encoded data with regard to cryptanalysis attacks. Existing PPRL methods, furthermore, do not provide conceptual analyses of how a user should set the various parameters required, possibly leading to sub-optimal results with regard to both linkage quality and privacy protection. Here we present a <em>novel encoding method for PPRL that employs reference q-gram sets to generate bit arrays that represent sensitive values. Our method requires a single user parameter that determines a trade-off between linkage quality, scalability, and privacy.</em> All other parameters are either data driven or have strong bounds based on the user-set parameter. Furthermore, our method addresses the length, frequency, and pattern-based PPRL vulnerabilities that are exploited by existing PPRL attacks. We conceptually analyse our method and experimentally evaluate it using multiple databases. Our results show that our method provides robust results for both high linkage quality and strong privacy protection.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"133 ","pages":"Article 102569"},"PeriodicalIF":3.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Training-free sparse representations of dense vectors for scalable information retrieval 面向可扩展信息检索的密集向量的无训练稀疏表示

IF 3 2区计算机科学

Information Systems Pub Date : 2025-05-13 DOI: 10.1016/j.is.2025.102567

Fabio Carrara, Lucia Vadicamo, Giuseppe Amato, Claudio Gennaro

{"title":"Training-free sparse representations of dense vectors for scalable information retrieval","authors":"Fabio Carrara, Lucia Vadicamo, Giuseppe Amato, Claudio Gennaro","doi":"10.1016/j.is.2025.102567","DOIUrl":"10.1016/j.is.2025.102567","url":null,"abstract":"<div><div>In this paper, we propose and analyze Vec2Doc, a novel training-free method to transform dense vectors into sparse integer vectors, facilitating the use of inverted indexes for information retrieval (IR). The exponential growth of deep learning and artificial intelligence has revolutionized scientific problem-solving in areas such as computer vision, natural language processing, and automatic content generation. These advances have also significantly impacted IR, with a better understanding of natural language and multimodal content analysis leading to more accurate information retrieval. Despite these developments, modern IR relies primarily on the similarity evaluation of dense vectors from the latent spaces of deep neural networks. This dependence introduces substantial challenges in performing similarity searches on large collections containing billions of vectors. Traditional IR methods, which employ inverted indexes and vector space models, are adept at handling sparse vectors but do not work well with dense ones. Vec2Doc attempts to fill this gap by converting dense vectors into a format compatible with conventional inverted index techniques. Our preliminary experimental evaluations show that Vec2Doc is a promising solution to overcome the scalability problems inherent in vector-based IR, offering an alternative method for efficient and accurate large-scale information retrieval.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"133 ","pages":"Article 102567"},"PeriodicalIF":3.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Finding HSP neighbors via an exact, hierarchical approach 通过精确的分层方法找到HSP邻居

IF 3 2区计算机科学

Information Systems Pub Date : 2025-05-12 DOI: 10.1016/j.is.2025.102565

Cole Foster , Edgar Chávez , Benjamin Kimia

{"title":"Finding HSP neighbors via an exact, hierarchical approach","authors":"Cole Foster , Edgar Chávez , Benjamin Kimia","doi":"10.1016/j.is.2025.102565","DOIUrl":"10.1016/j.is.2025.102565","url":null,"abstract":"<div><div>The Half Space Proximal (HSP) graph is a low out-degree monotonic graph with a wide range of applications in various domains, including combinatorial optimization in strings, enhancing <span><math><mi>k</mi></math></span>NN classification, simplifying chemical networks, estimating local intrinsic dimensionality, and generating uniform samples from skewed distributions, among others. However, the linear complexity of finding HSP neighbors of a query limits its scalability, thus motivating approximate indexing which sacrifices accuracy in favor of restricting the test to a small local neighborhood. This compromise leads to the loss of crucial long-range connections which as a result introduce false positives and exclude false negatives, and compromising some of the essential properties of the HSP. To overcome these limitations, this paper proposes a fast and exact algorithm for computing the HSP which enjoys sublinear complexity as demonstrated by extensive experimentation. Our hierarchical approach leverages the triangle inequality applied to pivots to enable efficient HSP search in metric spaces with the Hilbert Exclusion property. A key component of our approach is the concept of the <em>shifted generalized hyperplane</em> between two points, which allows for the invalidation of entire groups of points. Our approach ensures the computation of the exact HSP with efficiency, even for datasets containing hundreds of millions of points.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"133 ","pages":"Article 102565"},"PeriodicalIF":3.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Alternating Optimization Scheme for Binary Sketches 二元草图的交替优化方案

IF 3 2区计算机科学

Information Systems Pub Date : 2025-05-10 DOI: 10.1016/j.is.2025.102563

Erik Thordsen, Erich Schubert

{"title":"An Alternating Optimization Scheme for Binary Sketches","authors":"Erik Thordsen, Erich Schubert","doi":"10.1016/j.is.2025.102563","DOIUrl":"10.1016/j.is.2025.102563","url":null,"abstract":"<div><div>Searching for similar objects in intrinsically high-dimensional data sets is a challenging task. The use of compact sketches has been proposed for faster similarity search using linear scans. Binary sketches are one such approach to find a good mapping from the original data space to bit strings of a fixed length. These bit strings can be compared efficiently using only few XOR and bit count operations, replacing costly similarity computations with an inexpensive approximation. We propose a new scheme to initialize and improve binary sketches for similarity search in Euclidean spaces. Our optimization iteratively improves the quality of the sketches with a form of orthogonalization. We provide empirical evidence that the quality of the sketches has a peak beyond which it is not correlated to neither bit independence nor bit balance, which contradicts a previous hypothesis in the literature. Regularization in the form of noise added to the training data can turn the peak into a plateau and applying the optimization in a stochastic fashion, i.e., training on smaller subsets of the data, allows for rapid initialization. We provide a loss function that allows to approximate the same objective using neural network frameworks such as PyTorch, elevating the approach to GPU-based training.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"133 ","pages":"Article 102563"},"PeriodicalIF":3.0,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0