Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis最新文献

Learning Permutation-Invariant Embeddings for Description Logic Concepts 描述逻辑概念的置换不变嵌入学习

Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis Pub Date : 2023-03-03 DOI: 10.48550/arXiv.2303.01844

Caglar Demir, A. N. Ngomo

{"title":"Learning Permutation-Invariant Embeddings for Description Logic Concepts","authors":"Caglar Demir, A. N. Ngomo","doi":"10.48550/arXiv.2303.01844","DOIUrl":"https://doi.org/10.48550/arXiv.2303.01844","url":null,"abstract":"Concept learning deals with learning description logic concepts from a background knowledge and input examples. The goal is to learn a concept that covers all positive examples, while not covering any negative examples. This non-trivial task is often formulated as a search problem within an infinite quasi-ordered concept space. Although state-of-the-art models have been successfully applied to tackle this problem, their large-scale applications have been severely hindered due to their excessive exploration incurring impractical runtimes. Here, we propose a remedy for this limitation. We reformulate the learning problem as a multi-label classification problem and propose a neural embedding model (NERO) that learns permutation-invariant embeddings for sets of examples tailored towards predicting $F_1$ scores of pre-selected description logic concepts. By ranking such concepts in descending order of predicted scores, a possible goal concept can be detected within few retrieval operations, i.e., no excessive exploration. Importantly, top-ranked concepts can be used to start the search procedure of state-of-the-art symbolic models in multiple advantageous regions of a concept space, rather than starting it in the most general concept $top$. Our experiments on 5 benchmark datasets with 770 learning problems firmly suggest that NERO significantly (p-value<1%) outperforms the state-of-the-art models in terms of $F_1$ score, the number of explored concepts, and the total runtime. We provide an open-source implementation of our approach.","PeriodicalId":91439,"journal":{"name":"Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis","volume":"9 1","pages":"103-115"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84515096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Effects of Locality and Rule Language on Explanations for Knowledge Graph Embeddings 局部性和规则语言对知识图嵌入解释的影响

Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis Pub Date : 2023-02-14 DOI: 10.48550/arXiv.2302.06967

Luis Galárraga

{"title":"Effects of Locality and Rule Language on Explanations for Knowledge Graph Embeddings","authors":"Luis Galárraga","doi":"10.48550/arXiv.2302.06967","DOIUrl":"https://doi.org/10.48550/arXiv.2302.06967","url":null,"abstract":"Knowledge graphs (KGs) are key tools in many AI-related tasks such as reasoning or question answering. This has, in turn, propelled research in link prediction in KGs, the task of predicting missing relationships from the available knowledge. Solutions based on KG embeddings have shown promising results in this matter. On the downside, these approaches are usually unable to explain their predictions. While some works have proposed to compute post-hoc rule explanations for embedding-based link predictors, these efforts have mostly resorted to rules with unbounded atoms, e.g., bornIn(x,y) =>residence(x,y), learned on a global scope, i.e., the entire KG. None of these works has considered the impact of rules with bounded atoms such as nationality(x,England) =>speaks(x, English), or the impact of learning from regions of the KG, i.e., local scopes. We therefore study the effects of these factors on the quality of rule-based explanations for embedding-based link predictors. Our results suggest that more specific rules and local scopes can improve the accuracy of the explanations. Moreover, these rules can provide further insights about the inner-workings of KG embeddings for link prediction.","PeriodicalId":91439,"journal":{"name":"Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis","volume":"415 1","pages":"143-155"},"PeriodicalIF":0.0,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79429056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transferable Deep Metric Learning for Clustering 用于聚类的可转移深度度量学习

Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis Pub Date : 2023-02-13 DOI: 10.48550/arXiv.2302.06523

C. SimoAlami, Rim Kaddah, J. Read

引用次数: 0

Revised Conditional t-SNE: Looking Beyond the Nearest Neighbors 修订条件t-SNE:超越近邻

Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis Pub Date : 2023-02-07 DOI: 10.1007/978-3-031-30047-9_14

Edith Heiter, Bo Kang, R. Seurinck, Jefrey Lijffijt

引用次数: 1

Shapley Values with Uncertain Value Functions 具有不确定值函数的Shapley值

Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis Pub Date : 2023-01-19 DOI: 10.1007/978-3-031-30047-9_13

R. Heese, Sascha Mücke, Matthias Jakobs, Thore Gerlach, N. Piatkowski

引用次数: 3

APs: A Proxemic Framework for Social Media Interactions Modeling and Analysis ap:社交媒体互动建模与分析的本体框架

Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-30047-9_23

Maxime Masson, P. Roose, C. Sallaberry, R. Agerri, M. Bessagnet, A. L. Parc-Lacayrelle

引用次数: 0

Discovering Diverse Top-K Characteristic Lists 发现不同的Top-K特征列表

Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-30047-9_21

Antonio Lopez-Martinez-Carrasco, Hugo Manuel Proença, J. Juarez, M. Leeuwen, M. Campos

引用次数: 0

ROCKAD: Transferring ROCKET to Whole Time Series Anomaly Detection 将火箭转移到全时间序列异常检测

Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-30047-9_33

Andreas Theissler, Manuel Wengert, Felix Gerschner

引用次数: 0

On Compositionality in Data Embedding 关于数据嵌入中的组合性

Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-30047-9_38

Zhaozhen Xu, Zhijin Guo, N. Cristianini

引用次数: 1

User Authentication via Multifaceted Mouse Movements and Outlier Exposure 用户身份验证通过多方面的鼠标移动和异常暴露

Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-30047-9_24

J. Matthiesen, Hanne Hastedt, Ulf Brefeld

引用次数: 0