7,654 research outputs found

    Transforming Graph Representations for Statistical Relational Learning

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    Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

    Vermeidung von ReprÀsentationsheterogenitÀten in realweltlichen Wissensgraphen

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    Knowledge graphs are repositories providing factual knowledge about entities. They are a great source of knowledge to support modern AI applications for Web search, question answering, digital assistants, and online shopping. The advantages of machine learning techniques and the Web's growth have led to colossal knowledge graphs with billions of facts about hundreds of millions of entities collected from a large variety of sources. While integrating independent knowledge sources promises rich information, it inherently leads to heterogeneities in representation due to a large variety of different conceptualizations. Thus, real-world knowledge graphs are threatened in their overall utility. Due to their sheer size, they are hardly manually curatable anymore. Automatic and semi-automatic methods are needed to cope with these vast knowledge repositories. We first address the general topic of representation heterogeneity by surveying the problem throughout various data-intensive fields: databases, ontologies, and knowledge graphs. Different techniques for automatically resolving heterogeneity issues are presented and discussed, while several open problems are identified. Next, we focus on entity heterogeneity. We show that automatic matching techniques may run into quality problems when working in a multi-knowledge graph scenario due to incorrect transitive identity links. We present four techniques that can be used to improve the quality of arbitrary entity matching tools significantly. Concerning relation heterogeneity, we show that synonymous relations in knowledge graphs pose several difficulties in querying. Therefore, we resolve these heterogeneities with knowledge graph embeddings and by Horn rule mining. All methods detect synonymous relations in knowledge graphs with high quality. Furthermore, we present a novel technique for avoiding heterogeneity issues at query time using implicit knowledge storage. We show that large neural language models are a valuable source of knowledge that is queried similarly to knowledge graphs already solving several heterogeneity issues internally.Wissensgraphen sind eine wichtige Datenquelle von EntitĂ€tswissen. Sie unterstĂŒtzen viele moderne KI-Anwendungen. Dazu gehören unter anderem Websuche, die automatische Beantwortung von Fragen, digitale Assistenten und Online-Shopping. Neue Errungenschaften im maschinellen Lernen und das außerordentliche Wachstum des Internets haben zu riesigen Wissensgraphen gefĂŒhrt. Diese umfassen hĂ€ufig Milliarden von Fakten ĂŒber Hunderte von Millionen von EntitĂ€ten; hĂ€ufig aus vielen verschiedenen Quellen. WĂ€hrend die Integration unabhĂ€ngiger Wissensquellen zu einer großen Informationsvielfalt fĂŒhren kann, fĂŒhrt sie inhĂ€rent zu HeterogenitĂ€ten in der WissensreprĂ€sentation. Diese HeterogenitĂ€t in den Daten gefĂ€hrdet den praktischen Nutzen der Wissensgraphen. Durch ihre GrĂ¶ĂŸe lassen sich die Wissensgraphen allerdings nicht mehr manuell bereinigen. DafĂŒr werden heutzutage hĂ€ufig automatische und halbautomatische Methoden benötigt. In dieser Arbeit befassen wir uns mit dem Thema ReprĂ€sentationsheterogenitĂ€t. Wir klassifizieren HeterogenitĂ€t entlang verschiedener Dimensionen und erlĂ€utern HeterogenitĂ€tsprobleme in Datenbanken, Ontologien und Wissensgraphen. Weiterhin geben wir einen knappen Überblick ĂŒber verschiedene Techniken zur automatischen Lösung von HeterogenitĂ€tsproblemen. Im nĂ€chsten Kapitel beschĂ€ftigen wir uns mit EntitĂ€tsheterogenitĂ€t. Wir zeigen Probleme auf, die in einem Multi-Wissensgraphen-Szenario aufgrund von fehlerhaften transitiven Links entstehen. Um diese Probleme zu lösen stellen wir vier Techniken vor, mit denen sich die QualitĂ€t beliebiger Entity-Alignment-Tools deutlich verbessern lĂ€sst. Wir zeigen, dass RelationsheterogenitĂ€t in Wissensgraphen zu Problemen bei der Anfragenbeantwortung fĂŒhren kann. Daher entwickeln wir verschiedene Methoden um synonyme Relationen zu finden. Eine der Methoden arbeitet mit hochdimensionalen Wissensgrapheinbettungen, die andere mit einem Rule Mining Ansatz. Beide Methoden können synonyme Relationen in Wissensgraphen mit hoher QualitĂ€t erkennen. DarĂŒber hinaus stellen wir eine neuartige Technik zur Vermeidung von HeterogenitĂ€tsproblemen vor, bei der wir eine implizite WissensreprĂ€sentation verwenden. Wir zeigen, dass große neuronale Sprachmodelle eine wertvolle Wissensquelle sind, die Ă€hnlich wie Wissensgraphen angefragt werden können. Im Sprachmodell selbst werden bereits viele der HeterogenitĂ€tsprobleme aufgelöst, so dass eine Anfrage heterogener Wissensgraphen möglich wird

    A Comprehensive Survey on Rare Event Prediction

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    Rare event prediction involves identifying and forecasting events with a low probability using machine learning and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it requires using specialized methods within each step of the machine learning pipeline, i.e., from data processing to algorithms to evaluation protocols. Predicting the occurrences of rare events is important for real-world applications, such as Industry 4.0, and is an active research area in statistical and machine learning. This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, data processing, algorithmic approaches, and evaluation approaches. Specifically, we consider 73 datasets from different modalities (i.e., numerical, image, text, and audio), four major categories of data processing, five major algorithmic groupings, and two broader evaluation approaches. This paper aims to identify gaps in the current literature and highlight the challenges of predicting rare events. It also suggests potential research directions, which can help guide practitioners and researchers.Comment: 44 page

    Generating Knowledge in Maintenance from Experience Feedback

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    Knowledge is nowadays considered as a significant source of performance improvement, but may be difficult to identify, structure, analyse and reuse properly. A possible source of knowledge is in the data and information stored in various modules of industrial information systems, like CMMS (Computerized Maintenance Management Systems) for maintenance. In that context, the main objective of this paper is to propose a framework allowing to manage and generate knowledge from information on past experiences, for improving the decisions related to the maintenance activity. In that purpose, we suggest an original Experience Feedback process dedicated to maintenance, allowing to capitalize on past interventions by i) formalizing the domain knowledge and experiences using a visual knowledge representation formalism with logical foundation (Conceptual Graphs); ii) extracting new knowledge thanks to association rules mining algorithms, using an innovative interactive approach; iii) interpreting and evaluating this new knowledge thanks to the reasoning operations of Conceptual Graphs. The suggested method is illustrated on a case study based on real data dealing with the maintenance of overhead cranes

    Fuzzy-Granular Based Data Mining for Effective Decision Support in Biomedical Applications

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    Due to complexity of biomedical problems, adaptive and intelligent knowledge discovery and data mining systems are highly needed to help humans to understand the inherent mechanism of diseases. For biomedical classification problems, typically it is impossible to build a perfect classifier with 100% prediction accuracy. Hence a more realistic target is to build an effective Decision Support System (DSS). In this dissertation, a novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, is proposed to build such a DSS for binary classification problems in the biomedical domain. Empirical studies show that FARM-DS is competitive to state-of-the-art classifiers in terms of prediction accuracy. More importantly, FARs can provide strong decision support on disease diagnoses due to their easy interpretability. This dissertation also proposes a fuzzy-granular method to select informative and discriminative genes from huge microarray gene expression data. With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers can be modeled. Empirical studies show that the proposed method is more accurate than traditional algorithms for cancer classification. And hence we expect that genes being selected can be more helpful for further biological studies
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