Knowledge Graph Query Embedding (KGQE) aims to embed First-Order Logic (FOL) queries in a low-dimensional KG space for complex reasoning over incomplete KGs. To enhance the generalization of KGQE models, recent studies integrate various external information (such as entity types and relation context) to better capture the logical semantics of FOL queries. The whole process is commonly referred to as Query Pattern Learning (QPL). However, current QPL methods typically suffer from the pattern-entity alignment bias problem, leading to the learned defective query patterns limiting KGQE models' performance. To address this problem, we propose an effective Query Instruction Parsing Plugin (QIPP) that leverages the context awareness of Pre-trained Language Models (PLMs) to capture latent query patterns from code-like query instructions. Unlike the external information introduced by previous QPL methods, we first propose code-like instructions to express FOL queries in an alternative format. This format utilizes textual variables and nested tuples to convey the logical semantics within FOL queries, serving as raw materials for a PLM-based instruction encoder to obtain complete query patterns. Building on this, we design a query-guided instruction decoder to adapt query patterns to KGQE models. To further enhance QIPP's effectiveness across various KGQE models, we propose a query pattern injection mechanism based on compressed optimization boundaries and an adaptive normalization component, allowing KGQE models to utilize query patterns more efficiently. Extensive experiments demonstrate that our plug-and-play method improves the performance of eight basic KGQE models and outperforms two state-of-the-art QPL methods.

Due to the presence of noise in crowdsourced labels, label aggregation (LA) has become a standard procedure for post-processing these labels. LA methods estimate true labels from crowdsourced labels by modeling worker quality. However, most existing LA methods are iterative in nature. They require multiple passes through all crowdsourced labels, jointly and iteratively updating true labels and worker qualities until a termination condition is met. As a result, these methods are burdened with high space and time complexities, which restrict their applicability in scenarios where scalability and online aggregation are essential. Furthermore, defining a suitable termination condition for iterative algorithms can be challenging. In this article, we view LA as a dynamic system and represent it as a Dynamic Bayesian Network. From this dynamic model, we derive two lightweight and scalable algorithms: LAonepassand LAtwopass. These algorithms can efficiently and effectively estimate worker qualities and true labels by traversing all labels at most twice, thereby eliminating the need for explicit termination conditions and multiple traversals over the crowdsourced labels. Due to their dynamic nature, the proposed algorithms are also capable of performing label aggregation online. We provide theoretical proof of the convergence property of the proposed algorithms and bound the error of the estimated worker qualities. Furthermore, we analyze the space and time complexities of our proposed algorithms, demonstrating their equivalence to those of majority voting. Through experiments conducted on 20 real-world datasets, we demonstrate that our proposed algorithms can effectively and efficiently aggregate labels in both offline and online settings, even though they traverse all labels at most twice. The code is on https://github.com/yyang318/LA_onepass .

Relation reasoning inKnowledgeGraphCompletion (KGC) aims at predicting missing relations between entities. Recently, effective KGC methods have usually focused on exploring the path pattern between entities, such as reward-based path walking and path context mining, to complete target relations. However, these methods typically suffer from two challenges: 1) They have difficulty in handling the individual representation limitation of candidate paths when there are no paths that directly represent latent relations between entities; 2) They overlook the biases of path context induction, which leads to unreasonable information interfering with the model's reasoning. To manage these challenges, aGeometric-ContextualMutualInfomax (GCMI) path aggregator is proposed for relation reasoning. First, we design an attentive path aggregator with a shared Transformer encoder to capture the contexts from several candidate paths parallelly and integrate these contexts to sufficiently represent the latent relations of each entity pair for reasoning. Then, the GCMI modules are proposed to constrain the local and global biases of path context induction in the Transformer encoder and the path aggregator, respectively, by a straightforward geometric rule. Extensive experiments on 32 real-world relation reasoning tasks demonstrate that our method significantly outperforms 8 state-of-the-art baselines in terms of AP and AUC.

A Session-Based Recommendation (SBR) seeks to predict users’ future item preferences by analyzing their interactions with previously clicked items. In recent approaches, Graph Neural Networks (GNNs) have been commonly applied to capture item relations within a session to infer user intentions. However, these GNN-based methods typically struggle with feature ambiguity between the sequential session information and the item conversion within an item graph, which may impede the model’s ability to accurately infer user intentions. In this article, we propose a novel Multi-hop Multi-view Memory Transformer (M 3 T) to effectively integrate the sequence-view information and relation conversion (graph-view information) of items in a session. First, we propose a Multi-view Memory Transformer (M 2 T) module to concurrently obtain multi-view information of items. Then, a set of trainable memory matrices are employed to store sharable item features, which mitigates cross-view item feature ambiguity. To comprehensively capture latent user intentions, an M 3 T framework is designed to integrate user intentions across different hops of an item graph. Specifically, a k-order power method is proposed to manage the item graph to alleviate the over-smoothing problem when obtaining high-order relations of items. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our method.

Micro-video recommendation has attracted extensive research attention with the increasing popularity of micro-video sharing platforms. Recently, graph contrastive learning (GCL) is adopted for enhancing the performance of graph neural network based micro-video recommendation. However, these GCL methods may suffer from the following problems: (1) they fail to fully exploit the potential of contrastive learning for ignoring or misjudging highly similar samples, and (2) the complementary recommendation effects between graph structure information and multi-modal feature information are not effectively utilized. In this paper, we propose a novel Cross-View Sample-Enriched Graph Contrastive Learning Network (CSGCL) for micro-video recommendation. Specifically, we build a collaborative learning view and a semantic learning view to learn node representations. For the collaborative learning view, we leverage similar nodes at the structure level to construct an effective collaborative contrastive objective. For the semantic learning view, we derive the k-nearest neighbor graph generated from multi-modal features as the semantic graphs and build a semantic contrastive objective for learning high-quality micro-video representations. Finally, a cross-view contrastive objective is designed to consider the mutually complementary recommendation effects by maximizing the agreement between the two above views. Extensive experiments on three real-world datasets demonstrate that the proposed model outperforms the baselines.

Supervised Meta-event extraction suffers from two limitations: (1) The extracted meta-events only contain local semantic information and do not present the core content of the text; (2) model performance is easily degraded because of labeled samples with insufficient number and poor quality. To overcome these limitations, this study presents an approach called frame-incorporated semi-supervised topic event extraction (FISTEE), which aims to extract topic events containing global semantic information. Inspired by the frame-based knowledge representation, a topic event frame is developed to integrate multiple meta-events into a topic event. Combined with the tri-training algorithm, a strategy for selecting unlabeled samples is designed to expand the training sets, and labeling models based on conditional random field (CRF) are constructed to label meta-events. The experimental results show that the event extraction performance of FISTEE is better than supervised learning-based approaches. Furthermore, the extracted topic events can present the core content of the text.

Crowdsourcing truth inference aims to assign a correct answer to each task from candidate answers that are provided by crowdsourced workers. A common approach is to generate workers’ reliabilities to represent the quality of answers. Although crowdsourced triples can be converted into various crowdsourced relationships, the available related methods are not effective in capturing these relationships to alleviate the harm to inference that is caused by conflicting answers. In this research, we propose aReliability-drivenMulti-viewGraphEmbedding framework forTruthinference (TiReMGE), which explores multiple crowdsourced relationships by organically integrating worker reliabilities into a graph space that is constructed from crowdsourced triples. Specifically, to create an interactive environment, we propose a reliability-driven initialization criterion for initializing vectors of tasks and workers as interactive carriers of reliabilities. From the perspective of multiple crowdsourced relationships, a multi-view graph embedding framework is proposed for reliability information interaction on a task-worker graph, which encodes latent crowdsourced relationships into vectors of workers and tasks for reliability update and truth inference. A heritable reliability updating method based on the Lagrange multiplier method is proposed to obtain reliabilities that match the quality of workers for interaction by a novel constraint law. Our ultimate goal is to minimize the Euclidean distance between the encoded task vector and the answer that is provided by a worker with high reliability. Extensive experimental results on nine real-world datasets demonstrate that TiReMGE significantly outperforms the nine state-of-the-art baselines.

Truth inference can help solve some difficult problems of data integration in crowdsourcing. Crowdsourced workers are not experts and their labeling ability varies greatly; therefore, in practical applications, it is difficult to determine whether the labels collected from a crowdsourcing platform are correct. This article proposes a novel algorithm called truth inference based on label confidence clustering (TILCC) to improve the quality of integrated labels for the single-choice classification problem in crowdsourcing labeling tasks. We obtain the label confidence via worker reliability, which is calculated from multiple noise labels using a truth discovery method, and then we generate the clustering features and use the K-means algorithm to cluster all the tasks into K different clusters. Each cluster corresponds to a specific class, and the tasks in the cluster are assigned a label. Compared with the performances of six state-of-the-art methods, MV, ZenCrowd, PM, CATD, GLAD, and GTIC, on 12 randomly selected real-world datasets, the performance of our algorithm showed many advantages: no need to set complex parameters, faster running speed, and significantly higher accuracy.

In multi-label learning, each instance is associated with multiple labels simultaneously. Multi-label data often have noisy, irrelevant, and redundant features of high dimensionality. Multi-label feature selection has received considerable attention as an effective means for dealing with high-dimensional multi-label data. Many multi-label feature selection methods exploit label correlations to help select features. However, finding label correlations and selecting features in existing multi-label feature selection methods are often two separate processes, the existence of noises and outliers in training data makes the label correlations exploited from label space less reliable. Therefore, the learned label correlations may mislead the feature selection process and result in the selection of less informative features. This article proposes a novel algorithm named ROAD, i.e., multi-label featuRe selectiOn via ADaptive label correlation estimation. ROAD jointly performs adaptive label correlation exploration and feature selection with alternating optimization to obtain reliable estimation of label correlations, which can more effectively reveal the intrinsic manifold structure among labels and lead to the selection of a more proper feature subset. Comprehensive experiments on several frequently used datasets validate the superiority of ROAD against the state-of-the-art multi-label feature selection algorithms.

Crowdsourcing platforms collect massive dirty claims that are provided by sources for crowdsourced objects, which prompts truth inference to be proposed for crowdsourcing data denoising. Although current graph-based truth-inference methods achieve remarkable success by capturing complex crowdsourcing relationships, they typically suffer from two challenges: 1) They fail to obtain complete crowdsourcing relationships because of the structural limitations of crowdsourcing relationship graphs; 2) Their vector initialization methods for objects and sources are disturbed by claim noise, which limits them from obtaining correct object and source semantics. To cope with these challenges, we propose a novelTruth-Inference method viaReliabilityAggregation (TIRA) on an object-source graph. Specifically, we propose a hierarchical graph auto-encoder to adapt to a reasonable object-source graph, which enables TIRA to capture complete crowdsourcing relationships from multiple perspectives. To better guide TIRA, we design a vector initialization method based on source reliabilities to map the denoised claims to a representation space of objects and sources. Finally, TIRA aggregates the reliability information on an object-source graph to generate object embeddings for truth inference. We conducted extensive experiments on 12 real-world datasets. The experimental results demonstrate that our method significantly outperforms 12 state-of-the-art baselines in terms of the$accuracy$and$weighted\_{F}1$.

The research on community structure is a key to analyze the network functionality and topology, and thus it is significant to detect and analysis the community structure. During the abstract process from an actual system to a network, especially for a large-scale network, it is inevitable to have mistaken connections between nodes or have connection missing. In addition, in real applications, from time to time we can obtain prior information in the form of pairwise constraints between nodes besides topology information, although they may be inaccurate or conflicted. These noises in the network-related information will dramatically reduce the accuracy of community detection. Hence, in this paper, we introduce a dissimilarity index to determine the trustworthiness of pairwise constraints and settle the conflict of pairwise constraints. Then, focusing on the community detection with false connections or conflicted connections, we propose a pairwise constrained structure-enhanced extremal optimization-based semi-supervised algorithm (PCSEO-SS algorithm). Compared with existing semi-supervised community detection approaches, the experimental results executed on real networks and synthetic networks, show that PCSEO-SS can solve the problem of false connections or conflicted connections to some extent and detect the community structure more precisely.

Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.

In addition to the news content, most web news pages also contain navigation panels, advertisements, related news links etc. These non-news items not only exist outside the news region, but are also present in the news content region. Effectively extracting the news content and filtering the noise have important effects on the follow-up activities of content management and analysis. Our extensive case studies have indicated that there exists potential relevance between web content layouts and their tag paths. Based on this observation, we design two tag path features to measure the importance of nodes: Text to tag Path Ratio (TPR) and Extended Text to tag Path Ratio (ETPR), and describe the calculation process of TPR by traversing the parsing tree of a web news page. In this paper, we present Content Extraction via Path Ratios (CEPR) - a fast, accurate and general on-line method for distinguishing news content from non-news content by the TPR/ETPR histogram effectively. In order to improve the ability of CEPR in extracting short texts, we propose a Gaussian smoothing method weighted by a tag path edit distance. This approach can enhance the importance of internal-link nodes but ignore noise nodes existing in news content. Experimental results on the CleanEval datasets and web news pages randomly selected from well-known websites show that CEPR can extract across multi-resources, multi-styles, and multi-languages. The average F and average score with CEPR is 8.69% and 14.25% higher than CETR, which demonstrates better web news extraction performance than most existing methods.

How to accurately extract the content of Web news is a popular and significant issue in Web Intelligence. Many Web news sites have similar structures and layout styles, and there are potential correlations between Web content layouts and tag path patterns. Compared with other extraction features, such as HTML tags, literal words and visual features, a tag path pattern not only addresses content segments well, but also has an advantage in the generalization. However, can we accurately extract Web news using only tag path patterns? Motivated by this problem, we propose a PPWIE extraction model. We design an extraction algorithm WEtr using self-defined tag path patterns, and then define a special tag path pattern called the distinguishing tag path pattern. In addition, to tackle the NPC-hard problem in path pattern mining, we propose a polynomial-time (ln|n|+1)-approximation algorithm MPM, in which n indicates the scale of positive samples. Our experiments show that our integration method WEtr+MPM in PPWIE can achieve better performance with more than 98% of precision, recall and the F-score on real world datasets.