Entity Matching (EM) is a fundamental task in data integration, traditionally studied over structured data such as tables and knowledge graphs. In modern repositories, real-world objects are often represented across multiple modalities, including structured entities with symbolic attributes and visual entities in image-centric collections. This motivates cross-modal entity matching, which aims to identify visual-structured entity pairs that refer to the same object. Existing methods typically rely on pretrained vision-language models to compute entity pair similarity and derive correspondence via local ranking, which, however, can be unreliable given noisy and ambiguous cross-modal data and may produce globally inconsistent correspondences across related entities. Multimodal large language models (MLLMs) offer richer cross-modal cues for matching, but exhaustive MLLM reasoning over large candidate spaces is prohibitively expensive. To address these limitations, in this work, we propose øurq, a blackboard-based multi-agent framework for zero-shot cross-modal entity matching that performs iterative, self-correcting refinement by fusing multiple matching signals, explicitly regulating global consistency, and selectively invoking an MLLM only for hard cases. We further construct two new benchmarks from real-world visual and structured data. Extensive experiments show that øurq consistently outperforms competitive baselines, providing an effective solution for zero-shot cross-modal entity matching. We release our data and code at https://github.com/Q-17/CoMCo.

Knowledge Graph Alignment (KGA) identifies elements in different knowledge graphs (KGs) that refer to the same real-world object. It is a key step towards improving the coverage of KGs, which in turn can better facilitate downstream tasks. However, there is currently no interactive tool to support KGA research, particularly for visualizing alignment results, hence limiting the understanding of the procedure and also the development of more advanced solutions. To fill in this gap, in this paper, we introduce IKGA, an interactive visualization tool for KGA, which visualizes the alignment process by integrating various algorithms of representation learning and alignment inference-two key steps in KGA. The system allows users to observe KGA results, compare existing algorithms, and apply to domain-specific KGs at hand.

Multi-modal Entity Alignment (MMEA) aims to identify equivalent entities across different multi-modal knowledge graphs (MMKGs), facilitating their integration and enhancing coverage. However, current MMEA datasets have limitations, including low entity coverage, a single image per entity, high inter-image correlation, and images sourced from the same search engine, which do not reflect real-world challenges. The fair comparison and development of alignment solutions may be hindered by these oversimplified scenarios. To address this problem, in this work, we first construct M3, an MMEA benchmark equipped with multiple images from different search engines in real-world scenarios. Additionally, we design a simple and universal multi-image processing module (AMIA), which assigns varying attention weights to images associated with entities to effectively model visual information. Experimental results validate the difficulty of M3, as well as the effectiveness of AMIA. Despite the superior performance of AMIA, there is still room for developing more advanced solutions to address these difficulties. Our dataset is publicly released.

Dynamic graphs are ubiquitous in our lives, yet they are also susceptible to the risks imposed by malicious activities. However, identifying anomalies in these dynamic graphs presents a challenging task due to the complex graph structures. Existing methods for dynamic anomaly detection primarily focus on learning representations for each timestamp and using sequence modeling techniques to capture and model the temporal information. Despite extensive research, dynamic anomaly detection still faces two key challenges. First, existing methods are limited in effectively using fine-grained temporal information. Second, they have limited generalization capabilities under unsupervised settings. Overcoming these challenges is crucial for advances in dynamic anomaly detection. In this paper, we propose a novel unsupervised anomaly detection method for dynamic graphs. Our approach leverages complex temporal information through fine-grained sampling and embedding modules. Additionally, we introduce an attention alignment strategy to minimize discrepancies in contextual attention between source and target nodes. Through a comprehensive evaluation, we demonstrate that our strategy effectively mitigates overfitting and improves generalization. Experiments on ten dynamic graph datasets validate the effectiveness of our proposed method in detecting anomalies.

Network dismantling is a process of identifying influential nodes that can decompose a network into disconnected sub-networks. This provides a novel approach to understanding and analyzing complex networks abstracted from the real world. State-of-the-art solutions for this task exploit graph encoders to capture the structural features of the network, which are then sent to the multi-layer perceptron for predicting the node importance. This process, however, fails to exploit the interactions among the graph representations learned from different views and neglects the neighboring information when evaluating node importance. In this work, we address these issues with a graph contrastive learning framework with multi-hop aggregation, resulting in the identification of influential nodes. Firstly, we construct role graphs to provide a holistic view of the original graphs. Secondly, graph representations are obtained in the individual views, and enhanced expressiveness is achieved through contrastive learning. Finally, based on the representations, the multi-hop neighbor information of the nodes is aggregated to rank the node importance, and thus aid in the identification of important nodes. We evaluate our proposal on real and synthetic networks, and the results show that our method outperforms the baseline with fewer nodes required to disassemble a network.

Heterogeneous information networks (HINs) are a key resource in many domain-specific retrieval and recommendation scenarios and in conversational environments. Current approaches to mining graph data often rely on abundant supervised information. However, supervised signals for graph learning tend to be scarce for a new task and only a handful of labeled nodes may be available. Meta-learning mechanisms are able to harness prior knowledge that can be adapted to new tasks. In this article, we design meta-learning framework for heterogeneous information networks ( META-HIN ), for few-shot learning problems on HINs. To the best of our knowledge, we are among the first to design a unified framework to realize the few-shot learning of HINs and facilitate different downstream tasks across different domains of graphs. Unlike most previous models, which focus on a single task on a single graph, META-HIN is able to deal with different tasks (node classification, link prediction, and anomaly detection are used as examples) across multiple graphs. Subgraphs are sampled to build the support and query set. Before being processed by the meta-learning module, subgraphs are modeled via a structure module to capture structural features. Then, a heterogeneous Graph Neural Network module is used as the base model to express the features of subgraphs. We also design a Generative Adversarial Network-based contrastive learning module that is able to exploit unsupervised information of the subgraphs. In our experiments, we fuse several datasets from multiple domains to verify META-HIN ’s broad applicability in a multiple-graph scenario. META-HIN consistently and significantly outperforms state-of-the-art alternatives on every task and across all datasets that we consider.

Multimodal Named Entity Recognition (MNER) aims to combine data from different modalities (e.g. text, images, videos, etc.) for recognition and classification of named entities, which is crucial for constructing Multimodal Knowledge Graphs (MMKGs). However, existing researches suffer from two prominant issues: over-reliance on textual features while neglecting visual features, and the lack of effective reduction of the feature space discrepancy of multimodal data. To overcome these challenges, this paper proposes a Multi-Grained Interaction Contrastive Learning framework for MNER task, namely MGICL. MGICL slices data into different granularities, i.e., sentence level/word token level for text, and image level/object level for image. By utilizing multimodal features with different granularities, the framework enables cross-contrast and narrows down the feature space discrepancy between modalities. Moreover, it facilitates the acquisition of valuable visual features by the text. Additionally, a visual gate control mechanism is introduced to dynamically select relevant visual information, thereby reducing the impact of visual noise. Experimental results demonstrate that the proposed MGICL framework satisfactorily tackles the challenges of MNER through enhancing information interaction of multimodal data and reducing the effect of noise, and hence, effectively improves the performance of MNER.

Relation classification detects the semantic relation between two annotated entities from a piece of text, which is a useful tool for structurization of knowledge. Recently, federated learning has been introduced to train relation classification models in decentralized settings. Current methods strive for a strong server model by decoupling the model training at server from direct access to texts at clients while taking advantage of them. Nevertheless, they overlook the fact that clients have heterogeneous texts (i.e., texts with diversely skewed distribution of relations), which renders existing methods less practical. In this paper, we propose to investigate personalized federated relation classification, in which strong client models adapted to their own data are desired. To further meet the challenges brought by heterogeneous texts, we present a novel framework, namely pf-RC, with several optimized designs. It features a knowledge aggregation method that exploits a relation-wise weighting mechanism, and a feature augmentation method that leverages prototypes to adaptively enhance the representations of instances of long-tail relations. We experimentally validate the superiority of pf-RC against competing baselines in various settings, and the results suggest that the tailored techniques mitigate the challenges.

Graph self-supervised learning aims to mine useful information from unlabeled graph data, and has been successfully applied to pre-train graph representations. Many existing approaches use contrastive learning to learn powerful embeddings by learning contrastively from two augmented graph views. However, none of these graph contrastive methods fully exploits the diversity of different augmentations, and hence is prone to overfitting and limited generalization ability of learned representations. In this paper, we propose a novel Graph Self-supervised Learning method with Augmentation-aware Contrastive Learning. Our method is based on the finding that the pre-trained model after adding augmentation diversity can achieve better generalization ability. To make full use of the information from the diverse augmentation method, this paper constructs new augmentation-aware prediction task which complementary with the contrastive learning task. Similar to how pre-training requires fast adaptation to different downstream tasks, we simulate train-test adaptation on the constructed tasks for further enhancing the learning ability; this strategy can be deemed as a form of meta-learning. Experimental results show that our method outperforms previous methods and learns better representations for a variety of downstream tasks.

Node importance estimation is a fundamental task in graph analysis, which can be applied to various downstream applications such as recommendation and resource allocation. However, existing studies merely work under a single view, which neglects the rich information hidden in other aspects of the graph. Hence, in this work, we propose a Multiview Contrastive Representation Learning (MCRL) model to obtain representations of nodes from multiple perspectives and then infer the node importance. Specifically, we are the first to apply the contrastive learning technique to the node importance analysis task, which enhances the expressiveness of graph representations and lays the foundation for importance estimation. Moreover, based on the improved representations, we generate the entity importance score by attentively aggregating the scores from two different views, i.e., node view and node‐edge interaction view. We conduct extensive experiments on real‐world datasets, and the experimental results show that MCRL outperforms existing methods on all evaluation metrics.

In network representation learning we learn how to represent heterogeneous information networks in a low-dimensional space so as to facilitate effective search, classification, and prediction solutions. Previous network representation learning methods typically require sufficient task-specific labeled data to address domain-specific problems. The trained model usually cannot be transferred to out-of-domain datasets. We propose a self-supervised pre-training and fine-tuning framework, PF-HIN, to capture the features of a heterogeneous information network. Unlike traditional network representation learning models that have to train the entire model all over again for every downstream task and dataset, PF-HIN only needs to fine-tune the model and a small number of extra task-specific parameters, thus improving model efficiency and effectiveness. During pre-training, we first transform the neighborhood of a given node into a sequence. PF-HIN is pre-trained based on two self-supervised tasks, masked node modeling and adjacent node prediction. We adopt deep bi-directional transformer encoders to train the model, and leverage factorized embedding parameterization and cross-layer parameter sharing to reduce the parameters. In the fine-tuning stage, we choose four benchmark downstream tasks, i.e., link prediction, similarity search, node classification, and node clustering. PF-HIN outperforms state-of-the-art alternatives on each of these tasks, on four datasets.

Few-shot Relational Triple Extraction (RTE) aims at detecting emerging relation types along with their entity pairs from unstructured text with the support of a few labeled samples. Prior arts use conditional random field or nearest-neighbor matching strategy to extract entities and use prototypical networks for extracting relations from sentences. Nevertheless, they fail to utilize the triple-level information to verify the plausibility of extracted relational triples, and ignore the proper transfer among the perspectives of entity, relation and triple. To fill in these gaps, in this work, we put forward a novel perspective transfer network (PTN) to address few-shot RTE. Specifically, PTN starts from the relation perspective by checking the existence of a given relation. Then, it transfers to the entity perspective to locate entity spans with relation-specific support sets. Next, it transfers to the triple perspective to validate the plausibility of extracted relational triples. Finally, it transfers back to the relation perspective to check the next relation, and repeats the aforementioned procedure. By transferring among the perspectives of relation, entity, and triple, PTN not only validates the extracted elements at both local and global levels, but also effectively handles more realistic and difficult few-shot RTE scenarios such as multiple triple extraction and nonexistence of triples. Extensive experimental results on existing dataset and new datasets demonstrate that our approach can significantly improve performance over the state-of-the-arts.

Content representation is a fundamental task in information retrieval. Representation learning is aimed at capturing features of an information object in a low-dimensional space. Most research on representation learning for heterogeneous information networks (HINs) focuses on static HINs. In practice, however, networks are dynamic and subject to constant change. In this article, we propose a novel and scalable representation learning model, M-DHIN , to explore the evolution of a dynamic HIN. We regard a dynamic HIN as a series of snapshots with different time stamps. We first use a static embedding method to learn the initial embeddings of a dynamic HIN at the first time stamp. We describe the features of the initial HIN via metagraphs, which retains more structural and semantic information than traditional path-oriented static models. We also adopt a complex embedding scheme to better distinguish between symmetric and asymmetric metagraphs. Unlike traditional models that process an entire network at each time stamp, we build a so-called change dataset that only includes nodes involved in a triadic closure or opening process, as well as newly added or deleted nodes. Then, we utilize the above metagraph-based mechanism to train on the change dataset. As a result of this setup, M-DHIN is scalable to large dynamic HINs since it only needs to model the entire HIN once while only the changed parts need to be processed over time. Existing dynamic embedding models only express the existing snapshots and cannot predict the future network structure. To equip M-DHIN with this ability, we introduce an LSTM-based deep autoencoder model that processes the evolution of the graph via an LSTM encoder and outputs the predicted graph. Finally, we evaluate the proposed model, M-DHIN , on real-life datasets and demonstrate that it significantly and consistently outperforms state-of-the-art models.

To represent a complex network, paths are often employed for capturing relationships among node: random walks for (homogeneous) networks and metapaths for heterogeneous information networks (HINs). However, there is structural (and possibly semantic) information loss when using paths to represent the subgraph between two nodes, since a path is a linear structure and a subgraph often is not. Can we find a better alternative for network embeddings? We offer a novel mechanism to capture the features of HIN nodes via metagraphs, which retains more structural and semantic information than path-oriented models. Inspired by developments in knowledge graph embedding, we propose to construct HIN triplets using nodes and metagraphs between them. Metagraphs are generated by harnessing the GRAMI algorithm, which enumerates frequent subgraph patterns in a HIN. Subsequently, the Hadamard function is applied to encode relationships between nodes and metagraphs, and the probability whether a HIN triplet can be evaluated. Further, to better distinguish between symmetric and asymmetric cases of metagraphs, we introduce a complex embedding scheme that is able to precisely express fine-grained features of HIN nodes. We evaluate the proposed model, M-HIN, on real-life datasets and demonstrate that it significantly and consistently outperforms state-of-the-art models.

Geoscience gives insights into our surroundings and benefits many aspects of our life. Nowadays, with massive sensors deployed to sense all kinds of parameters for environments, tens of billions, even trillions of sensed data are collected and need to be analyzed for surveillance or other purposes. From many perspectives, users always issue queries according to specific spatial and temporal predicates. For these applications, relational databases are overwhelmed by the large scale and high rate insertions, and NoSQL database could be considered a feasible solution. HBase, a popular key-value store system, is capable to solve the storage problem, but fails to provide in-built spatio-temporal querying capability. Many previous works tackle the problem by designing schema, i.e., designing row key and column key formation for HBase, which we don't believe is an effective solution. In this paper, we address this problem from nature level of HBase, and propose an index structure as a built-in component for HBase. STEHIX (Spatio-TEmporal Hbase IndeX) is adapted to two-level architecture of HBase and suitable for HBase to process spatio-temporal queries. It is composed of index in the meta table (the first level) and region index (the second level) for indexing inner structure of HBase regions. Base on this structure, two common queries, range query and kNN query are solved by proposing algorithms, respectively. For achieving load balancing and scalable kNN query, two optimizations are also presented. We implement STEHIX and conduct experiments on real dataset, and the results show our design outperforms a previous work in many aspects.

With the development of various Cloud platforms, providing spatio-temporal database services is an essential requirement for many applications, e.g., location-based services in Cloud. However, many previous works on processing queries in distributed environment fail to apply to spatio-temporal queries which is a significant role in spatio-temporal database. In this paper, we propose an efficient and scalable index for answering spatio-temporal queries in the Cloud. The index is a peer-to-peer-based overlay network, which is composed of two ring-structured overlays: spatial ring globally indexing spatial dimension and temporal ring for the temporal one. And it is featured by cost-aware function, i.e., a query is always able to be accomplished at a low cost, utilized by histograms keeping the distributions of Cloud nodes and data, maintained by each Cloud node. Both range query and kNN query can benefit from the mechanism, additionally, an elaborate algorithm for kNN processing is proposed, with which Cloud nodes can deliberatively send messages only to the result-related destinations. Furthermore, optimizations are also proposed for achieving low cost index maintenance and scalable kNN query processing. Experiments on both synthetic and real dataset show that our index is capable to support efficient and scalable range and kNN query, even for a skewed distribution.

Visualization is an important tool for social network analysis. Traditional visualization tools only take account into the links in the networks when displaying them. However, social networks are inherently attached to abundant attribute information. In order to display their attribute information, a novel visualization tool with attribute-based layout and coloring is developed in this paper. The core of this tool are two algorithms: one for node layout and the other for node coloring. These two algorithms are designed based on the principle of making the nodes with same attribute values closer in the diagram. The experiments on synthetic networks and real-world networks show that this tool can return more reasonable visualization.

Community evolution in social networks attracts a lot of attention in recent years. Existing methods always depict the relationship of two nodes using the temporary connection. However, these temporary connections cannot be fully recognized as the real relationships when the history connections among nodes are considered. Cumulative stable contacts are proposed to depict the correlation among nodes. The whole process is divided into timestamps. At each timestamp, the community cores will be detected due to the variation of nodes and links firstly. Then, all nodes will be divided into a few of communities due to the community cores. Meanwhile, communities can be tracked through the incremental computing, which can help to recognize the evolving of community structure. Empirical studies on real-world social networks demonstrate that our proposed method can effectively detect stable community in mobile social networks.

Provenance is a record that describes entities and processes involved in producing, delivering and influencing a resource. Provenance management and reuse can enable interesting applications for knowledge discovery and analytics. One crucial component of a provenance management system is the comparison between provenances. In the era of big data, provenance management systems are in need of a scalable algorithmic solution for efficient comparison. Existing solutions to the problem have large memory footprint and require overlong system response time. In this paper, we present a new solution to threshold-based provenance comparison. We model provenance directly as graph, and propose to measure provenance similarity using provenance edit distance. Following the depth-first search paradigm, we design an algorithm PEDSim based on an encoding technique specific to provenance graphs and quantifiable heuristics. Extensive experiments on real data demonstrate the superiority of our method to other alternatives.

In Network Centric Warfare, the sensor network's capability is much stronger than ever; the force can get a mass of information about battlespace in real or near-real time. How to utilize the information about battlespace effectively and transform the information superiority into knowledge superiority is a key problem for NCW research. To solve this problem, first we need to establish a suitable knowledge infrastructure. In this article, battlespace ontology is considered as the knowledge infrastructure of NCW, and we propose a battlespace ontology construction approach based on OWL, which consists of two parts: formal ontology construction approach to construct subdomain ontologies of battlespace and formal ontology integration approach to integrate subdomain ontologies of battlespace. Then a concrete application of the approach to an air combat battlespace is given. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1219–1231, 2005.

This paper presents an efficient decentralized Grid service discovery approach based on service ontology. It uses two techniques to improve efficiency. First, Grid information nodes are organized into community overlays of different service categories defined in service ontology. A distributed hash table (DHT) based upper layer network is constructed to provide efficient navigation between communities. Second, a simple and lightweight greedy search based service location (GSBSL) method is introduced to identify service providers with high QoS efficiently within communities. Simulation results show that, the efficiency is improved compared with existing decentralized Grid service discovery approaches, and the overhead is acceptable and controllable.