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A degree centrality-enhanced computational approach for local network alignment leveraging knowledge graph embeddings

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 275卷

作者： Djeddi, Warith Eddine Ben Yahia, Sadok Nguifo, Engelbert Mephu Univ Tunis El Manar Fac Sci Dept Comp Sci Tunis 2092 Tunisia Tallinn Univ Technol Akad Tee 15a EE-12618 Tallinn Estonia Southern Denmark Univ Als 2 Sonderborg DK-6400 Sonderborg Denmark Univ Clermont Auvergne Clermont Auvergne INP CNRS Clermont AuvergneENSMSELIMOS F-63000 Clermont Ferrand France

Biological network alignment serves various goals, including identifying similar regions between networks of different species, transferring biological knowledge, and predicting protein complexes and functions. This paper presents KOGAL (knowledge graph ALignment), a scalable multiprocessing algorithm for local protein- protein interaction (PPI) network alignment. KOGAL aims to predict conserved protein complexes across species' PPI networks. It employs two strategies for seed discovery and initial alignment: the first computes the cosine similarity between embedding vectors derived from knowledge graph models like TransE or DistMult to generate an alignment matrix. The second strategy begins the alignment process by calculating the centrality degree of nodes within each network, highlighting the importance of each protein in the network structure. Protein similarities are quantified by combining protein sequence similarities with knowledge graph embeddings, ensuring biologically meaningful structural alignments. KOGAL depicts strong results compared to other state-of-the-art approaches. When evaluated on real PPI networks, KOGAL demonstrates high accuracy across multiple metrics, including coverage, sensitivity, the number of matched reference conserved complexes (Frac), complex-wise sensitivity (Sn), positive predictive value (PPV), geometric accuracy (ACC), and maximum matching ratio (MMR).

关键词： Protein networks Degree centrality knowledge graph embedding Local network alignment Protein complex

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Towards leveraging explicit negative statements in knowledge graph embeddings

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JOURNAL OF WEB SEMANTICS 2025年 84卷

作者： Sousa, Rita T. Pesquita, Catia Paulheim, Heiko Univ Mannheim Data & Web Sci Grp Mannheim Germany Univ Lisbon LaSIGE Lisbon Portugal

knowledge graphs are used in various domains to represent knowledge about entities and their relations. In the vast majority of cases, they capture what is known to be true about those entities, i.e., positive statements, while the Open World Assumption implicitly states that everything not expressed in the graph mayor may not be true. This makes it difficult and less frequent to capture information explicitly known not to be true, i.e., negative statements. Moreover, while those negative statements could bear the potential to learn more useful representations in knowledge graph embeddings, that direction has been explored only rarely. However, in many domains, negative information is particularly interesting, for example, in recommender systems, where negative associations of users and items can help in learning better user representations, or in the biomedical domain, where the knowledge that a patient does exhibit a specific symptom can be crucial for accurate disease diagnosis. In this paper, we argue that negative statements should be given more attention in knowledge graph embeddings. Moreover, we investigate how they can be used in knowledge graph embedding methods, highlighting their potential in some interesting use cases. We discuss some existing works and preliminary results that incorporate explicitly declared negative statements in walk-based knowledge graph embedding methods. Finally, we outline promising avenues for future research in this area.

关键词： knowledge graph knowledge graph embedding Negative statements

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Multihop Fuzzy Spatiotemporal RDF knowledge graph Query via Quaternion embedding

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第5期32卷 3026-3036页

作者： Ji, Hao Yan, Li Ma, Zongmin Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China

The proliferation of uncertain spatiotemporal data has led to an increasing demand for fuzzy spatiotemporal knowledge modeling in various applications. However, performing multihop query modeling on incomplete fuzzy spatiotemporal knowledge graphs (KGs) poses significant challenges. Recently, embedding-based multihop KG querying approaches have gained attention. Yet, these approaches often overlook KG uncertainty and spatiotemporal sensitivity, resulting in the neglect of fuzzy spatiotemporal information during multihop path reasoning. To address these challenges, we propose an embedding-based multihop query model for fuzzy spatiotemporal KG. We use quaternion to jointly embed spatiotemporal entities, and relations are represented as rotations from spatiotemporal subject to object. We incorporate uncertainty by the scoring function's bias factor, allowing for relaxation embedding. This approach facilitates the learning of a richer representation of fuzzy spatiotemporal KGs in vector space. By exploiting the inherent noncommutative compositional pattern of quaternions, we construct more accurate multihop paths within fuzzy spatiotemporal KGs, thus improving path reasoning performance. To evaluate the effectiveness of our model, we conduct experiments on two fuzzy spatiotemporal KG datasets, focusing on link prediction and path query answering. Results show that our proposed method significantly outperforms several state-of-the-art baselines in terms of performance metrics.

关键词： Spatiotemporal phenomena Uncertainty Semantics Quaternions Data models Cognition Resource description framework Fuzzy spatiotemporal data knowledge graph embedding knowledge reasoning multihop query

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Sub-Entity embedding for inductive spatio-temporal knowledge graph completion

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2023年第1期148卷 240-249页

作者： Wan, Guojia Zhou, Zhengyun Zheng, Zhigao Du, Bo Wuhan Univ Inst Artificial Intelligence Natl Engn Res Ctr Multimedia Software Wuhan 430072 Hubei Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Hubei Peoples R China Hubei Luojia Lab Wuhan 430072 Hubei Peoples R China Wuhan Univ Hubei Key Lab Multimedia & Network Commun Engn Wuhan 430072 Hubei Peoples R China

Existing large-scale knowledge graphs generally contain rich spatial and temporal information. knowledge graph completion has gained wide attention with a massive number of models proposed for inferring missing knowledge. Despite the fact that numerous approaches to knowledge graph completion exist, they often overlook the importance of simultaneously modeling spatial and temporal information, resulting in limited capacity to infer knowledge related to time and location. An intuitive solution to this issue is to use quintuple representation for spatio-temporal facts. To reduce the complexity of learning quintuples, in this paper, we propose sub-entity to tokenize every entity, relation, time stamp and location with a fixed-size vocabulary. Meanwhile, we design Spatio-Temporal Message Passing layer to learn the latent feature vectors of a knowledge graph. We conducted entity link prediction, relation link prediction, time prediction and location prediction experiments. The quantitative results demonstrate the effectiveness of our model in both predicting missing knowledge under both transductive link prediction and inductive link prediction. The visualization results also show that our model can capture meaningful time and location information.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： knowledge graph knowledge graph embedding knowledge graph completion Spatio-temporal data

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Open knowledge graph Link Prediction with Semantic-Aware embedding

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期249卷

作者： Wang, Jingbin Huang, Hao Wu, Yuwei Zhang, Fuyuan Zhang, Sirui Guo, Kun Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China

Link prediction in open knowledge graphs (OpenKGs) is crucial for applications like question answering and recommendation systems. Existing OpenKG models leverage the semantic information of noun phrases (NPs) to enhance the performance in the link prediction task. However, these models only extract superficial semantic information from NPs, ignoring the fact that an NP possesses diverse semantics. Furthermore, these models have not fully exploited the semantic information of the relation phrases (RPs). To address these issues, we propose a model for link prediction called Open knowledge graph Link Prediction with Semantic -Aware embedding (SeAE). First, we develop an adaptive disentanglement embedding (ADE) mechanism to learn the intrinsically abundant semantics of NPs. The ADE mechanism can adaptively calculate the embedding segmentation number according to the dataset and has an ingenious method for updating embeddings. Second, we integrate the attention mechanism into the GRU encoder to obtain the distribution of importance inside RP, facilitating a more comprehensive capture of the RP's semantic information and enhancing the model's interpretability. Finally, we design a relation gate, which extracts the RP semantic features of tail NP from the shared edge. This gate realizes the relation constraints on entities while enhancing the interaction between entities and relations. Extensive experiments on four benchmarks demonstrate that SeAE outperforms the state-of-the-art models, resulting in improvements of approximately 5.4% and 7.4% in MRR on ReVerb45K and ReVerb45KF datasets respectively.

关键词： Open knowledge graph knowledge graph embedding Link prediction Semantic-aware Attention mechanism

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knowledge graph confidence-aware embedding for recommendation

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NEURAL NETWORKS 2024年 180卷 106601页

作者： Huang, Chen Yu, Fei Wan, Zhiguo Li, Fengying Ji, Hui Li, Yuandi Zhejiang Lab Hangzhou 311121 Peoples R China Harbin Univ Sci & Technol Harbin 150006 Peoples R China Jiangsu Univ Zhenjiang 212013 Peoples R China

knowledge graphs (KG) are vital for extracting and storing knowledge from large datasets. Current research favors knowledge graph-based recommendation methods, but they often overlook the features learning of relations between entities and focus excessively on entity-level details. Moreover, they ignore a crucial fact: the aggregation process of entity and relation features in KG is complex, diverse, and imbalanced. To address this, we propose a recommendation-oriented KG confidence-aware embedding technique. It introduces an information aggregation graph and a confidence feature aggregation mechanism to overcome these challenges. Additionally, we quantify entity confidence at the feature and category levels, improving the precision of embeddings during information propagation and aggregation. Our approach achieves significant improvements over state-of-the-art KG embedding-based recommendation methods, with up to 6.20% increase in AUC and 8.46% increase in GAUC, as demonstrated on four public KG datasets(2).

关键词： Recommendation systems knowledge graph embedding Confidence-aware embedding

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Generalized Translation-Based embedding of knowledge graph

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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 2020年第5期32卷 941-951页

作者： Ebisu, Takuma Ichise, Ryutaro SOKENDAI Dept Informat Tokyo 2400193 Japan Natl Inst Informat Tokyo 1000003 Japan

knowledge graphs are useful for many AI tasks but often have missing facts. To populate the graphs, knowledge graph embedding models have been developed. TransE is one of such models and the first translation-based method. TransE is well known because the principle of TransE can effectively capture the rules of a knowledge graph although it seems very simple. However, TransE has problems with its regularization and an unchangeable ratio of negative sampling. In this paper, we generalize TransE to solve these problems by proposing knowledge graph embedding on a Lie group (KGLG) and the Weighted Negative Part (WNP) method for the objective function of translation-based models. KGLG is the novel translation-based method which embeds entities and relations of a knowledge graph on any Lie group. It allows us not to employ regularization during training of the model if we choose a compact lie group for the embedding space. The WNP method is for changing the ratio of negative sampling, which enhances translation-based models. Our approach outperforms other state-of-the-art approaches such as TransE, DistMult, and ComplEx on a standard link prediction task. We show that TorusE, KGLG on a torus, is scalable to large-size knowledge graphs and faster than the original TransE.

关键词： knowledge graph embedding

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A semantic guide-based embedding method for knowledge graph completion

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EXPERT SYSTEMS 2024年第8期41卷 e13584-e13584页

作者： Zhang, Jinglin Shen, Bo Wang, Tao Zhong, Yu Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China

knowledge graph embedding aims to map entities and relations into a low-dimensional vector space for easy manipulation. However, frequent entities are updated more often than infrequent ones during training, leading to inadequate representation of the latter's embeddings, which, in turn, affects the model's overall performance in downstream tasks. To address this issue, we propose a semantic information guide and enhance (SGE) method. The SGE tackles the heterogeneity in the frequency of entities through semantic reconstruction and a guidance network. The semantic reconstruction strengthens the semantic relevance among all entities and connects entities with different frequencies in semantic space. The guidance network extends these connections to knowledge space, enhancing the expression abilities of infrequent entities' embeddings without compromising the embeddings of frequent entities. Experiments with four commonly used benchmark datasets show that the SGE method improves the performance of baseline models in most cases and that the method is model-independent.

关键词： entity frequency heterogeneity knowledge graph embedding semantic information semantic guidance

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Improving knowledge graph completion via increasing embedding interactions

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APPLIED INTELLIGENCE 2022年第8期52卷 9289-9307页

作者： Li, Weidong Peng, Rong Li, Zhi Wuhan Univ Sch Comp Sci Wuhan Peoples R China Guangxi Normal Univ Coll Comp Sci & Informat Technol Guilin Guangxi Peoples R China

knowledge graphs usually consist of billions of triplet facts describing the real world. Although most of the existing knowledge graphs are huge in scale, they are still far from completion. As a result, varieties of knowledge graph embedding approaches have emerged, which have been proven to be an effective and efficient solution for knowledge graph completion. In this paper, we devise a novel knowledge graph embedding model named InterERP, which aims to improve model performance by increasing Inter actions between the embeddings of E ntities, R elations and relation P aths. Specifically, we first introduce the interaction matrix to obtain the interaction embeddings of entities and relations. Then, we employ the Inception network to learn the query embedding, which can further increase the interactions between entities and relations. Furthermore, we resort to logical rules to construct semantic relation paths and are committed to modeling the interactions between different relations in a relation path. The experimental results on four commonly used datasets, demonstrate that the proposed InterERP matches or outperforms the state-of-the-art approaches.

关键词： knowledge graph completion Interaction embeddings knowledge graph embedding Inception network

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NEUROCOMPUTING 2022年 491卷 186-196页

作者： Li, Mingda Sun, Zhengya Zhang, Wensheng Univ Chinese Acad Sci UCAS Beijing 100049 Peoples R China Chinese Acad Sci Res Ctr Precis Sensing & Control Inst Automat Beijing 100190 Peoples R China

knowledge graph embedding acts as a pivotal role in predicting the missing information in knowledge graphs (KGs). Due to the evolving nature of real-world KGs, one requires the ability to make predictions for newly emerging entities besides those already observed at training time. Current studies have made great efforts to develop a neighborhood aggregator and embed out-of-knowledge-graph (OOKG) entities inductively, with less focus on exploiting the similarity between the existing and newly emerging enti-ties. Attaching importance to such similarity helps facilitate semantic transfer. In this work, we propose a similarity-aware aggregation network for embedding out-of-knowledge-graph entities. Motivated by the fact that similar entities are likely to occur in common graph context, we skillfully design a similarity-aware function, which measures the distance of each entity pair based on the contextual gap. Moreover, we aggregate the neighborhood surrounding the target entity and its similarity information by query-specific attention weights, which are optimized during the learning process. Extensive experi-ments on knowledge graph completion task show that our method achieves substantial improvements over baselines.(c) 2022 Elsevier B.V. All rights reserved.

关键词： knowledge graph embedding Out-of-knowledge-graph entities knowledge graph completion Similarity search

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