CTEA: Context and Topic Enhanced Entity Alignment for knowledge graphs

Multilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge with cross-lingual inferences, which benefit various knowledge-driven cross-lingual NLP tasks. However, precisely learning such cross-lingual inferences is usually hindered by the low coverage of entity alignment in many KGs. Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions. Our approach performs co-training of two embedding models, i.e. a multilingual KG embedding model and a multilingual literal description embedding model. The models are trained on a large Wikipedia-based trilingual dataset where most entity alignment is unknown to training. Experimental results show that the performance of the proposed approach on the entity alignment task improves at each iteration of co-training, and eventually reaches a stage at which it significantly surpasses previous approaches. We also show that our approach has promising abilities for zero-shot entity alignment, and cross-lingual KG completion.

Knowledge graph (KG) embedding is to embed the entities and relations of a KG into a low-dimensional continuous vector space while preserving the intrinsic semantic associations between entities and relations. One of the most important applications of knowledge graph embedding (KGE) is link prediction (LP), which aims to predict the missing fact triples in the KG. A promising approach to improving the performance of KGE for the task of LP is to increase the feature interactions between entities and relations so as to express richer semantics between them. Convolutional neural networks (CNNs) have thus become one of the most popular KGE models due to their strong expression and generalization abilities. To further enhance favorable features from increased feature interactions, we propose a lightweight CNN-based KGE model called IntSE in this paper. Specifically, IntSE not only increases the feature interactions between the components of entity and relationship embeddings with more efficient CNN components but also incorporates the channel attention mechanism that can adaptively recalibrate channel-wise feature responses by modeling the interdependencies between channels to enhance the useful features while suppressing the useless ones for improving its performance for LP. The experimental results on public datasets confirm that IntSE is superior to state-of-the-art CNN-based KGE models for link prediction in KGs.

Knowledge Graph (KG) embedding approaches have been proved effective to infer new facts for a KG based on the existing ones–a problem known as KG completion. However, most of them have focused on static KGs, in fact, relational facts in KGs often show temporal dynamics, e.g., the fact (US, has president, Barack Obama, [2009–2017]) is only valid from 2009 to 2017. Therefore, utilizing available time information to develop temporal KG embedding models is an increasingly important problem. In this paper, we propose a new hyperplane-based time-aware KG embedding model for temporal KG completion. By employing the method of time-specific hyperplanes, our model could explicitly incorporate time information in the entity-relation space to predict missing elements in the KG more effectively, especially temporal scopes for facts with missing time information. Moreover, in order to model and infer four important relation patterns including symmetry, antisymmetry, inversion and composition, we map facts happened at the same time into a polar coordinate system. During training procedure, a time-enhanced negative sampling strategy is proposed to get more effective negative samples. Experimental results on datasets extracted from real-world temporal KGs show that our model significantly outperforms existing state-of-the-art approaches for the KG completion task.

Computing knowledge graph (KG) embeddings is a technique to learn distributional representations for components of a knowledge graph while preserving structural information. The learned embeddings can be used in multiple downstream tasks such as question answering, information extraction, query expansion, semantic similarity, and information retrieval. Over the past years, multiple embedding techniques have been proposed based on different underlying assumptions. The most actively researched models are translation-based which treat relations as translation operations in a shared (or relation-specific) space. Interestingly, almost all KG embedding models treat each triple equally, regardless of the fact that the contribution of each triple to the global information content differs substantially. Many triples can be inferred from others, while some triples are the foundational (basis) statements that constitute a knowledge graph, thereby supporting other triples. Hence, in order to learn a suitable embedding model, each triple should be treated differently with respect to its information content. Here, we propose a data-driven approach to measure the information content of each triple with respect to the whole knowledge graph by using rule mining and PageRank. We show how to compute triple-specific weights to improve the performance of three KG embedding models (TransE, TransR and HolE). Link prediction tasks on two standard datasets, FB15K and WN18, show the effectiveness of our weighted KG embedding model over other more complex models. In fact, for FB15K our TransE-RW embeddings model outperforms models such as TransE, TransM, TransH, and TransR by at least 12.98% for measuring the Mean Rank and at least 1.45% for HIT@10. Our HolE-RW model also outperforms HolE and ComplEx by at least 14.3% for MRR and about 30.4% for HIT@1 on FB15K. Finally, TransR-RW show an improvement over TransR by 3.90% for Mean Rank and 0.87% for HIT@10.

Knowledge graph embedding (KGE) models represent each entity and relation of a knowledge graph (KG) with low-dimensional embedding vectors. These methods have recently been applied to KG link prediction and question answering over incomplete KGs (KGQA). KGEs typically create an embedding for each entity in the graph, which results in large model sizes on real-world graphs with millions of entities. For downstream tasks these atomic entity representations often need to be integrated into a multi stage pipeline, limiting their utility. We show that an off-the-shelf encoder-decoder Transformer model can serve as a scalable and versatile KGE model obtaining state-of-the-art results for KG link prediction and incomplete KG question answering. We achieve this by posing KG link prediction as a sequence-to-sequence task and exchange the triple scoring approach taken by prior KGE methods with autoregressive decoding. Such a simple but powerful method reduces the model size up to 98% compared to conventional KGE models while keeping inference time tractable. After finetuning this model on the task of KGQA over incomplete KGs, our approach outperforms baselines on multiple large-scale datasets without extensive hyperparameter tuning.

Knowledge Graph (KG) embedding has attracted more attention in recent years. Most KG embedding models learn from time-unaware triples. However, the inclusion of temporal information besides triples would further improve the performance of a KGE model. In this regard, we propose ATiSE, a temporal KG embedding model which incorporates time information into entity/relation representations by using Additive Time Series decomposition. Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal KGs into the space of multi-dimensional Gaussian distributions. The mean of each entity/relation embedding at a time step shows the current expected position, whereas its covariance (which is temporally stationary) represents its temporal uncertainty. Experimental results show that ATiSE significantly outperforms the state-of-the-art KGE models and the existing temporal KGE models on link prediction over four temporal KGs.

Recently, Hyper-relational Knowledge Graphs (HKGs) have been proposed as an extension of traditional Knowledge Graphs (KGs) to better represent real-world facts with additional qualifiers. As a result, researchers have attempted to adapt classical Knowledge Graph Embedding (KGE) models for HKGs by designing extra qualifier processing modules. However, it remains unclear whether the superior performance of Hyper-relational KGE (HKGE) models arises from their base KGE model or the specially designed extension module. In this paper, we data-wise convert HKGs to KG format using decomposition methods and then evaluate several classical KGE models' performance on HKGs. Our results show that some KGE models achieve comparable performance to HKGE models. Upon further analysis, we find that the decomposition methods alter the original HKG topology and fail to fully preserve HKG information. Moreover, we observe that current HKGE models are either insufficient in capturing the graph's long-range dependency or struggle to integrate main-triple and qualifier information due to the information compression issue. To further justify our findings and provide a direction for HKGE research, we propose FormerGNN, which employs a qualifier integrator to preserve the original HKG topology, a GNN-based graph encoder to capture the graph's long-range dependencies, and an improved approach for integrating main-triple and qualifier information to mitigate compression issues. Our experimental results demonstrate that FormerGNN outperforms existing HKGE models.

Neuro-Symbolic Artificial Intelligence (AI) focuses on integrating symbolic and sub-symbolic systems to enhance the performance and explainability of predictive models. Symbolic and sub-symbolic approaches differ fundamentally in how they represent data and make use of data features to reach conclusions. Neuro-symbolic systems have recently received significant attention in the scientific community. However, despite efforts in neural-symbolic integration, symbolic processing can still be better exploited, mainly when these hybrid approaches are defined on top of knowledge graphs. This work is built on the statement that knowledge graphs can naturally represent the convergence between data and their contextual meaning (i.e., knowledge). We propose a hybrid system that resorts to symbolic reasoning, expressed as a deductive database, to augment the contextual meaning of entities in a knowledge graph, thus, improving the performance of link prediction implemented using knowledge graph embedding (KGE) models. An entity context is defined as the ego network of the entity in a knowledge graph. Given a link prediction task, the proposed approach deduces new RDF triples in the ego networks of the entities corresponding to the heads and tails of the prediction task on the knowledge graph (KG). Since knowledge graphs may be incomplete and sparse, the facts deduced by the symbolic system not only reduce sparsity but also make explicit meaningful relations among the entities that compose an entity ego network. As a proof of concept, our approach is applied over a KG for lung cancer to predict treatment effectiveness. The empirical results put the deduction power of deductive databases into perspective. They indicate that making explicit deduced relationships in the ego networks empowers all the studied KGE models to generate more accurate links.

Knowledge Graph Embedding (KGE) is a technique used to capture structural information from Knowledge Graphs (KGs), enabling various downstream applications such as recommender system. KGE models trained on integrated KGs from multiple organizations tend to outperform those trained on a single KG, owing to the greater richness and diversity of information. Therefore, Federated Knowledge Graph Embedding (FKGE) emerges as a promising approach for privacy-preserving training of KGE models on KGs across organizations (clients). Existing FKGE framework learns a uniform global KGE model that achieves global optima by minimizing aggregated loss across clients. However, heterogeneity among KGs often leads to divergent local optima. This presents a fundamental trade-off: ensuring global optima can compromise local performance, while focusing on local optima can decrease global model utility. To overcome this, we propose Federated Local Adaptive Knowledge Graph Embedding (FedAdap) by drawing inspiration from partial federated learning. FedAdap employs a multilayer convolutional neural network, wherein its lower layers are shared across clients to learn shared information, it maps a seed KGE model into an alignment vector space representation. Its upper layers remain private, transforming the alignment vector space representation to an adaptive KGE model tailored to the local KG. Through this, FedAdap allows clients to leverage shared information while maintaining local adaptability and mitigating the impact of KGs heterogeneity. Experiments on data sets FB15k-237 and NELL-995 show that FedAdap outperforms its counterparts in link prediction tasks.

We study the problem of generating data poisoning attacks against Knowledge Graph Embedding (KGE) models for the task of link prediction in knowledge graphs. To poison KGE models, we propose to exploit their inductive abilities which are captured through the relationship patterns like symmetry, inversion and composition in the knowledge graph. Specifically, to degrade the model's prediction confidence on target facts, we propose to improve the model's prediction confidence on a set of decoy facts. Thus, we craft adversarial additions that can improve the model's prediction confidence on decoy facts through different inference patterns. Our experiments demonstrate that the proposed poisoning attacks outperform state-of-art baselines on four KGE models for two publicly available datasets. We also find that the symmetry pattern based attacks generalize across all model-dataset combinations which indicates the sensitivity of KGE models to this pattern.

The rapid development of knowledge graphs (KGs), such as Freebase and WordNet, has changed the paradigm for AI-related applications. However, even though these KGs are impressively large, most of them are suffering from incompleteness, which leads to performance degradation of AI applications. Most existing researches are focusing on knowledge graph embedding (KGE) models. Nevertheless, those models simply embed entities and relations into latent vectors without leveraging the rich information from the relation structure. Indeed, relations in KGs conform to a three-layer hierarchical relation structure (HRS), i.e., semantically similar relations can make up relation clusters and some relations can be further split into several fine-grained sub-relations. Relation clusters, relations and sub-relations can fit in the top, the middle and the bottom layer of three-layer HRS respectively. To this end, in this paper, we extend existing KGE models TransE, TransH and DistMult, to learn knowledge representations by leveraging the information from the HRS. Particularly, our approach is capable to extend other KGE models. Finally, the experiment results clearly validate the effectiveness of the proposed approach against baselines.

Knowledge graph embedding (KGE) models have become popular means for making discoveries in knowledge graphs (e.g., RDF graphs) in an efficient and scalable manner. The key to success of these models is their ability to learn low-rank vector representations for knowledge graph entities and relations. Despite the rapid development of KGE models, state-of-the-art approaches have mostly focused on new ways to represent embeddings interaction functions (i.e., scoring functions). In this paper, we argue that the choice of other training components such as the loss function, hyperparameters and negative sampling strategies can also have substantial impact on the model efficiency. This area has been rather neglected by previous works so far and our contribution is towards closing this gap by a thorough analysis of possible choices of training loss functions, hyperparameters and negative sampling techniques. We finally investigate the effects of specific choices on the scalability and accuracy of knowledge graph embedding models.

Rule-enhanced iterative complementation for knowledge graph reasoning

Knowledge graph (KG) embedding aims to encode entities and relations into low-dimensional vector spaces, in turn, can support various machine learning models on KG related tasks with good performance. However, existing methods for knowledge graph embedding fail to consider the influence of the embedding space, which makes them still unsatisfactory in practical applications. In this study, we try to improve the expressiveness of the embedding space from the perspective of the metric. Specifically, we first point out the implications of Minkowski metric used in KG embedding and then make a quantitative analysis. To solve the limitations, we introduce a new metric, named Cycle metric, based on the oscillation property of the periodic function. Furthermore, we find that the function period has a significant influence on the expressiveness of the embedding space. Given a fully trained model, the smaller the period, the better the expressive ability. Finally, to validate the findings, we propose a new model, named CyclE by combining Cycle Metric and the popular KG embeddings models. Comprehensive experimental results show that Cycle is more appropriate than Minkowski for KG embedding.

Recently, large-scale knowledge graphs (KGs) have become a key asset for search, analytics, recommendations and data integration. Large-scale KGs provide millions of facts about the real world. Each fact is composed as (subject, relation, object), e.g., the triplet (“Cristiano Ronaldo”, playFor, “Real Madrid”). However, these facts are blind to the temporal dimension. Actually, knowledge in practice is time-variant and many relations are only valid for a certain period of time. This phenomenon highlights the importance of building temporal knowledge graphs. In particular, knowledge in temporal KG is represented as (subject, relation, object, valid time), e.g., (“Cristiano Ronaldo”, playFor, “Real Madrid”, “[2009, 2018]”. However, research on temporal KG is very current and there are still many problems needed to be addressed. One obvious problem is that the size of temporal KG is still very small. For example, only 6.6% of the facts are time-aware in one of the largest knowledge graphs, YAGO3. In addition, 71% of people have no known place of birth, and 75% have no known nationality in Freebase. Furthermore, over 87.7% of the facts are uncovered in Japanese DBpedia compared with English DBpedia. Therefore, in this thesis, we study how to enlarge and enrich temporal knowledge graphs from three aspects, namely, temporal KG enrichment. In particular, we study the enrichment problem from the following aspects: (1) volume, (2) completeness, and (3) coverage.Our first solution is temporal knowledge harvesting which extracts temporal knowledge from free text directly. However, text corpus is noisy, and extracting structured temporal facts with high accuracy and coverage is very challenging. Inspired by pattern-based systems, we propose a temporal knowledge harvesting framework. In particular, we propose various techniques to extract temporal patterns, including corpus annotation, pattern generation, scoring and clustering. These techniques can reduce ambiguity in the text corpus and can improve both the accuracy and coverage of the extracted patterns. Second, we leverage the extracted patterns to harvest temporal knowledge. To improve the accuracy, we propose a parse-tree-based method. And to increase the coverage, we consider the relationships between tree components, including part of speech (POS), clause types, constituency and dependency. Experiments on real-world datasets verify the effectiveness of our proposed framework.Our second solution is temporal knowledge graph completion. Temporal KG completion is the task of inferring unobserved edges between entity pairs. Generally, temporal KG completion relies on the temporal KG embeddings technique, which learns the low-dimensional representations of all KG components. As research on temporal KG embedding is very current, we study both the embedding and the completion problems. In particular, we observe that contexts are extremely useful for learning the representations of entities and for inferring the unknown time intervals. As a result, we propose a context-aware embedding model for KG embeddings and a context-based temporal inference model for KG completion. In our embedding model, we not only capture factual plausibility as traditional methods did, but also propose a new measure on contexts, named temporal consistency. It measures how well the target entity interacts with its surrounding contexts on the temporal dimension. Our completion model is based on the embedding model, and further captures the interactions on the entity dimension. Extensive experiments verify the effectiveness of our models.Our third solution is temporal knowledge graph alignment which aims to discover the SameAs edges across two temporal KGs. Not like previous attribute-based alignment models, we further divide attribute facts into character facts, digit facts and time facts. In particular, we observe that the context information is extremely useful for the identification of the same entities. Therefore, we propose an alignment model on leveraging temporal contexts to represent entities. However, contexts of the same object can be very different. For example, the value of career predicate changed from “football player” to “coach” for the football player “Zidane”. This is because entities are evolving over time and thus the predicate values can be different. In our framework, we propose an alignment module that simulates the entity evolving process. Specifically, this module captures the interactions between contexts and aggregates context information to represent the entity. Lastly, we found that not all contexts are relevant, e.g., height v.s. career. Actually, only the relevant contexts are useful for capturing the evolving. Therefore, we propose a clustering approach for grouping relevant contexts together. Our experimental results validate the superiority of our proposed alignment model.