Wikidata is the largest general-interest knowledge base that is openly available. It is collaboratively edited by thousands of volunteer editors and has thus evolved considerably since its inception in 2012. In this paper, we present Wikidated 1.0, a dataset of Wikidata's full revision history, which encodes changes between Wikidata revisions as sets of deletions and additions of RDF triples. To the best of our knowledge, it constitutes the first large dataset of an evolving knowledge graph, a recently emerging research subject in the Semantic Web community. We introduce the methodology for generating Wikidated 1.0 from dumps of Wikidata, discuss its implementation and limitations, and present statistical characteristics of the dataset.

Learning the embeddings of knowledge graphs is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering. In recent years, many research efforts have been proposed for knowledge graph embedding. However, most previous knowledge graph embedding methods ignore the semantic similarity between the related entities and entity-relation couples in different triples since they separately optimize each triple with the scoring function. To address this problem, we propose a simple yet efficient contrastive learning framework for knowledge graph embeddings, which can shorten the semantic distance of the related entities and entity-relation couples in different triples and thus improve the expressiveness of knowledge graph embeddings. We evaluate our proposed method on three standard knowledge graph benchmarks. It is noteworthy that our method can yield some new state-of-the-art results, achieving 51.2% MRR, 46.8% Hits@1 on the WN18RR dataset, and 59.1% MRR, 51.8% Hits@1 on the YAGO3-10 dataset.

We have created a knowledge graph based on major data sources used in ecotoxicological risk assessment. We have applied this knowledge graph to an important task in risk assessment, namely chemical effect prediction. We have evaluated nine knowledge graph embedding models from a selection of geometric, decomposition, and convolutional models on this prediction task. We show that using knowledge graph embeddings can increase the accuracy of effect prediction with neural networks. Furthermore, we have implemented a fine-tuning architecture which adapts the knowledge graph embeddings to the effect prediction task and leads to a better performance. Finally, we evaluate certain characteristics of the knowledge graph embedding models to shed light on the individual model performance.

Representation learning models for Knowledge Graphs (KG) have proven to be effective in encoding structural information and performing reasoning over KGs. In this paper, we propose a novel pre-training-then-fine-tuning framework for knowledge graph representation learning, in which a KG model is firstly pre-trained with triple classification task, followed by discriminative fine-tuning on specific downstream tasks such as entity type prediction and entity alignment. Drawing on the general ideas of learning deep contextualized word representations in typical pre-trained language models, we propose SCoP to learn pre-trained KG representations with structural and contextual triples of the target triple encoded. Experimental results demonstrate that fine-tuning SCoP not only outperforms results of baselines on a portfolio of downstream tasks but also avoids tedious task-specific model design and parameter training.

Let's take a look at the visualization of the knowledge graph we built. We do this by clicking the Graph tab at the top bar, where the page will then display the graph diagram (based on a random sample of the entire network). The red nodes represent the drugs, while the blue nodes represent the side effects. We can easily see that Aspirin has the highest number of side effects based on node size. When we select a single side effect, e.g., hyporeflexia, we can see which drugs can potentially cause that specific side effect.

Existing KG-augmented models for question answering primarily focus on designing elaborate Graph Neural Networks (GNNs) to model knowledge graphs (KGs). However, they ignore (i) the effectively fusing and reasoning over question context representations and the KG representations, and (ii) automatically selecting relevant nodes from the noisy KGs during reasoning. In this paper, we propose a novel model, JointLK, which solves the above limitations through the joint reasoning of LMs and GNNs and the dynamic KGs pruning mechanism. Specifically, JointLK performs joint reasoning between the LMs and the GNNs through a novel dense bidirectional attention module, in which each question token attends on KG nodes and each KG node attends on question tokens, and the two modal representations fuse and update mutually by multi-step interactions. Then, the dynamic pruning module uses the attention weights generated by joint reasoning to recursively prune irrelevant KG nodes. Our results on the CommonsenseQA and OpenBookQA datasets demonstrate that our modal fusion and knowledge pruning methods can make better use of relevant knowledge for reasoning.

A major component for developing intelligent and autonomous robots is a suitable knowledge representation, from which a robot can acquire knowledge about its actions or world. However, unlike humans, robots cannot creatively adapt to novel scenarios, as their knowledge and environment are rigidly defined. To address the problem of producing novel and flexible task plans called task trees, we explore how we can derive plans with concepts not originally in the robot's knowledge base. Existing knowledge in the form of a knowledge graph is used as a base of reference to create task trees that are modified with new object or state combinations. To demonstrate the flexibility of our method, we randomly selected recipes from the Recipe1M+ dataset and generated their task trees. The task trees were then thoroughly checked with a visualization tool that portrays how each ingredient changes with each action to produce the desired meal. Our results indicate that the proposed method can produce task plans with high accuracy even for never-before-seen ingredient combinations.

Wikidata is a frequently updated, community-driven, and multilingual knowledge graph. Hence, Wikidata is an attractive basis for Entity Linking, which is evident by the recent increase in published papers. This survey focuses on four subjects: (1) Which Wikidata Entity Linking datasets exist, how widely used are they and how are they constructed? (2) Do the characteristics of Wikidata matter for the design of Entity Linking datasets and if so, how? (3) How do current Entity Linking approaches exploit the specific characteristics of Wikidata? (4) Which Wikidata characteristics are unexploited by existing Entity Linking approaches? This survey reveals that current Wikidata-specific Entity Linking datasets do not differ in their annotation scheme from schemes for other knowledge graphs like DBpedia. Thus, the potential for multilingual and time-dependent datasets, naturally suited for Wikidata, is not lifted. Furthermore, we show that most Entity Linking approaches use Wikidata in the same way as any other knowledge graph missing the chance to leverage Wikidata-specific characteristics to increase quality. Almost all approaches employ specific properties like labels and sometimes descriptions but ignore characteristics such as the hyper-relational structure. Hence, there is still room for improvement, for example, by including hyper-relational graph embeddings or type information. Many approaches also include information from Wikipedia, which is easily combinable with Wikidata and provides valuable textual information, which Wikidata lacks.

Knowledge Graph (KG) inference is the vital technique to address the natural incompleteness of KGs. The existing KG inference approaches can be classified into rule learning-based and KG embedding-based models. However, these approaches cannot well balance accuracy, generalization, interpretability and efficiency, simultaneously. Besides, these models always rely on pure triples and neglect additional information. Therefore, both KG embedding (KGE) and rule learning KG inference approaches face challenges due to the sparse entities and the limited semantics. We propose a novel and effective closed-loop KG inference framework EngineKGI operating similarly as an engine based on these observations. EngineKGI combines KGE and rule learning to complement each other in a closed-loop pattern while taking advantage of semantics in paths and concepts. KGE module exploits paths to enhance the semantic association between entities and introduces rules for interpretability. A novel rule pruning mechanism is proposed in the rule learning module by leveraging paths as initial candidate rules and employing KG embeddings together with concepts for extracting more high-quality rules. Experimental results on four real-world datasets show that our model outperforms other baselines on link prediction tasks, demonstrating the effectiveness and superiority of our model on KG inference in a joint logic and data-driven fashion with a closed-loop mechanism.

Knowledge graphs (KGs) on COVID-19 have been constructed to accelerate the research process of COVID-19. However, KGs are always incomplete, especially the new constructed COVID-19 KGs. Link prediction task aims to predict missing entities for (e, r, t) or (h, r, e), where h and t are certain entities, e is an entity that needs to be predicted and r is a relation. This task also has the potential to solve COVID-19 related KGs' incomplete problem. Although various knowledge graph embedding (KGE) approaches have been proposed to the link prediction task, these existing methods suffer from the limitation of using a single scoring function, which fails to capture rich features of COVID-19 KGs. In this work, we propose the MDistMult model that leverages multiple scoring functions to extract more features from existing triples. We employ experiments on the CCKS2020 COVID-19 Antiviral Drugs Knowledge Graph (CADKG). The experimental results demonstrate that our MDistMult achieves state-of-the-art performance in link prediction task on the CADKG dataset