Bert-based Knowledge Graph Completion Algorithm for Few-Shot

Knowledge graph completion is an important research issue in knowledge graph construction, knowledge engineering, and natural language processing. A knowledge graph is a knowledge support for realizing accurate knowledge services in general and professional fields. It is also an important breakthrough foundation in information retrieval, question-and-answer interactions, and information recommendation. The low quality and small scale of the knowledge graph are the main bottlenecks that hinder its wide applications. The purpose of knowledge graph completion is to build a large-scale and high-quality knowledge graph for continuously updating and expanding the knowledge graph. Aiming at the difficulty of knowledge graph completion methods to extract deep semantic features from auxiliary information, such as unstructured texts, this study proposes a knowledge graph completion method based on parsing graph embedding and weighted graph convolutional network. This method uses the weighted graph convolutional network to model the semantic dependency parsing of the entity description and construct the semantic dependency parsing graph embedding. Furthermore, it introduces a multi-grained sentence-embedding generation method of the entity description, which is intended to build entity representation learning that can capture multi-grained semantics and deep-level semantic features. The experimental results on two public datasets show that the proposed knowledge graph completion approach outperforms the existing methods, thereby demonstrating its effectiveness and superiority.

As research on utilizing human knowledge in natural language processing has attracted considerable attention in recent years, knowledge graph (KG) completion has come into the spotlight. Recently, a new knowledge graph completion method using a pre-trained language model, such as KG-BERT, is presented and showed high performance. However, its scores in ranking metrics such as Hits@k are still behind state-of-the-art models. We claim that there are two main reasons: 1) failure in sufficiently learning relational information in knowledge graphs, and 2) difficulty in picking out the correct answer from lexically similar candidates. In this paper, we propose an effective multi-task learning method to overcome the limitations of previous works. By combining relation prediction and relevance ranking tasks with our target link prediction, the proposed model can learn more relational properties in KGs and properly perform even when lexical similarity occurs. Experimental results show that we not only largely improve the ranking performances compared to KG-BERT but also achieve the state-of-the-art performances in Mean Rank and Hits@10 on the WN18RR dataset.

In natural language processing (NLP) and computer vision (CV), the successful application of foundation models across diverse tasks has demonstrated their remarkable potential.However, despite the rich structural and textual information embedded in knowledge graphs (KGs), existing research of foundation model for KG has primarily focused on their structural aspects, with most efforts restricted to in-KG tasks (e.g., knowledge graph completion, KGC).This limitation has hindered progress in addressing more challenging out-of-KG tasks.In this paper, we introduce MERRY, a foundation model for general knowledge graph reasoning, and investigate its performance across two task categories: in-KG reasoning tasks (e.g., KGC) and out-of-KG tasks (e.g., KG question answering, KGQA).We not only utilize the structural information, but also the textual information in KGs.Specifically, we propose a multi-perspective Conditional Message Passing (CMP) encoding architecture to bridge the gap between textual and structural modalities, enabling their seamless integration.Additionally, we introduce a dynamic residual fusion module to selectively retain relevant textual information and a flexible edge scoring mechanism to adapt to diverse downstream tasks.Comprehensive evaluations on 28 datasets demonstrate that MERRY outperforms existing baselines in most scenarios, showcasing strong reasoning capabilities within KGs and excellent generalization to out-of-KG tasks such as KGQA.

<abstract><p>Knowledge graphs (KGs) often suffer from incompleteness. Knowledge graph completion (KGC) is proposed to complete missing components in a KG. Most KGC methods focus on direct relations and fail to leverage rich semantic information in multi-hop paths. In contrast, path-based embedding methods can capture path information and utilize extra semantics to improve KGC. However, most path-based methods cannot take advantage of full multi-hop information and neglect to capture multiple semantic associations between single and multi-hop triples. To bridge the gap, we propose a novel path-enhanced joint learning approach called PEJL for KGC. Rather than learning multi-hop representations, PEJL can recover multi-hop embeddings by encoding full multi-hop components. Meanwhile, PEJL extends the definition of translation energy functions and generates new semantic representations for each multi-hop component, which is rarely considered in path-based methods. Specifically, we first use the path constraint resource allocation (PCRA) algorithm to extract multi-hop triples. Then we use an embedding recovering module consisting of a bidirectional gated recurrent unit (GRU) layer and a fully connected layer to obtain multi-hop embeddings. Next, we employ a KG modeling module to leverage various semantic information and model the whole knowledge graph based on translation methods. Finally, we define a joint learning approach to train our proposed PEJL. We evaluate our model on two KGC datasets: FB15K-237 and NELL-995. Experiments show the effectiveness and superiority of PEJL.</p></abstract>

Scholarly knowledge graph is a knowledge graph that is used to represent knowledge contained in scientific publication documents. The information we can find in a scientific publication document is as follows: author, institution, name of journal/conference, and research topic. A knowledge graph that has been built is usually still not perfect. Some incomplete information may be found. To add the missing information, we can use knowledge graph completion, which is a method for finding missing or incorrect relationships to improve the quality of a knowledge graph. Knowledge graph completion can be carried out on a scholarly knowledge graph by adding new entities and relationships to produce further information in the scholarly knowledge graph. The data added to the scholarly knowledge graph are only other papers of first author entity, the research field of first author entity, and a description of the conference/journal entity. The result shows that the scholarly knowledge graph was completed by adding 81% correct data for other papers of first author entity, 80.3% correct data for the research field of first author entity, and 53.9% correct data for the description of the conference/journal.

AttenE: a knowledge graph completion model based on entity-relation attention embedding

Knowledge Graph Completion (KGC) aims at complementing missing relationships between entities in a Knowledge Graph (KG). While closed-world KGC approaches utilizing the knowledge within KG could only complement very limited number of missing relations, more and more approaches tend to get knowledge from open-world resources such as online encyclopedias and newswire corpus. For instance, a recent proposed open-world KGC model called ConMask learns embeddings of the entity’s name and parts of its text-description to connect unseen entities to the KG. However, this model does not make full use of the rich feature information in the text descriptions, besides, the proposed relationship-dependent content masking method may easily miss to find the target-words. In this paper, we propose to use a Multiple Interaction Attention (MIA) mechanism to model the interactions between the head entity description, head entity name, the relationship name, and the candidate tail entity descriptions, to form the enriched representations. Our empirical study conducted on two real-world data collections shows that our approach achieves significant improvements comparing to state-of-the-art KGC methods.

Knowledge Graph Completion (KGC) is an essential task aimed at mitigating the issue of incompleteness in knowledge graphs, thereby enhancing their utility for various downstream applications. Existing KGC models predominantly fall into two categories: structure-based and semantic-based approaches. Structure-based methods often encounter challenges with long-tail entities due to the scarcity of structural information and imbalanced entity distributions. Conversely, semantic-based methods, while addressing those limitations, necessitate extensive training of language models and specific finetuning for each knowledge graph, thus constraining their practical efficiency. To alleviate those limitations in both approaches, in this paper, we propose KICGPTv2, an innovative framework that synergizes a large language model (LLM) with traditional KGC methods. This integration effectively mitigates the long-tail entity problem without incurring significant additional training overhead. Central to the KICGPTv2 model is a novel in-context learning strategy, termed Knowledge Prompt, which encodes structural knowledge into demonstrations to effectively guide the LLM. Comprehensive evaluations on various KGC tasks, including link prediction, relation prediction, and triple classification, underscore the efficacy of the KICGPTv2 model, highlighting its ability to achieve competitive performance with reduced training demands and without the need for finetuning

MRGAT: Multi-Relational Graph Attention Network for knowledge graph completion

Natural language processing (NLP) is an important and challenging field of computer science. With the advent of computers and related technologies, NLP has grown out of the field of linguistics and is getting more attention in the computer field to support a wide variety of intelligent applications. NLP basically refers to automatic processing of natural languages by computers or machines in order to understand human language in written or spoken form. Ontologies are semantic models that provide general description of the types and structures of objects in a particular subject area or domain. Core components of ontology include concepts, relationships, instances, and axioms. Ontology is used to depict the hierarchical organization of concepts and their relationships. Knowledge graphs represent a set of interconnected descriptions of objects or concepts, and are sometimes referred to as semantic networks. In short, an ontology can be treated as a meta-model for data and knowledge graphs as a representation of the actual data or facts. An ontology (a formal description of relationships) plays a vital role in building a knowledge graph. Ontology and knowledge graphs have proven to be powerful tools to manage enterprise or big data. Today, a number of applications rely on ontology and knowledge graphs, such as knowledge management, information discovery, text extraction, semantic search, content-based recommendation, and intelligent chatbots. This chapter focuses on the importance of ontology and knowledge graphs for Developing NLP-based applications is difficult because computers require structured data, and human speech is highly complex, extremely diverse, unstructured in nature, and often ambiguous. In general, manual creation of knowledge graphs is expensive, therefore any automation of the process is highly desirable. Here, ontologies and knowledge graphs play a crucial role by providing a well-structured knowledge base, allowing semi-automatic creation of a lexicon to be used effectively in language translations, spellcheckers, keyword extraction and similar NLP applications.

Knowledge Graph Completion (KGC) is a popular topic in knowledge graph construction and related applications, aiming to complete the structure of knowledge graphs by predicting missing entities or relations and mining unknown facts in the knowledge graph. In the KGC task, Graph Neural Network (GNN)-based methods have achieved remarkable results due to their advantage of effectively capturing complex relations among entities and generating more accurate and rich entity representations by aggregating information from neighbouring nodes. These methods mainly focus on the representation of entities, and the representation of relations is obtained using simple dimensional transformations or initial embeddings. This treatment ignores the diversity and complex semantics of relations and restricts the efficiency of the model in utilizing relational information in the reasoning process. In this work, we propose the Relational Representation Augmented Graph Attention Network (RRA-GAT), which effectively identifies and weights neighbouring relations that actually contribute to the target relation by filtering out irrelevant information through an attention function based on the information and spatial domain. Furthermore, we capture complex patterns and features in the relational embedding by means of a feed-forward network consisting of a series of linear transformations and nonlinear activation functions. Experiments demonstrate the very advanced performance of RRA-GAT on the link prediction task on standard datasets FB15k-237 and WN18RR (e.g., improved the MRR metric on the WN18RR dataset by 7.8% relative improvement).

Sparsity is a challenge for knowledge graph completion. The negative sample space, which is much larger than the positive sample space, has many negative samples similar to the positive samples or potential positive samples, which affects the performance of the knowledge graph completion model. We propose a novel knowledge graph completion method, hard negative examples mining bilinear model (HNEMB). This model uses two components to model entities and relations, namely models relations into relation matrices and inverse relation matrices to learn latent inverse semantics of relations, and models entities into head vectors and tail vectors to learn different representations of entity in different positions. Then we calculate the bilinear scores of triples, we choose correct candidates based on the scores. To address the problem of sparsity of the knowledge graph, we first propose a method for mining hard negative examples for knowledge graph completion, to make model effectively capture the associations between head entities, relations and tail entities. Finally, experiments on multiple knowledge graph completion datasets shows that, as compare with baselines, our model achieves significant improvements and shows its advantages to model sparse knowledge graph.

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.

With the rise of multi-modal methods, multi-modal knowledge graphs have become a better choice for storing human knowledge. However, knowledge graphs often suffer from the problem of incompleteness due to the infinite and constantly updating nature of knowledge, and thus the task of knowledge graph completion has been proposed. Existing multi-modal knowledge graph completion methods mostly rely on either embedding-based representations or graph neural networks, and there is still room for improvement in terms of interpretability and the ability to handle multi-hop tasks. Therefore, we propose a new method for multi-modal knowledge graph completion. Our method aims to learn multi-level graph structural features to fully explore hidden relationships within the knowledge graph and to improve reasoning accuracy. Specifically, we first use a Transformer architecture to separately learn about data representations for both the image and text modalities. Then, with the help of multimodal gating units, we filter out irrelevant information and perform feature fusion to obtain a unified encoding of knowledge representations. Furthermore, we extract multi-level path features using a width-adjustable sliding window and learn about structural feature information in the knowledge graph using graph convolutional operations. Finally, we use a scoring function to evaluate the probability of the truthfulness of encoded triplets and to complete the prediction task. To demonstrate the effectiveness of the model, we conduct experiments on two publicly available datasets, FB15K-237-IMG and WN18-IMG, and achieve improvements of 1.8 and 0.7%, respectively, in the Hits@1 metric.

Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to cover as much knowledge as possible, while applying a KG to meet the needs of different industrial tasks, are two difficulties. This paper proposes a two-stage model construction strategy to benefit both knowledge graph completion and industrial tasks. Firstly, this paper summarizes the specific forms of multi-source data in industry and provides processing methods for each type of data. The core is to vectorize the data and align it conceptually, thereby achieving the fusion modeling of multi-source data. Secondly, this paper defines two interrelated subtasks to construct a pretrained language–knowledge graph model based on multi-task learning. At the same time, considering the dynamic characteristics of the production process, a dynamic expert network structure is adopted for different tasks combined with the pretrained model. In the knowledge completion task, the proposed model achieved an accuracy of 91.25%, while in the self-healing control task of a blast furnace, the proposed model reduced the incorrect actions rate to 0 and completed self-healing control for low stockline fault in 278 min. The proposed framework has achieved satisfactory results in experiments, which verifies the effectiveness of introducing knowledge into industry.