Tele-Knowledge Pre-training for Fault Analysis

In this work, we share our experience on tele-knowledge pre-training for fault analysis. Fault analysis is a vital task for tele-application, which should be timely and properly handled. Fault analysis is also a complex task, that has many sub-tasks. Solving each task requires diverse tele-knowledge. Machine log data and product documents contain part of the tele-knowledge. We create a Tele-KG to organize other tele-knowledge from experts uniformly. With these valuable tele-knowledge data, in this work, we propose a tele-domain pre-training model KTeleBERT and its knowledge-enhanced version KTeleBERT, which includes effective prompt hints, adaptive numerical data encoding, and two knowledge injection paradigms. We train our model in two stages: pre-training TeleBERT on 20 million telecommunication corpora and re-training TeleBERT on 1 million causal and machine corpora to get the KTeleBERT. Then, we apply our models for three tasks of fault analysis, including root-cause analysis, event association prediction, and fault chain tracing. The results show that with KTeleBERT, the performance of task models has been boosted, demonstrating the effectiveness of pre-trained KTeleBERT as a model containing diverse tele-knowledge.

Schema-aware Reference as Prompt Improves Data-Efficient Relational Triple and Event Extraction

Information Extraction, which aims to extract structural relational triple or event from unstructured texts, often suffers from data scarcity issues. With the development of pre-trained language models, many prompt-based approaches to data-efficient information extraction have been proposed and achieved impressive performance. However, existing prompt learning methods for information extraction are still susceptible to several potential limitations: (i) semantic gap between natural language and output structure knowledge with pre-defined schema; (ii) representation learning with locally individual instances limits the performance given the insufficient features. In this paper, we propose a novel approach of schema-aware Reference As Prompt (RAP), which dynamically leverage schema and knowledge inherited from global (few-shot) training data for each sample. Specifically, we propose a schema-aware reference store, which unifies symbolic schema and relevant textual instances. Then, we employ a dynamic reference integration module to retrieve pertinent knowledge from the datastore as prompts during training and inference. Experimental results demonstrate that RAP can be plugged into various existing models and outperforms baselines in low-resource settings on five datasets of relational triple extraction and event extraction. In addition, we provide comprehensive empirical ablations and case analysis regarding different types and scales of knowledge in order to better understand the mechanisms of RAP. Code is available in https://github.com/zjunlp/RAP.

Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study

This paper presents an empirical study to build relation extraction systems in low-resource settings. Based upon recent pre-trained language models, we comprehensively investigate three schemes to evaluate the performance in low-resource settings: (i) different types of prompt-based methods with few-shot labeled data; (ii) diverse balancing methods to address the long-tailed distribution issue; (iii) data augmentation technologies and self-training to generate more labeled in-domain data. We create a benchmark with 8 relation extraction (RE) datasets covering different languages, domains and contexts and perform extensive comparisons over the proposed schemes with combinations. Our experiments illustrate: (i) Though prompt-based tuning is beneficial in low-resource RE, there is still much potential for improvement, especially in extracting relations from cross-sentence contexts with multiple relational triples; (ii) Balancing methods are not always helpful for RE with long-tailed distribution; (iii) Data augmentation complements existing baselines and can bring much performance gain, while self-training may not consistently achieve advancement to low-resource RE. Code and datasets are in https://github.com/zjunlp/LREBench.

Relational Message Passing for Fully Inductive Knowledge Graph Completion

In knowledge graph completion (KGC), predicting triples involving emerging entities and/or relations, which are unseen when the KG embeddings are learned, has become a critical challenge. Subgraph reasoning with message passing is a promising and popular solution. Some recent methods have achieved good performance, but they (i) usually can only predict triples involving unseen entities alone, failing to address more realistic fully inductive situations with both unseen entities and unseen relations, and (ii) often conduct message passing over the entities with the relation patterns not fully utilized. In this study, we propose a new method named RMPI which uses a novel Relational Message Passing network for fully Inductive KGC. It passes messages directly between relations to make full use of the relation patterns for subgraph reasoning with new techniques on graph transformation, graph pruning, relation-aware neighborhood attention, addressing empty subgraphs, etc., and can utilize the relation semantics defined in the ontological schema of KG. Extensive evaluation on multiple benchmarks has shown the effectiveness of techniques involved in RMPI and its better performance compared with the existing methods that support fully inductive KGC. RMPI is also comparable to the state-of-the-art partially inductive KGC methods with very promising results achieved. Our codes and data are available at https://github.com/zjukg/RMPI.

Multimodal Analogical Reasoning over Knowledge Graphs

Analogical reasoning is fundamental to human cognition and holds an important place in various fields. However, previous studies mainly focus on single-modal analogical reasoning and ignore taking advantage of structure knowledge. Notably, the research in cognitive psychology has demonstrated that information from multimodal sources always brings more powerful cognitive transfer than single modality sources. To this end, we introduce the new task of multimodal analogical reasoning over knowledge graphs, which requires multimodal reasoning ability with the help of background knowledge. Specifically, we construct a Multimodal Analogical Reasoning dataSet (MARS) and a multimodal knowledge graph MarKG. We evaluate with multimodal knowledge graph embedding and pre-trained Transformer baselines, illustrating the potential challenges of the proposed task. We further propose a novel model-agnostic Multimodal analogical reasoning framework with Transformer (MarT) motivated by the structure mapping theory, which can obtain better performance.

PromptKG: A Prompt Learning Framework for Knowledge Graph Representation Learning and Application

Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information. KG representation models should consider graph structures and text semantics, but no comprehensive open-sourced framework is mainly designed for KG regarding informative text description. In this paper, we present PromptKG, a prompt learning framework for KG representation learning and application that equips the cutting-edge text-based methods, integrates a new prompt learning model and supports various tasks (e.g., knowledge graph completion, question answering, recommendation, and knowledge probing). PromptKG is publicly open-sourced at https://github.com/zjunlp/PromptKG with long-term technical support.

Construction and Applications of Open Business Knowledge Graph

Business Knowledge Graph is important to many enterprises today, providing the factual knowledge and structured data that steer many products and make them more intelligent. Despite the welcome outcome, building business KG brings prohibitive issues of deficient structure, multiple modalities and unmanageable quality. In this paper, we advance the practical challenges related to building KG in non-trivial real-world systems. We introduce the process of building an open business knowledge graph (OpenBG) derived from a well-known enterprise. Specifically, we define a core ontology to cover various abstract products and consumption demands, with fine-grained taxonomy and multi-modal facts in deployed applications. OpenBG is ongoing, and the current version contains more than 2.6 billion triples with more than 88 million entities and 2,681 types of relations. We release all the open resources (OpenBG benchmark) derived from it for the community. We also report benchmark results with best learned lessons \url{https://github.com/OpenBGBenchmark/OpenBG}.

Neural-Symbolic Entangled Framework for Complex Query Answering

Answering complex queries over knowledge graphs (KG) is an important yet challenging task because of the KG incompleteness issue and cascading errors during reasoning. Recent query embedding (QE) approaches to embed the entities and relations in a KG and the first-order logic (FOL) queries into a low dimensional space, answering queries by dense similarity search. However, previous works mainly concentrate on the target answers, ignoring intermediate entities' usefulness, which is essential for relieving the cascading error problem in logical query answering. In addition, these methods are usually designed with their own geometric or distributional embeddings to handle logical operators like union, intersection, and negation, with the sacrifice of the accuracy of the basic operator - projection, and they could not absorb other embedding methods to their models. In this work, we propose a Neural and Symbolic Entangled framework (ENeSy) for complex query answering, which enables the neural and symbolic reasoning to enhance each other to alleviate the cascading error and KG incompleteness. The projection operator in ENeSy could be any embedding method with the capability of link prediction, and the other FOL operators are handled without parameters. With both neural and symbolic reasoning results contained, ENeSy answers queries in ensembles. ENeSy achieves the SOTA performance on several benchmarks, especially in the setting of the training model only with the link prediction task.

Aspect-based Sentiment Classification with Sequential Cross-modal Semantic Graph

Multi-modal aspect-based sentiment classification (MABSC) is an emerging classification task that aims to classify the sentiment of a given target such as a mentioned entity in data with different modalities. In typical multi-modal data with text and image, previous approaches do not make full use of the fine-grained semantics of the image, especially in conjunction with the semantics of the text and do not fully consider modeling the relationship between fine-grained image information and target, which leads to insufficient use of image and inadequate to identify fine-grained aspects and opinions. To tackle these limitations, we propose a new framework SeqCSG including a method to construct sequential cross-modal semantic graphs and an encoder-decoder model. Specifically, we extract fine-grained information from the original image, image caption, and scene graph, and regard them as elements of the cross-modal semantic graph as well as tokens from texts. The cross-modal semantic graph is represented as a sequence with a multi-modal visible matrix indicating relationships between elements. In order to effectively utilize the cross-modal semantic graph, we propose an encoder-decoder method with a target prompt template. Experimental results show that our approach outperforms existing methods and achieves the state-of-the-art on two standard datasets MABSC. Further analysis demonstrates the effectiveness of each component and our model can implicitly learn the correlation between the target and fine-grained information of the image.

DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning

Zero-shot learning (ZSL) aims to predict unseen classes whose samples have never appeared during training, often utilizing additional semantic information (a.k.a. side information) to bridge the training (seen) classes and the unseen classes. One of the most effective and widely used semantic information for zero-shot image classification are attributes which are annotations for class-level visual characteristics. However, due to the shortage of fine-grained annotations, the attribute imbalance and co-occurrence, the current methods often fail to discriminate those subtle visual distinctions between images, which limits their performances. In this paper, we present a transformer-based end-to-end ZSL method named DUET, which integrates latent semantic knowledge from the pretrained language models (PLMs) via a self-supervised multi-modal learning paradigm. Specifically, we (1) developed a cross-modal semantic grounding network to investigate the model's capability of disentangling semantic attributes from the images, (2) applied an attribute-level contrastive learning strategy to further enhance the model's discrimination on fine-grained visual characteristics against the attribute co-occurrence and imbalance, and (3) proposed a multi-task learning policy for considering multi-model objectives. With extensive experiments on three standard ZSL benchmarks and a knowledge graph equipped ZSL benchmark, we find that DUET can often achieve state-of-the-art performance, its components are effective and its predictions are interpretable.