The field of open relation extraction (ORE) has recently observed significant advancement thanks to the growing capability of large language models (LLMs).Nevertheless, challenges persist when ORE is performed on specific topics.Existing methods give suboptimal results in five dimensions: factualness, topic relevance, informativeness, coverage, and uniformity.To improve topic-oriented ORE, we propose a zero-shot approach called Pri-ORE: Open Relation Extraction with a Priori seed generation.PriORE leverages the builtin knowledge of LLMs to maintain a dynamic seed relation dictionary for the topic.The dictionary is initialized by seed relations generated from topic-relevant entity types and expanded during contextualized ORE.PriORE then reduces the randomness in generative ORE by converting it to a more robust relation classification task.Experiments show the approach empowers better topic-oriented control over the generated relations and thus improves ORE performance along the five dimensions, especially on specialized and narrow topics. Dimension Extracted Relation Explanation Factualness gives toWrong given text and topic Relevance is exposed to Correct given text but not directly relevant to topic Informativeness form compound with Correct but conceptually too general given text and topic Coverage form powdery magnesium oxide Correct but too specific, covering too few instances Uniformity {be oxidized by, give electrons to, . . .} Multiple correct expressions extracted for the same relation

Massive amount of unstructured text data are generated daily, ranging from news articles to scientific papers. How to mine structured knowledge from the text data remains a crucial research question. Recently, large language models (LLMs) have shed light on the text mining field with their superior text understanding and instruction-following ability. There are typically two ways of utilizing LLMs: fine-tune the LLMs with human-annotated training data, which is labor intensive and hard to scale; prompt the LLMs in a zero-shot or few-shot way, which cannot take advantage of the useful information in the massive text data. Therefore, it remains a challenge on automated mining of structured knowledge from massive text data in the era of large language models.

Entity alignment aims to automatically align entities which are equivalent in the real world from different knowledge graphs (KGs). The task is challenged by the diversity of KGs, the multi-relational graph structures and limited seed entity pairs. Most existing GNN-based models do not distinguish the types or the directions of relations while the existing translation-based models merely rely on local semantics. To tackle the challenges, we propose a Multi-Role Graph Attention network (MRGA), which incorporates local semantics in triples with global structural information in graphs and exploits rich relational information effectively. MRGA devises a GAT-based layer which regards each entity as a combination of roles that it plays in different triples. Multifaceted entity semantics (i.e. roles), associated with different triples, are aggregated to enrich entity embeddings. An additional attention mechanism is applied to capture the correlation in relations of different KGs. The model is evaluated on three publicly available datasets. The results show the superior performance of MRGA compared with baseline methods and demonstrate the effectiveness of each component in MRGA via ablation study.

Text generation is one of the essential yet challenging tasks in natural language processing. However, the input text alone is usually hard to provide enough information to generate the desired output. Previous work attempts to incorporate syntactic information into the generative models based on variational autoencoder(VAE). But these methods have difficulty in adequately modeling the tree structure of syntactic data. In this paper, we formulate the syntactic structure as a graph and introduce a syntax encoder based on graph neural network(GNN) to model the syntactic information of sentences. Based on the syntax encoder, we propose a novel syntax-enhanced variational autoencoder(SEVAE) with two variants. The variant SEVAE-m merges sentence information and syntactic information into one latent space to enrich the fine-grained syntactic information of latent representations. And the variant SEVAE-s with two separate latent spaces allows the sentence decoder to dynamically attend to semantic and syntactic information from two latent variables. Experiments on two benchmark datasets show that our methods achieve significant and consistent improvements compared with previous work.