Attribute extraction aims to identify attribute names and the corresponding values from descriptive texts, which is the foundation for extensive downstream applications such as knowledge graph construction, search engines, and e-Commerce.In previous studies, attribute extraction is generally treated as a classification problem for predicting attribute types or a sequence tagging problem for labeling attribute values, where two paradigms, i.e., closed-world and open-world assumption, are involved.However, both of these paradigms have limitations in terms of real-world applications.And prior studies attempting to integrate these paradigms through ensemble, pipeline, and co-training models, still face challenges like cascading errors, high computational overhead, and difficulty in training.To address these existing problems, this paper presents Attribute Tree, a unified formulation for realworld attribute extraction application, where closed-world, open-world, and semi-open attribute extraction tasks are modeled uniformly.Then a text-to-tree generation model, AtTGen, is proposed to learn annotations from different scenarios efficiently and consistently.Experiments demonstrate that our proposed paradigm well covers various scenarios for real-world applications, and the model achieves state-ofthe-art, outperforming existing methods by a large margin on three datasets.Our code, pretrained model, and datasets are available at https://github.com/lsvih/AtTGen.

As a powerful expression of human knowledge in a structural form, knowledge graph (KG) has drawn great attention from both the academia and the industry and a large number of construction and application technologies have been proposed. Large-scale knowledge graphs such as DBpedia, YAGO and Wikidata are published and widely used in various tasks. However, most of them are far from perfect and have many quality issues. For example, they may contain inaccurate or outdated entries and do not cover enough facts, which limits their credibility and further utility. Data quality has a long research history in the field of traditional relational data and recently attracts more knowledge graph experts. In this paper, we provide a systematic and comprehensive review of the quality management on knowledge graphs, covering overall research topics about not only quality issues, dimentions and metrics, but also quality management processes from quality assessment and error detection, to error correction and KG completion. We categorize existing works in terms of target goals and used methods for better understanding. In the end, we discuss some key issues and possible directions on knowledge graph quality management for further research.

Paraphrase, i.e., differing textual realizations of the same meaning, has proven useful for many natural language processing (NLP) applications. Collecting paraphrase for predicates in knowledge bases (KBs) is the key to comprehend the RDF triples in KBs. Existing works have published some paraphrase datasets automatically extracted from large corpora, but have too many redundant pairs or don't cover enough predicates, which cannot be improved by computer only and need the help of human beings. This paper shows a full process of collecting large-scale and high-quality paraphrase dictionaries for predicates in knowledge bases, which takes advantage of existing datasets and combines the technologies of machine mining and crowdsourcing. Our dataset comprises 2284 distinct predicates in DBpedia and 31130 paraphrase pairs in total, the quality of which is a great leap over previous works. Then it is demonstrated that such good paraphrase dictionaries can do great help to natural language processing tasks such as question answering and language generation. We also publish our own dictionary for further research.