Knowledge graphs have emerged as a sophisticated advancement and refinement
of semantic networks, and their deployment is one of the critical methodologies
in contemporary artificial intelligence. The construction of knowledge graphs
is a multifaceted process involving various techniques, where researchers aim
to extract the knowledge from existing resources for the construction since
building from scratch entails significant labor and time costs. However, due to
the pervasive issue of heterogeneity, the description diversity across
different knowledge graphs can lead to mismatches between concepts, thereby
impacting the efficacy of knowledge extraction. This Ph.D. study focuses on
automatic knowledge graph extension, i.e., properly extending the reference
knowledge graph by extracting and integrating concepts from one or more
candidate knowledge graphs. We propose a novel knowledge graph extension
framework based on entity type recognition. The framework aims to achieve
high-quality knowledge extraction by aligning the schemas and entities across
different knowledge graphs, thereby enhancing the performance of the extension.
This paper elucidates three major contributions: (i) we propose an entity type
recognition method exploiting machine learning and property-based similarities
to enhance knowledge extraction; (ii) we introduce a set of assessment metrics
to validate the quality of the extended knowledge graphs; (iii) we develop a
platform for knowledge graph acquisition, management, and extension to benefit
knowledge engineers practically. Our evaluation comprehensively demonstrated
the feasibility and effectiveness of the proposed extension framework and its
functionalities through quantitative experiments and case studies.

本文提出了一种基于实体类型识别的知识图谱扩展框架，通过对不同知识图谱的模式和实体进行对齐，提高了扩展的性能，从而实现高质量的知识提取。通过定量实验和案例研究，全面展示了所提出的扩展框架及其功能的可行性和有效性。

知识图谱扩展 —— 通过实体类型识别

Knowledge Graph Extension by Entity Type Recognition

Training a Named Entity Recognition (NER) model often involves fixing a
taxonomy of entity types. However, requirements evolve and we might need the
NER model to recognize additional entity types. A simple approach is to
re-annotate entire dataset with both existing and additional entity types and
then train the model on the re-annotated dataset. However, this is an extremely
laborious task. To remedy this, we propose a novel approach called Partial
Label Model (PLM) that uses only partially annotated datasets. We experiment
with 6 diverse datasets and show that PLM consistently performs better than
most other approaches (0.5 - 2.5 F1), including in novel settings for taxonomy
expansion not considered in prior work. The gap between PLM and all other
approaches is especially large in settings where there is limited data
available for the additional entity types (as much as 11 F1), thus suggesting a
more cost effective approaches to taxonomy expansion.

本研究提出了一种称为 “部分标签模型（Partial Label Model）” 的新方法，该方法可以使用部分有标注的数据集来训练 NER 模型，以更节省成本地扩展分类法，实验表明，该方法在新分类法设置下的表现显著优于其他方法，并在额外实体类型数据有限的情况下表现出更加节省成本。

命名实体识别的分类扩展

Taxonomy Expansion for Named Entity Recognition

The mainstream approach to the development of ontologies is merging
ontologies encoding different information, where one of the major difficulties
is that the heterogeneity motivates the ontology merging but also limits
high-quality merging performance. Thus, the entity type (etype) recognition
task is proposed to deal with such heterogeneity, aiming to infer the class of
entities and etypes by exploiting the information encoded in ontologies. In
this paper, we introduce a property-based approach that allows recognizing
etypes on the basis of the properties used to define them. From an
epistemological point of view, it is in fact properties that characterize
entities and etypes, and this definition is independent of the specific labels
and hierarchical schemas used to define them. The main contribution consists of
a set of property-based metrics for measuring the contextual similarity between
etypes and entities, and a machine learning-based etype recognition algorithm
exploiting the proposed similarity metrics. Compared with the state-of-the-art,
the experimental results show the validity of the similarity metrics and the
superiority of the proposed etype recognition algorithm.

本文介绍了一种基于属性的方法，用于识别本体中的实体类型，通过提供一组基于属性的度量来衡量实体类型与实体之间的上下文相似度，并利用机器学习算法来识别实体类型的有效性和优越性。