A ctive EA : Active Learning for Neural Entity Alignment (2021)

Abstract: Entity Alignment (EA) aims to match equivalent entities across different Knowledge Graphs (KGs) and is an essential step of KG fusion. Current mainstream methods – neural EA models – rely on training with seed alignment, i.e., a set of pre-aligned entity pairs which are very costly to annotate. In this paper, we devise a novel Active Learning (AL) framework for neural EA, aiming to create highly informative seed alignment to obtain more effective EA models with less annotation cost. Our framework tackles two main challenges encountered when applying AL to EA: (1) How to exploit dependencies between entities within the AL strategy. Most AL strategies assume that the data instances to sample are independent and identically distributed. However, entities in KGs are related. To address this challenge, we propose a structure-aware uncertainty sampling strategy that can measure the uncertainty of each entity as well as its impact on its neighbour entities in the KG. (2) How to recognise entities that appear in one KG but not in the other KG (i.e., bachelors). Identifying bachelors would likely save annotation budget. To address this challenge, we devise a bachelor recognizer paying attention to alleviate the effect of sampling bias. Empirical results show that our proposed AL strategy can significantly improve sampling quality with good generality across different datasets, EA models and amount of bachelors.

## introduction knowledge graphs (kgs) store entities and their relationships with a graph structure and are used as knowledge drivers in many applications @xcite . existing kgs are often incomplete but complementary to each other. a popular approach used to tackle this problem is kg fusion, which attempts to combine several kgs into a single, comprehensive one. entity alignment (ea) is an essential step for kg fusion: it identifies equivalent entities across different kgs, supporting the unification of their complementary knowledge. for example, in fig. 1 donald trump and us in the first kg correspond to d.j. trump and america respectively in the second kg. by aligning them, the political and business knowledge about donald trump can be integrated within one kg. neural models @xcite @xcite are the current stateof-the-art in ea and are capable of matching entities in an end-to-end manner. typically, these neural ea models rely on a seed alignment as training data which is very labour-intensive to annotate. however, previous ea research has assumed the availability of such seed alignment and ignored the cost involved with their annotation. in this paper, we seek to reduce the cost of annotating seed alignment data, by investigating methods capable of selecting the most informative entities for labelling so as to obtain the best ea model with the least annotation cost: we do so using active learning. active learning (al) @xcite is a machine learning (ml) paradigm where the annotation of data and the training of a model are performed iteratively so that the sampled data is highly informative for training the model. though many general al strategies have been proposed @xcite , there are some unique challenges in applying al to ea. the first challenge is how to exploit the dependencies between entities. in the ea task, neighbouring entities (context) in the kgs naturally affect each other. for example, in the two kgs of fig. 1 , we can infer us corresponds to america if we already know that donald trump and d.j. trump refer to the same person: this is because a single person can only be the president of one country. therefore, when we estimate the value of annotating an entity, we should consider its impact on its context in the kg. most al strategies assume data instances are independent, identically distributed and cannot capture dependencies between entities @xcite . in addition, neural ea models exploit the structure of kgs in different and implicit ways @xcite . it is not easy to find a general way of measuring the effect of entities on others. the second challenge is how to recognize the entities in a kg that do not have a counterpart in the other kg (i.e., bachelors). in the first kg of fig. 1 , donald trump and us are matchable entities while new york city and republican party are bachelors. selecting bachelors to annotate will not lead to any aligned entity pair. the impacts of recognizing bachelors are twofold: to address these challenges, we propose a novel al framework for ea. our framework follows the typical al process: entities are sampled iteratively, and in each iteration a batch of entities with the highest acquisition scores are selected. our novel acquisition function consists of two components: a structure-aware uncertainty measurement module and a bachelor recognizer. the structure-aware uncertainty can reflect the uncertainty of a single entity as well as the influence of that entity in the context of the kg, i.e., how many uncertainties it can help its neighbours eliminate. in addition, we design a bachelor recognizer, based on graph convolutional networks (gcns). because the bachelor recognizer is trained with the sampled data and used to predict the remaining data, it may suffer from bias (w.r.t. the preference of sampling strategy) of these two groups of data. we apply model ensembling to alleviate this problem. our major contributions in this paper are: ## conclusion entity alignment is an essential step for kg fusion. current mainstream methods for ea are neural models, which rely on seed alignment. the cost of labelling seed alignment is often high, but how to reduce this cost has been neglected. in this work, we proposed an active learning framework (named activeea), aiming to produce the best ea model with the least annotation cost. specifically, we attempted to solve two key challenges affecting ea that general al strategies cannot deal with. firstly, we proposed a structure-aware uncertainty sampling, which can combine uncertainty sampling with the structure information of kgs. secondly, we designed a bachelor recognizer, which reduces annotation budget by avoiding the selection of bachelors. specially, it can tolerate sampling biases. extensive experimental showed activeea is more effective than the considered baselines and has great generality across different datasets, ea models and bachelor percentages. in future, we plan to explore combining active learning and self-training which we believe are complementary approaches. self-training can generate extra training data automatically but suffers from incorrectly labelled data. this can be addressed by amending incorrectly labelled data using al strategies.