Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training
Authors: Hongmin Cai
Wenxiong Liao
Zhengliang Liu
Yuzhong Chen
Tianming Liu
Xiang Li
Venue: Submitted to ICLR 2023 (2023)
Keywords: Knowledge Graph Domain Adaptation
Knowledge Graph Construction
Named Entity Recognition
Relationship Extraction

Abstract: Modern supervised learning neural network models require a large amount of manually labeled data, which makes the construction of domain-specific knowledge graphs time-consuming and labor-intensive. In parallel, although there has been much research on named entity recognition and relation extraction based on distantly supervised learning, constructing a domain-specific knowledge graph from large collections of textual data without manual annotations is still an urgent problem to be solved. In response, we propose an integrated framework for adapting and re-learning knowledge graphs from one coarse domain (biomedical) to a finer-define domain (oncology). In this framework, we apply distant-supervision on cross-domain knowledge graph adaptation. Consequently, no manual data annotation is required to train the model. We introduce a novel iterative training strategy to facilitate the discovery of domain-specific named entities and triples.  Experimental results indicate that the proposed framework can perform domain adaptation and construction of knowledge graph efficiently.

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COARSE-TO-HINE KNOWLEDGE GRAPH DOMAIN ADAPTATION BASED ON DISTANTLY-SUPERVISHR ITERATIVE TRAINING
Anonymous authors Paper under double-bling boindd review
ABSTRACT
Modern supervised learning neural network models require large amount of manually labeled data, which makes the construction of domain-specific knowl- edge graphs time-consuming and labor-intensive. In parallel, although there has been much research on named entity recognition and relation extraction based on distantly supervised learning, constructing a domain-speciffe knowledge graph from large collections of textual data without manual annotations is still an urgent problem to be solved. In response, ing and re-learning knowledge graphs from one coarse domain (biomedical) to a finer-define domain (oncology). In this framework, apply distant-supervision on cross-domain knowledge graph adaptation. Consequently, no manual data an- notation is required to train the model. We introduce a novel iterative training strategy to facilitate the discovery of domain-specific named entities and triples. Experimental results indicate that the proposed framework can perform domain adaptation and construction of knowledge graph efficiently.
propose integrated framework for adapt-
INTRODUCTION
The triples in the knowledge graph (KG) contain the relationships between various entities, pro- viding rich semantic background knowledge for various natural language processing (NLP) tasks, such natural language representation Liu et al. (2020), question answering Saxena et al. 22220) image captioning /hhang et al. (2021a), and text classification Jiang et al. (2020). Consequently, au-
tomatically constructing knowledge graphs directly from natural texts has attracted close attentions of searchrss in recent years Kertkeidkachorn & Ichise (2017): Rossanez et al. (2020); Stewart &
Liu 2020).
KG construction from text generally involves two primitive steps: named entity recognition (NER) and relation extraction (RE). Named entity recognition aims to identify the types of entities men- tioned in text sequences, such as people, place, etc. in the open domain; or disease, medicine, disease symptom, etc. in the biomedical domain. The relation extraction also known triple extraction, aims to identify the relationship between two entities, such as the birthplace relationship between people and places in the open domain; or the therapeutic relationship between drug and discase in the biomedical domain. NER and RE are necessary steps for information extraction to construct KG from the text. In addition to NER and RE, constructing a KG usually includes other steps such as coreference resolution, entity linking, knowledge fusion, and ontology extraction. In order to facilitate model evaluation, this paper mainly focuses on information extraction and then constructs a KG.
In the construction of fine-domain KG scenarios, there are usually some existing resources available, such biomedical KGs in coarse domains, which generally cover broader concepts and more com- monsense knowledge. When constructing the oncology KG, the biomedical KG is thus available. However, few studies have focused adapting KG from the coarse domain (e.g., biomedical) the fine domain (e.g., oncology) where large collection of unlabeled textual data are available, which motivates the work in this paper.
Distant supervision Smirnova & Cudré Mauroux (0018) is an intuitive way to transfer coarse- domain KG fine domains. Distant-supervision provides labels for data with the help of an external

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knowledge base, which saves the time of manual labeling. For distantly-superviseer NER, build distant labels by matching unlabeled sentences with external semantic dictionaries or knowl- edge bases. The matching strategies usually include string matching Zhao et al. (2019), regular expressions Fries et al. 2017), and some heuristic rules. The distantly-supervee sumption Mintz et al. (200)): if two entities participate in relation, then any sentence that contains those two entities might express that relation. Following this assumption, any sentence mentioning a pair of entities that have relation according the knowledge base will be labeled with this relation 2018).
RE holds an as-
Smirnova &
Therefore, the KG in the coarse domain can be potentially used as a knowledge base for distant supervision, thus avoiding a large number of manual annotations. However, only using the KG of the coarse domain as the knowledge base might limit the model's ability to discover domain- -peccic named entities and triples in the fine domain, which further limits the construction of the fine domain KG. To address these problems, in this paper, we propose a novel coarse-to-fine knowledge graph domain adaptation (KGDA) framework. Our KGDA framework utilizes an iterative training strategy to enhance the model's ability to discover fine-domain entities and triples, thereby facilitating fast and effective coarse-to- to-see KG domain adaptation.
Overall, the contributions our work are as follows:
An integrated framework for adapting and re-learning KG from coarse-domain to fine- domain is proposed. As a case study, the biomedical domain and oncology domain are considered the coarse domain and fine domain, respectively.
Our model does not require human annotated samples with distant-supervision for cross- domain KG adaptation, and the iterative training strategy is applied to discovering domain- specific named entities and new triples.
The proposed method can be adapted to various pre-trained language models (PLMs) and can be easily applied to different coarse-to-fine KGDA tasks. It so far the simplest data- driven approach for learning KG from free text data, with the help of the coarse domain KG.
Experimental results demonstrate the effectiveness of the proposed KGDA framework. We will release the source code and the data used in this work to fuel further research. The constructed oncology KG will be hosted as a web service to be used by the general public.
RELATED WORK
2.1 PIPELINE- BBASED METHODS FOR KG CONSTRUCTION
The pipeline- based methods apply carefully-craffee linguistic and statistical patterns extract the co-occurred noun phrases as triples. There are many off-the-sheel toolkits available, for example, Stanford CoreNLP Manning et al. 2014), NLTK Thanaki (2017), and spaCy, which can be used for the NER tasks; Reveb Fader et al. 2011). OLLIE Schmitzz et al. (2012), and Stanford OpenlE An- OLLIE|Schmitz
geli et al. (2015) can be used for the information extraction task. There have been multiple pipelines Mehta et (2019) Rossanez et al. (2020) developed as well, consisting of modules targeting differ- ent functionalities needed for the KG construction. However, the pre-defined rules of off-the-shelf toolkits are generally tailored to specific domains, such methods are not domain-agnostic and new set of rules will be needed for a new domain.
2.2 DATA-DRIVEN METHODS FOR KG CONSTRUCTION
With the development of representation learning in language models, researchers began to apply data-driven models to solve the KG construction tasks. Based on how the model is trained, these works can be divided into three categories: fully-supervised methods Zhao et al. (2019): Li et al. 2002b), semi-supervised methods Zahera et al. (2021). and weakly-supervised methods Yu et al. 2021). We will introduce the methods of suleyviuee and seakkly-supervised
this section. Specifically, the NER, RE, and entity linking tasks in the KG construction pipeline can all be solved by fully-super supervisd learning methods such as long short-term memory neural network (LSTM) Hoccreiter & Schmidhuber (1997); Zeng al. (201)). Graph neural network methods have also
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Disurily spperssoo NER
Enity naiding
Desaatty-srpertoon cxppo
NER model
NEX moded mmining
Fine-donam
NER prodiction
New extilies
Coufiden satlees
Finc-derain KG
Pesnicssigg
Oct spacifii koen
AE proliction
New tiplo
Coniiiicn piplex
Haild KG
RE REis
REnoski
par
Deunih--speeessn XB ourpo
Oxellayeg
Figure 1: The overall framework of iterative training KGDA.
been applied for Nomain--specife NER tasks Chen et al. 2202 and document-level RE Zhang et al. (2020). The bidirectional encoder representation from transformers (BERT) Kenton & Toutanova (2019). the widely-used PLM, can also tackle the NER Tia al. (2020), RE Roy & Pan (2021), and entity linking [i et al. (2022a) tasks. While the advancement of deep learning-based based methods has greatly improved the effectiveness of KG construction. sully-supervised learning requires a large amount of making it difficult to transfer the KG construction work to a new domain, and ultimately limiting the scalability and efficiency of the research in KG.
data text. Furthermore, the annotation only be Nomain-speciffe
On the other hand, distant supervision, a weakly supervised learning method, can replace manual annotation with existing and remote knowledge base. Previous studies have applied remote super- vised learning to deal with NER Zheng et al. (202I), and RE Wei 1) Zhang et al. (2021b) tasks. Thus in this work, we adopted the distant- superviiin scheme the proposed KGDA framework. should be noted that KG of the coarse domain (e.g.. biomedical) generally will not contain the com- plete knowledge of its finer sub-domains (e.g., oncology). So when we use the coarse-domain KG for distant supervision, labels of the target domain will be limited by the source domain, making it less effective to discover new knowledge. To address this issue, we introduced an iterative strategy to gradually update the model via distant supervision while at the same time using the partially-trained model to discover new entities and relations from the data of the target fine domain.
METHODOLOGY
3.1 NOTATION AND TASK DEFINITION
An unstructured sentence = [u1, W2, W...... W..] indicates sequence of tokens, where n is its
length. A dataset is a collection of unstructured sentences (i.e. knowledge graph, denoted as K, is collection of triples l. = (ei, Tj, ex), where e; E E and E E are the head entity and the tail entity respectively, and r, E V is the relation between e, and eg. Here denote coarse-domain KG as K, and fine-domain KG Ky,
(s1 (2,, S3. sm)) }). The $2,
In a typical scenario of KG domain adaptation, we will have an existing coarse-domain KG and a large amount of unlabeled text in the fine domain. For example, when constructing the oncology KG, we can utilize the existing biomedical KG and collect oncology-relatee KG constructed from the fine domain data would then include overlapping triples with the coarse- domain KG and new triples representing domain specifcc knowledge. Specifically, the fine-domain KG contains the following three types of triples:
literature as unlabeled text.
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Overlapping triples To: Triples that also existed in the coarse-domain KG, indicating knowledge overlapping between the coarse and fine domains.
Triples of new relations but overlapping entities TR: Triples with both entity pairs ex- isting in the coarse-domain KG but no indicated relationships between these entity pairs.
Triples of new entities Tg: Triples with at least one entity not existing in the coarse- domain KG. Consequently, the relationship is also unknown in the coarse domain.
Both Tn and TE belong to the specific knowledge of the fine domain. The goal of the coarse- to-fine KGDA task is to adapt the KG from the coarse domain to the fine domain and leverage the knowledge from the coarse domain to guide the mining of new knowledge specific to the fine domain. Finally, we will keep the definition of entity types and relation types from coarse-domain KG when constructing the fine-domain KG.
3.2 ITERATIVE TRAINING FRAMEWORK
While it is trivial to identify the overlapping entities Eo and triples To by distant supervision, if the NER and RE models are trained on the entire corpus, they will not be able to recognize the fine nomain-speciff named entities and triples (Tx a and g). Because the sustant-supervision labels are generated by matching K.,. Thus we introduce an iterative training strategy to construct T, and T from the text and adapt the knowledge from K2 to K,.
The overall framework of the iterative training scheme is shown in Fig. and the detailed pseudo code can be found in Algorithm [ Rather than performing distant-supervin supervision training on the whole unlabeled text corpus, the core mechanism of the proposed iterative training is to split the whole un- labeled dataset into n sub-datasets without intersection. Before building distant-supervise the trained model is used predict the text corpus for getting specific knowledge of fine-domain, which is conducive to mining Ta and Tg of the fine-domain.
corpus,
As shown in Figure I1 firstly, it is necessary preprocess the acquired text corpus in the fine do- main. Preprocessing operations include: handling special characters, word segmentation, filtering sentences using human-defined rules (such as sentence length), etc. 'hhn, our framework involves two neural network models: NER model and RE model. We replace the PLM's output layer with classifier head as NER model modely and fine-tune it by minimizing the cross-entropy loss on distant-supervision NER corpus. Additionally, we apply the BIO scheme Li et al. (2012) to gen- erate NER sequence labels. For the RE task, we use the template to generate Distant-supervisg samples. The template we adopted is "[CLS] head entity (head entity type) [SEP] tail entity (tail entity type) [SEP] sentence". The RE model modeR is defined as PLM with a fully con- nected layer as a relation classifier. The feature of special token [CLS] fed into this fully connected layer and fine-tune mode R by minimizing the cross-entropy loss on supervi-u RE corpus.
We summarize the steps to achieve KGDA in Algorithm I Di. the distant-supervision method is applied to construct the NER training corpus corpy and RE training corpus corPR and the NER model modely and RE model mode are trained based on corpus corpy and corPA, respectively. For other part of the text corpus D, we apply the previously trained modely and modeR to extract the entities and triples in the fine-domain, and select the high confidence entities F and high confidence triples Teonf as the specific knowledge of the fine-domain (line 7). Then, we take Kz, Beonf, and Teanf as the external knowledge base for constructing distant-supervision corpN and corpR (line 8). Finally, we use overlapping triples To and high-confidencee triples Teonf to construct knowledge graph of fine domains (line 17).
For the first parts of the text corpus
Next, we show the details of get.ddstanttcorry in Algorithm 2 and getsspecific. knowledge in Al-
gorithm 3
3.3 CONSTRUCTING DISTANTLY-SSPERVEEE CORPUS
Through
we can only match entity pairs that have a relationship and use them as positive samples. We then construct negative samples with NULL relationship by the following two schemes: 1) randomly sampling two entities which have relationship domain; 2) randomly sampling a word from out-of-domain words (i.c., a word that is not an entity as defined in the coarse domain) Wo one of the entities. The parameter ratio, controls the ratio
defined in the coarse-
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Algorithm Iterative training KGDA framework
Input: Text corpus D { Di,,... J. coarse-domain KG Ko out-of-domain words Wo Parameter: Initialized NER model modely. initialized RE model modelR
Output: fine-domain kg ,
1: Let new entities Bnew = 0 new entities with high confidence Econn = 0 new triples Tnew = O new triples with high confidence Teon =
2: corpN, corpn- Eo. To= build distant D1, Ke, Beonf Teomf Wo )
3: train NER(modely, corpN)
4: train_ RE(modela, corpR)
5: 1=2
6: while <= n do
7: Enew. Econs Tnews Teon (nooeeegcckkooolleee(((((), Ke, Enews Econy, Tnews Tconf
8: corpN, corPR, Eo. To= wwt..distatt.corpuue D2 Ke, Eeons conf. Wo
9:
corpN corpN corp
corPR = corpa U corPR
11:
Eo = Eo LE
12:
To To U
train Ny corpN)
14: train RE(model corpn)
i=i+1
16: end while
17: bbuild Teon))
18: return y
Algorithm 2 Constructing distantly-superviisn corpus
Input: A part of text corpus text corpus ID;, coarse-domain KG K. new entities with high confidence Beon new triples with high confidence Tem out-of-domain words Wo
Parameter: negative sample ratio ratio, out-of-domain sample ratio ratio,
Output: Distant-supervision NER corpus corpN. distant-supervision RE corpus corpR, overlapping
entities Eo, overlapping triples To
1: Let corpg = (. corPR = (), Eo = To = 0
2: sentence_num len( D,)
3: j=1
4: while senntece num do
5: entities = -atttiigm D/. K., Beenf)
6: Eo = Bo U enlilies
7: corpæ corpyU build_NER sample D/,, entities)
8: tripless.triples, entity pair matchingg DJ, Ke. Teon))
9: triple== U triples.
triples,, = get_.eeative_trrplee DV Wo. triples, ration, ratio.)
11:
corPn = corpn get samples(triples)
corpn = corpn get get sampleestrrr)
13:
To To= U triples;
14:
j=j+1
15: end while
16: return corpN, corpR, Eo, To
of negative samples (constructed by either schemes) to the total sample size. The parameter ratio, controls the ratio entity pairs constructed by the second scheme (i.c., via sampling the words outside the domain) to the size of negative samples, respectively.
In addition to the K2 the source domain, we use both K., Econy- and Teon as knowledge bases for constructing the remotely supervised corpus. This would ensure that the NER and RE models can identify the overlapping knowledge between K, and Ky, while at the same time be guided to discover the knowledge specific the fine domain.

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Algorithm 3 Discovering fine-domain specific knowledge
Input: A part of text corpus text corpus DD coarse-domain KG Ke, new entities Enew new entities with high confidence Econf new triples Tnww new triples with high confidence T conf Parameter: NER model modely, RE model model probability threshold of the entity the frequency threshold of the entity th probability threshold of the triple thpt. frequency threshold of the triple lhst
Output: Enews Eeonf T neww, Teoms
1: Let corpe (), corpn () Eo =0, To 0-
2: sentence num len( (D;)
3: j=1
4: while
<= <= sentence_num do
5: entities = NER -peepiiii DD modely)
6: entities get new entities( entities, Ko)
7: Enew = merge entity( Enew, entities)
8: j=
9: end while
10: Bomf wee..coofidencceentteee Enews Uppes thfee
11: j=1
12: while j <= sentence.num do
13: entities = entity -matciig D/, Ke. Bconf
14: pairs enumerate pairst entities)
15:
pairs gew_new www. wew.pairs( pairs, Kx)
16:
Iriples 8 = RE prediction( DJ pairs, model
17:
T new merge__riplee T nene, triples)
18:
j j
end while
20: Teomf gee..coniidence.rinne triplee newws thit that
return Enew, Econf Tnew: Tconf
As shown in Algorithm 2 for building the distantly-superviied NER corpus corps, the sentence Di firstly string-matched with the knowledge bases KK and Evonn extract the entities in the sentence (line 5). Afterward, the matched entities are merged into overlapping entities Eo, and the NER label sequences are generated through the BIO strategy to merge into corpN (line and 7). For building the distandly-supervieed RE corpus corpn, we firstly take K2 and Teon as knowledge bases and use entity pair matching match the triples triples; based on K, and the triples triples, based on conff appearing in the sentence DD! (line 8). We then build negative triples with parameters ralio, and ralio, (line 10). Finally, we construct the RE corpus based on the triples triples, triples, and corresponding sentences through a pre-defined relationship sample template (line and 12).
3.4 DISCOVERING FINE-DOMAIN SPECIFIC KNOWLEDGE
Recall that in the proposed iterative training framework, the whole unlabeled dataset is divided into sub-dataset D,, I...n, the fine-domain specific knowledge discovery will be performed on each sub-dataset except the first one D; = 2...n (line 5 to 16 in Algorithm []. For each new sub-dataset D 2..m, we will use the previously-updated models modely and model x to predict the new entities and triples. Afterward, the sub-datasel will be used for updating modely and model via distantly supeeviied training. noisy or incorrect entities and triples could be discovered during this procedure, we developed filtering mechanism only to keep the entities and triples with higher confidence. Specifically, we design the rules for filtering the discovered entities and triples by: 1) probability of the entities and triples predicted by the corresponding models should be greater than pre-defined thresholds the and thyt, respectively; 2) cumulative frequency of the new entities and triples discovered from datasets De to ID, should be greater than the pre-defined thresholds th and th respectively.
As shown in Algorithm for discovering new entities Enew, will apply the trained modely dataset D; and obtain entities that are disjoint with K,, (line 5 and 6). Then, we will merge entities with the previously-discovees entity set Ene (line 7). Finally, will select the "high-confident"

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entity as B based on the mechanism above by the prediction probability and cumulative fre- quency (line 10). For the discovery of new triples Tnew. we will enumerate entity pairs that are disjoint with the K, (line 13 15). We will then use the trained RE model and the predefined sam- ple template to predict the relationship of the entity pairs and delete the triples whose predicted relationship is NULL (line 16). Other processing is similar to the discovery of new entities.
After Algorithm 3 discovered entities specific to the fine domain are stored in Boon Discov- ered triples TR (new relation, overlapping entity) and TE (new relation, new entity) are stored in T Toonf- In the next iteration, Algorithm 2 will then use the updated Econ and Toony for building fistant-supervision corpus. Such iterative design can facilitate the interoperability between the two competing tasks based on a fixed number of unannotated data samples in the fine target domain: distantly-supervisee training of the NER and RE models versus the discovery of new knowledge using the trained NER and RE models, thus improve the efficiency of performing KG domain adap- tation and construction without any annotation.
EXPERIMENTS
In this work, we used the adaptation of KG from the biomedical domain (coarse) to the oncology domain (fine) evaluate its effectiveness in practice. Implementation details of the experiment are also provided, along with the publicly-avallble data and the containerized environment in the released source code, for easy replication of the experiment and the development of other KG methods.
example to demonstrate the workflow of the KGDA framework, well to
4.1 DATASET
We downloaded papers from 12 international journals (journal details can be found in the supple- mental materials) in the oncology domain. PDF files of the papers were cleaned and converted to sentences. In total, we included nearly 240,000 sentences as the unlabeled text corpus of the on- cology domain D. The coarse-domain KG K. used in this work is the biomedical KG defines 18 entity types and 19 relationship types, including 5.2 million English entities and 7.34 million triples. The lists of entity types and relationship types can be found in the supplementary materials.
4.2 EVALUATION
Similar to the previous works Mintz et al. 22009), evaluate our method in two schemes: held-out evaluation and manual evaluation. For the held-out evaluation, we reserved a part of the text corpus of D the test set. During the testing, we then compared the prediction results of the NER and RE models with the labels matched with K,, and calculated the precision, recall, and F1 of the held-out dataset. Specifically, we use seqve?" to evaluate the micro average precision, recall, F1 of NER. When evaluating the RE model, we perform relation classification prediction on the triples existing in KK, and corresponding entity pairs appearing in the held-out corpus. Finally, weighted average precision, recall, and Fl from the held-out evaluation will be reported.
As the labels of testing samples in the held-out evaluation are all inferred by distant supervision from the coarse domain, such scheme can only evaluate whether the trained model can capture the knowledge the coarse domain, but cannot evaluate the ability of the models to discover new knowledge in the fine-domain. Therefore, we also adopted the manual evaluation scheme, consisting of the evaluations of: 1) the entities specific to fine domain E,on,, which are not presented in K,;; 2) the triples of new relations TR; 3) the triples of new entities TE. We randomly sampled 50 cases of Eom,, Tn, and TE respectively, then asked one physician to manually label them for whether the entities and triples are correct. As the number of name entities and triples instances that are expressed in the corpus is unknown, we cannot estimate the recall of fine-domain KG. Therefore, only show the precision of Econf, Tn, and Tg. We fully recognize that the discovery of new knowledge in the fine-domain is an indispensable task for this work and we are recruiting more medical experts conduct human reader study and performance evaluation for the proposed model.
http://ddcaa.cuu.c//iioeo.o http:///gggituucoo//cakaiittttte
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4.3 IMPLEMENTATION SETTINGS
We divide the corpus D into six equal subsets, and each subset contains around 40,000 sentences. We used D1 to D for model training and KG construction. We reserved Dg for held-out evalu- ation. We tested BERT Kenton & Toutanova (2019), Bio ClinicalBERT Alsentzer et al. (2019), biomed RoBERTa Gururangan
al. (2020) for initializing NER and RE models. Our experi- ments were run an Ubuntu system computer with three NVIDIA 1080Ti graphics cards. The learning rate, batch size, and epochs are set as 2E-05, 20, and 4, respectively. Hyperparameters th ph tht set as 2, 0.95, 3, 0.97. The parameters ration and ratio, that control nega- tive sampling are set to 0.2 and 0.3.
4.4
HELD-OUT EVALUATION
Table 1: Held-out evaluation of NER model.
Table 2: Held-out evaluation of RE model.
models
precision
recall
F1
models
precision
recall
F1
BERT
0.878
0.859
0.868
BERT
0.977
0.912
0.943
Bio ClinicalBERT
0.877
0.853
0.865
Bio CloncliiialBRRTT
0.976
0.923
0.948
Nomed_RRoBRTI 0.871
0.848
0.859
BoBEed_RoBERTa
0.973
0.932
0.952
The results of the NER and RE models evaluated by the held-out dataset shown in Table and Table 2 respectively. The KGDA frameworks initialized by the three pre-trained language models (BERT, Bio Cloniicclal, and evaluations, demonstrating the robustness of our framework.
all show good performance in held-out
4.5 MANUAL EVALUATION
models
#
#T
#le.con/
#II'R
#
BERT
69587
20615
1631
24580
905
Bio_ CIoTiclicaBB
70055
20611
1936
25312
1195
biomed RoBERTa
69497
20495
1625
23183
1071
Table 3: The number of entities and triples.
The number of all discovered entities (Bo), triples (To), new entities with high confidence (Bemnf) triples representing new relations with overlapping entities (T and triples representing new re- lations with new entities (Tg) are shown in Table 3 with each row belonging to one pre-trained language models used. Numbers of IEo and To have minor differences among different pre-trained language models, possibly due to the conflicts in strings matching of knowledge bases. Econf nn, and T represent specific knowledge of the fine domain. We sampled 50 cases from Econf, TR. and TE for manual evaluation, and the results are shown Table 4
4.6 KNOWLEDGE GRAPH CONSTRUCTION IN THE FINE DOMAIN
As our ultimate goal, we can construct the KG the fine domain by combining To, TR. and Tg- We selected
as the backbone language model for KGDA and constructed the knowledge graph correspondingly. An example of the KG we built are shown the supplementary material.
4.7 ABLATION STUDY
We investigated the impact of techniques employed by KGDA on its held-out experiment perfor- mance by removing the corresponding component from the framework:
w/o (cumulative): When using corpus D; to train NER and RE models, the cumulative corpus is not used. i.e. delete lines 9 and 10 in Algorithm and mark corp'y and corp'R in line corp and corpR respectively.
w/o (iter): Remove the iterative training strategy and only K as external knowledge base.
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w/o (iter, type): Remove the iterative training strategy and delete the entity type in the template of RE. In, this method, the template is "[CLS] head entity [SEP] tail entity [SEP] sentence".
The results of the ablation analysis are shown in Table Compared to the complete framework with w/o (cumulative), it can be seen that the using of accumulated data through iterations is beneficial for improving the generalization ability of NER and RE models. The held-out performances of the model without iteration indicates that the iterative training strategy can not only discover the specific knowledge in the fine domain, but also maintain the ability to discover overlapping knowledge between coarse and fine domain. The RE performance of w/o (iter) is slightly better than that of w/o (iter, type), indicating that specifying the entity type of the entities is helpful for improving the performance of the RE task.
Table 4: Results of manual evaluations.
Table Results of ablation study.
models
f
Tn
TE
NER
RE
BERT
0.78
0.50
0.66
models
precision
precision
Bio_ ClinicalBBERT
0.92
0.60
0.56
BERT
0.878
0.977
biomed RoBERTa
0.92
0.70
0.72
w/o (cumulative)
0,845
0,970
w/o (iter)
0.857
0.967
w/o (iter, type)
0.857
0.967
Bio ClinicalBERT
0.877
0.976
w/o (cumulative)
0.847
0.971
w/o (iter)
0,860
0,972
w/o (iter, type)
0.860
0.970
biomed RoBERTa
0.871
0.973
w/o (cumulative)
0.822
0.967
w/o (iter)
0.858
0,971
w/o (iter, type) 0.857
0,968
5 CONCLUSION AND DISCUSSION
In this paper, we propose an integrated, end-to-end framework for knowledge graph domain adapta- tion using distant supervision, which be used to construct KG from fully unlabeled text data with the guidance of an existing KG. To deal with the potential challenges in distant supervision, which might limit the knowledge discovered from the new domain, we propose an iterative train- ing strategy, which divides an unlabeled corpus into multiple corpuses. For each new corpus to the model, we then combine the knowledge in the coarse domain with the knowledge identified from the previous corpuses for distantly-superr proposed KGDA framework can discover not only knowledge that overlaps with the coarse domain, but also knowledge specific to the fine domain and unknown to the coarse domain, thus enabling coarse-to-fine domain adaptation. We implemented the adaptation from biomedical KG to the on- cology domain in experiments and verified the effectiveness of the KGDA framework through held-out and manual evaluation.
training. By adopting the iterative training strategy, our
Several limitations and challenges remain beyond the current work for more effective and accurate KG construction: Firstly, more thorough evaluation with human reader study is needed to validate that new knowledge relevant (not only correct) to the target domain can be discovered by KGDA. Secondly, it has been recognized by the field that distant supervision will inevitably introduce noisy labels Liang al. (2020); Zhang et al. (2021b), thus the denoising step usually needed but not implemented in the current version of KGDA. Thirdly, there has been existing KG constructed in the related domains of oncology and cancer research. We will investigate the scheme to allow adaption from multiple sources (not only the coarse domain) to leverage this existing knowledge better. Another type of crucial prior information for this work is clinical ontology, where we will integrate the relationships defined in ontology and entity description to enhance the model. Fourthly, an essential premise of the KGDA that we assume the source and target domains share the same set of entity types and relation types, which can limit the knowledge discovered from the fine domain. We will investigate data mining techniques to adaptively add/remove entity and relation types in the fine domain. Finally, there have been many such as GPT-3 in recent years. While our model uses variations of BERT (BiomedRoBERT: and BBEC backbone networks, we can easily adapt KGDA other language models.
large-scale pre- trained language models developed

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Under review as a conference paper at ICLR 2023
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12


Reviews:
[{"review_id": "kKuYGU-Jv4", "reviewer": "Reviewer_KkJe", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper focuses on the poor annotation issue and proposes a distantly supervised iterative training method for knowledge graph construction. The authors leverage distant supervision to automatically label entities and triples from a find-grained domain based on a coarse domain. In specific, they first use distant supervision to annotate part of training corpus and train the NER and RE models. Then, the well-trained model will be applied on the left corpus to discover new entities and relations, where the high-confident ones will be used for further automate annotation. The experiments show improvements on the constructed test set. The presentation is mostly clear and easy to follow. However, there are several major issues that weaken the novelty.", "summary_of_the_review": "see above", "strength_and_weaknesses": "Strengths:\n1. The paper is well motivated and presented.\n2. The proposed method is simple yet effective.\n\nWeaknesses:\n1. The novelty of the paper is limited, leaving the key challenges unsolved. For example, 1) how to ensure the newly discovered entities and relations are fine-grained, out of the given corse domain does not mean fine-grained. 2) How to avoid the error propagation during iterative training? 3) How to label the fine-grained types for newly discovered entities and relations?\n\n2. The evaluation is not convincing. First, it lacks strong baseline. What if using distant supervision to annotate all corpus and applying the trained model on test set directly? Second, the improvements are marginal. Especially, in ablation study, the improvements from each component are very limited, what is the significance test results? ", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "URr43REDsLz", "reviewer": "Reviewer_8yCf", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper tackles the task of building a finer-grained KG from a coarse KG (biomedicine -> oncology). For this, the authors propose a pipeline that iteratively applies distant supervision to subsections of the fine domain corpus, which requires no annotated data for training. The authors compare the performance of 3 biomedical PLMs used in their pipeline and conduct a small-scale human evaluation. ", "summary_of_the_review": "This paper proposed a pipeline based on iterative distant supervision to learn a finer-grained KG from an existing coarse KG. The methods are not clearly presented, and the evaluation is invalid. I suggest rejecting this paper.", "strength_and_weaknesses": "Strength:\n- The task of learning finer-grained KG from an existing coarse KG without additional training data might be important.\n\nWeaknesses:\n1. The definition of domains is not clear. Why does biomedical KG not contain oncological information? Is the oncological domain defined by the 12 selected journal? How are these journals selected?\n2. The related work section lacks descriptions of methods that tackle the same task (learning a finer-grained KG from an existing coarse KG);\n3. The method section is lengthy (pages 3-7) and a little bit hard to follow. The authors should consider simplifying the notations or descriptions;\n4. Most importantly, the evaluation is not valid: (1) the main comparison should be made with other work that tackles similar tasks, not just variants of different biomedical PLMs; (2) the manual evaluation consists of only 50 samples and 1 physician. This is far from enough; (3) the authors should prove and show that the proposed model can discover new knowledge in oncology. Specific case studies must be provided.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "TfcB6UbBXT", "reviewer": "Reviewer_hrcf", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes an integrated framework for adapting and re-learning knowledge graphs from one coarse domain (biomedical) to a\nfiner-define domain (oncology). The proposed approach applies distant supervision on cross-domain knowledge graph adaptation. Consequently, no manual data annotation is required to train the model. This paper introduces a novel iterative training strategy to facilitate the discovery of domain-specific named entities and triples. Experimental results indicate that the proposed framework can perform domain adaptation and construction of knowledge graphs efficiently. Overall, this paper is not well-written and not novel at all. ", "summary_of_the_review": "I think this paper needs a major revision, and it benefits little for the ICLR community.", "strength_and_weaknesses": "Strength:\n\nI do not find any strength in this paper. \n\nWeaknesses:\n\n1.The proposed approach is not technically new and only utilizes existing methods for knowledge graph domain adaptation. \n\n2.The experiments are also not conceivable without detailed analysis and case studies. ", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "AgFkBrKx6C", "reviewer": "Reviewer_cH5Y", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper proposed an accurate pipeline for constructing a domain specific database (fine). The constructed database is adapted from a coarse domain. The construction process is distantly (self) supervised without any manually annotated data.", "summary_of_the_review": "Overall, this paper may be a useful for people who would like to build a domain-specific KB in low resource domains. However, it's not clear what are the learning components in this work. The results are not well evaluated (no external baselines). It is hard to evaluate the impact of this research.", "strength_and_weaknesses": "The proposed system, if works, will serve as a powerful tool to construct domain-specific KBs. \n\nThe proposed system is fairly complicated. It's not clear whether the proposed system will also work for non-medical domain.\n\nAnother question comes from their evaluations. The performance of the proposed method is not compared to external baselines. It is not clear how challenging is the experiment task.\n\nI am also concerned on the main focus of this paper. There's not much content on representation learning. It is also not clear how the proposed system will help other research in related fields.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject