SlowGuess/NAACL_2022_OCR huggingface dataset:
Evaluation Protocol: After augmenting FB15K237 with the borrowed  $k$  LDPs for each without-mention entity-pair, we train a KGE method to obtain embeddings for the entities in  $\mathcal{E}$ , relations in  $\mathcal{R}$  and textual relations. The hyperparameter  $k$  is tuned on the validation set of FB15K237 for each KGE method from  $\{1, 3, 10, 15, 20, 25, 30\}$ .
We use Link Prediction, which has been popularly used as an evaluation task to compare the KGEs we obtain from a KGE learning method before and after augmenting the KG with the LDPs borrowed using SuperBorrow and other baselines (Wang et al., 2021). Link prediction is the task of predicting the missing head (i.e.  $(? , r , t)$ ) or tail (i.e.  $(h, r, ?)$ ) entity in a given triple by ranking the entities in the KG according to the scoring function of the KGE method. Following prior work, the performance is evaluated using Mean Reciprocal Rank (MRR), Mean Rank (MR) and Hits at Rank k (H@k) under the filtered setting, which removes all triples appeared in training, validating or testing sets from candidate triples before obtaining the rank for the ground truth triple. We consider all entities that appear in the corresponding argument of the relation to be predicted to further filter out incorrect candidates, which is known as type-constraint setting (Chang et al., 2014; Toutanova and Chen, 2015).
We also evaluate the learnt KGEs using a relation prediction task, which predicts the relation between two given entities (i.e.,  $(h,?,t)$ ) from the set of relations in the KG. This task assumes that we are given entity-pairs with candidate relations. The performance is measured using the same evaluation metrics as used in the link prediction task under the filtered setting. We use the publicly available OpenKE tool to conduct experiments with different KGE methods (Han et al., 2018).
We compare the proposed LDP borrowing method against multiple baselines as follows.
LinkAll: In this baseline we connect the two entities in each without-mention entity-pair with a unique link, instead of reusing LDPs, and augment the KG with the without-mention entity-pairs. This baseline enables us to simply incorporate all without-mention entity-pairs into the KG without requiring to borrow any LDPs. It will demonstrate
the importance, if any, of sharing LDPs between with- vs. without-mention entity-pairs as opposed to simply connecting all without-mention entity-pairs with distinct relations.
Co-occurrence: This baseline connects all entity-pairs that co-occurs in any sentence in the corpus  $(\mathcal{T})$  with a generic relation (i.e. co-occurrence relation) in the augmented KG and does not distinguish between different textual relations. This baseline is designed to highlight the importance of the context of entity-pair co-occurrences in the corpus beyond simply treating all co-occurrences equally during the augmenting process.
NeighbBorrow: Given a without-mention entity-pair  $(h^{*},t^{*})$ , we can borrow the LDPs from the first nearest neighbouring (1NN) with-mention entity-pair  $(h,t)$ . The similarity between entity-pairs can be computed using (4) in an unsupervised manner using pretrained entity embeddings such as Rel-Walk embeddings (Bollegala et al., 2021).
$$
\operatorname {s i m} ((h, t), (h ^ {*}, t ^ {*})) = \cos (h, h ^ {*}) \cos (t, t ^ {*}) \tag {4}
$$
Here,  $\cos$  is the cosine similarity between two vectors converted to nonnegative values (i.e.  $[0,1]$ ) using the linear transformation  $(x + 1) / 2$ . On average, when considering 1NN, we borrow 1.3 LDPs for each without-mention pair of entities. In contrast to the proposed SuperBorrow, NeighbBorrow is unsupervised and decouples entities in each pair when computing their similarity.
Link Prediction: Table 3 shows the results of link prediction for different settings on FB15K237 under different KGE methods. Two translational distance-based KGE methods (i.e. TransE and RotatE) and two semantic matching-based models (i.e. DistMult and ComplEx) are used as the KGE learning methods (Rossi et al., 2021; Wang et al., 2021). We emphasize that our purpose here is not to compare the absolute performance among those KGE methods, but to evaluate the effect of using LDPs for augmenting the KG and representing the without-mention entity-pairs via different borrowing methods. For SuperBorrow, the optimal numbers of borrowed LDPs  $(k)$  determined using the validation set for TransE, DistMult, ComplEx and RotatE respectively are 30, 20, 15 and 25.
<sup>7</sup>https://github.com/thunlp/OpenKE
Model	overall	without-mention
MR	H@3	H@1	MR	H@3	H@1
DistMult (KG only)	4.1	0.938	0.856	4.0	0.942	0.865
KG+ExtractedLDPs	2.6	0.955	0.876	2.7	0.957	0.883
LinkAll	7.2	0.887	0.752	7.8	0.880	0.744
Co-occurrence	2.4	0.954	0.875	2.4	0.956	0.882
NeighbBorrow	3.0	0.955	0.874	3.0	0.956	0.881
SuperBorrow	2.2	0.960	0.875	2.2	0.962	0.882
ComplEx (KG only)	3.1	0.954	0.900	2.8	0.957	0.908
KG+ExtractedLDPs	1.9	0.967	0.917	1.9	0.967	0.922
LinkAll	4.0	0.909	0.812	4.3	0.902	0.808
Co-occurrence	1.8	0.967	0.916	1.8	0.967	0.920
NeighbBorrow	1.7	0.973	0.921	1.7	0.974	0.925
SuperBorrow	1.7	0.972	0.917	1.7	0.973	0.922
As shown in Table 3, augmenting the KG with the extracted LDPs (i.e., KG+ExtractedLDPs) significantly improves the performance for with- mention entity-pairs for all KGE methods. However, the performance when predicting links for without-mention entity-pairs decreases slightly for all KGE methods, except for DistMult in the KG+ExtractedLDPs setting. For the borrowing models, even though the co-occurrence baseline improves the prediction for without-mention set, borrowing relevant LDPs from the 1NN entitypairs (NeighbBorrow) or the proposed supervised borrowing (SuperBorrow) reports superior results. We can see that the best performance for the overall and without-mention sets are achieved with the augmented KG using SuperBorrow, followed by NeighbBorrow.
Relation Prediction: Table 4 shows the accuracies for the relation prediction task. Experimentally, the best results for this task is obtained when corrupting  $r$ , in addition to  $h$  and  $t$  corruptions, is applied to generate negative triples to train the KGE method. This negative sampling schedule follows the evaluation procedure of relation prediction. As shown in the table, SuperBorrow reports the best MR and Hits@3 for DistMult KGEs, while Neibh-Borrow baseline performs better than SuperBorrow with ComplEx method. Further results for relation prediction are in the Supplementary Appendix A.
Comparisons against Prior Work: We compare our proposed method against prior work, namely Feature Rich Network (FRN) (Komninos and Manandhar, 2017) and Conv (E+DistMult) (Toutanova et al., 2015). In FRN, an MLP is trained to predict the probability of a given triple being true using different types of features such as the entity
Table 4: Results of relation prediction on FB15K237.
Model	overall	w-m	w/o-m
MRR	H@10	MRR	H@10	MRR	H@10
Conv (E+DistMult)	0.401	0.581	0.339	0.499	0.424	0.611
FRN	0.403	0.620	0.441	0.683	0.387	0.595
ours (DistMult)	0.460	0.714	0.378	0.649	0.468	0.720
ours (RotatE)	0.499	0.712	0.439	0.674	0.504	0.715
Table 5: Comparisons against prior work on link prediction on FB15K237. w-m and w/o-m refer to with- mention and without-mention entity-pairs, respectively. The results for prior work are taken from the original papers. The best results are in bold, while the second best results are underlined.
types and features extracted from textual relation mentions. Conv(E+DistMult) represents LDPs by vectors using a convolutional neural network, and combines DistMult scoring function with that of the Entity model (E) proposed by Riedel et al. (2013). E model learns a vector for each entity and two vectors for each relation corresponding to the two arguments  $\boldsymbol{r}_h$  and  $\boldsymbol{r}_t$  of a relation  $r$ . The scoring function of a triple in E model is defined as  $h^\top \boldsymbol{r}_h + t^\top \boldsymbol{r}_t$ . The combined model (E+DistMult) is trained on a linearly weighted combination of KG triples and textual triples. For a fair comparison, we consider the task of predicting missing tail entities and we avoid the type-constraint setting.
As shown in Table 5, for the overall test set of FB15K237 our models outperform both FRN and Conv models according to MRR and H@10. For with-mention entity-pairs, our models report higher scores compared to Conv(E+DistMult), while FRN performs best. For with-mention entity-pairs FRN can extract rich features from the contexts of co-occurrences, which helps it to obtain superior performances. However, both FRN and Conv models perform poorly on without-mention entity-pairs, where such contextual features are unavailable. On the other hand, by using the proposed SuperBorrow to augment LDPs for KGs we can overcome this limitation successfully.
Borrowed LDPs: To provide examples of LDPs injected into FB15K237, Table 6 shows the borrowed LDPs by NeighbBorrow and SuperBorrow for some selected entity-pairs. We can see that representative LDPs of various relation types are ranked at the top by SuperBorrow. For example, for
Entity-pairs (h,r,t)	Borrowed LDPs
NeighbBorrow	SuperBorrow
h= Woodrow Wilson	h: <nsubj>:joined: <dobj>:t	h: <pos>:t
t= League of Nations	h: <nsubj>:left: <dobj>:t	h: <nsubj>:president: <prep>:of: <pobj>:t
r= organizations-founded	h: <pos>:t	h: <nsubj>:joined: <dobj>:t
h: <pos>:ambassador: <prep>:to: <pobj>:t
h: <nsubj>:member: <prep>:of: <pobj>:t
h= 20th Century Fox	h: <nn>:movie: <appos>:t	h: <dobj>:released: <nsubj>:t
t= Lincoln	h: <nn>:film: <nsubj>:t	h: <dobj>:release: <nsubj>:t
r= film-distributor	h: <nn>:movie: <dep>:t	h: <nsubj>:released: <dobj>:t
h: <pos>:t	h: <appos>:grant: <appos>:t
h: <pobj>:with: <prep>:t
h= Deep Impact	h: <pobj>:in: <prep>:work: <poss>:t	h: <dep>:film: <pos>:t
t= Leslie Dilley	h: <nn>:fame: <pobj>:of: <prep>:t	h: <vmod>:produced: <prep>:by: <pobj>:t
r= film-production-design-by	h: <pos>:t	h: <vmod>:written: <prep>:by: <pobj>:t
h: <dep>:tagged: <appos>:t
h: <nn>:film: <nsubj>:t
h= Idaho	h: <nsubjpass>:located: <prep>:in: <pobj>:t	h: <pos>:t
t= Christianity	h: <apps>:usa: <apps>:t	h: <amod>:state: <prep>:of: <pobj>:t
r= religion	h: <pos>:t	h: <rcmod>:plays: <dobj>:t
h: <dobj>:entered: <nsubj>:t
h: <nn>:date: <nn>:t
the film-distributor relation, NeighborBorrow selects LDPs containing specific tokens such as movie or film, whereas SuperBorrow retrieves LDPs that better express the target relation such as 20th Century Fox:  $\langle$  -dobj  $\rangle$ :released:  $\langle$  nsubj  $\rangle$ :Lincoln.
Relation Categories: To better analyse the effect of the proposed SuperBorrow for KGEs, we evaluate the link prediction task on different relation categories including 1to1, 1toN, Nto1 and NtoN as defined in Bordes et al. (2013).
Table 7 presents the results of predicting head entities for all KGE methods considering KG only and SuperBorrow. We can see that SuperBorrow achieves higher performance over the original graph on all relation categories. In particular, our proposal significantly boosts the performance of predicting head entities for the Nto1 relation type where all KGE methods report the lowest H@10 for the KG only setting. Similar results are obtained for predicting the tail entities as in Appendix B. Overall, these results show that incorporating information from text corpora into KGs enables us to learn KGEs that encode diverse relation types.
Visualisation of Entity Embeddings: In Figure 1, we visualise the entity embeddings of KGonly and KG with LDPs using  $t$ -distributed stochastic neighbour embeddings (t-SNE) (Hinton
Table 6: Borrowed LDPs of selected entity-pairs. Top 5 LDPs with our borrowing method and LDPs borrowed from 3NN entity-pairs are shown.
Method	# Tuples	1to1 192	1toN 1293	Nto1 4289	NtoN 14,696
TransE	KG only	0.536	0.597	0.124	0.418
SuperBorrow	0.947	0.984	0.377	0.829
DistMult	KG only	0.500	0.433	0.064	0.371
SuperBorrow	0.922	0.856	0.338	0.547
ComplEx	KG only	0.495	0.434	0.045	0.368
SuperBorrow	0.917	0.913	0.277	0.601
RotatE	KG only	0.568	0.631	0.118	0.388
SuperBorrow	0.932	0.969	0.404	0.722
Table 7: Hits@10 of head prediction for different relation categories using embeddings trained on KG only and embeddings from SuperBorrow for each KGE method.
and Roweis, 2002) method. Relations in FB15k237 are labelled as domain/type/property where domain/type represents the type of a head entity in the relation. Thus, for each entity in the KG, we extract its types from all training triples where the entity acts as the head. We label entities that belong to the two most frequent entity types, which are people/person (4,538 entities) and film/film (1,923 entities). From Figure 1, we see that the embeddings learnt from the augmented graph results in distinct clusters of the same type, compared to the clusters obtained from the KG alone. This emphasizes the importance of using textual mentions in KGE learning.
(a) KG only
(b) KG with LDPs
Figure 1: t-SNE plots for DistMult entity embeddings comparing (a) KG-only and (b) KG with LDPs.
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