Event Assignment Based on KBQA for Government Service Hotlines

Figures & data

Figure 1. Model structure.

Figure 2. Event extraction module structure.

Figure 3. The relationship schema diagram.

Table 1. Examples of relational triples.

Entity 1	Relation	Entity 2
3471	Contains	拒载 (refuse to take passenger)(Trigger)
3471	Belong to	出租车拒载 (Cab refusal)(Event type)
3471	Include	上周日 (Last Sunday)(Argument role)
3471	Include	车牌号 ABCDE （论元信息）
3471	Dealt by	公安局
3471	Contains	拒载 (refuse to take passenger)(Trigger)

Table

Algorithm 1: Knowledge Fusion

Input: Entity Set E:{entity1,entity2, … ,entitym}, Role Set R:{role1, role2, … , rolen} Output: Aligned Entity Set E’

group←K-Means(R) // Iterate over each entity in the entity set E for i in (1, m):  // Iterate over all entities except the current i  for j in (i+1, m):   if str(entityi)==str(entityj):    // Calculate the similarity of two entities    sim1=Sim(Concat(entityi, contexti),Concat(entityj, contextj))    // If the similarity is less than threshold δ, the two entities are merged    if sim1≥δ:     entityi←entityj    // If the similarity of the two entities is less than the threshold δ, but the similarity in the sentence containing    contextual information is greater than ζ, the two entities are merged    elseif (entityi∈R) &; (entityj∈R):     labeli=groupi     labelj=groupj     if labeli==labelj:       sim2=Sim’(Concat(entityi, senti), Concat(entityj, sentj))       if sim2≥ζ:       entityi←entityj

Figure 4. Subgraph retrieval procedure.

Table 2. Dataset description.

Dataset	Documents	Sentences	Characters	Department	Avg length	Max length	Min Length
Dataset1	83073	214638	9306k	30	112.02	500	10
Dataset2	106926	276036	11962k	36	111.87	500	10

Table 3. Comparisons of different baselines result.

	Dataset1					Dataset2
Model	Cov. Rate	Hits@1	Precision	Recall	F1	Cov. Rate	Hits@1	Precision	Recall	F1
HAN	-	-	0.7539	0.6917	0.6768	-	-	0.7203	0.6458	0.6246
XLNet	-	-	0.7514	0.7677	0.7349	-	-	0.7088	0.7149	0.6899
BERT-BiGRU	-	-	0.8560	0.8237	0.8281	-	-	0.8129	0.7651	0.7933
EEuEK	-	-	0.8417	0.8289	0.8307	-	-	0.7864	0.7895	0.7768
BGMA	-	-	0.8670	0.8319	0.8448	-	-	0.8103	0.7954	0.8113
DeBERTa-SSP	-	-	0.8656	0.8695	0.8640	-	-	0.8170	0.8310	0.8132
Pullnet	0.9188	0.8513	0.8769	0.8725	0.8653	0.9110	0.8375	0.8599	0.8429	0.8531
EmbedKGQA	0.9106	0.8590	0.8855	0.8639	0.8791	0.8846	0.8416	0.8721	0.8515	0.8511
RnG-KBQA	0.9338	0.8707	0.8755	0.8834	0.8808	0.9078	0.8505	0.8671	0.8584	0.8528
UniK-QA	0.9401	0.8757	0.8791	0.8815	0.8804	0.9102	0.8549	0.8699	0.8645	0.8603
SKP	0.9372	0.8669	0.8836	0.8785	0.8774	0.9076	0.8395	0.8650	0.8679	0.8596
no-sanding	0.9620	0.8664	0.9054	0.8933	0.8773	0.9349	0.8486	0.8607	0.8520	0.8535
Our	0.9620	0.8866	0.9350	0.9006	0.9132	0.9349	0.8552	0.8988	0.8741	0.8877

Table 4. KG incomplete experimental results of Dataset1.

	30% KG		50% KG		80% KG
Model	Coverage rate	Hits@1	Coverage rate	Hits@1	Coverage rate	Hits@1
Pullnet	0.6537	0.5497	0.8172	0.7655	0.8867	0.8353
EmbedKGQA	0.6317	0.5871	0.8040	0.7729	0.8788	0.8338
RnG-KBQA	0.6627	0.6121	0.8269	0.7826	0.9026	0.8434
UniK-QA	0.6594	0.6164	0.8178	0.7887	0.9113	0.8487
SKP	0.6655	0.6002	0.8100	0.7633	0.9054	0.8388
No-sanding	0.7133	0.5993	0.8513	0.7796	0.9253	0.8416
Our	0.7133	0.6489	0.8513	0.7925	0.9253	0.8614

Table 5. KG incomplete experimental results of Dataset2.

	30% KG		50% KG		80% KG
Model	Coverage rate	Hits@1	Coverage rate	Hits@1	Coverage rate	Hits@1
Pullnet	0.6459	0.5359	0.8002	0.7359	0.8745	0.8093
EmbedKGQA	0.6143	0.5737	0.7916	0.7449	0.8528	0.8136
RnG-KBQA	0.6543	0.5871	0.7989	0.7530	0.8752	0.8248
UniK-QA	0.6546	0.5899	0.7980	0.7595	0.8788	0.8258
SKP	0.6549	0.5824	0.7908	0.7541	0.8760	0.8128
No-sanding	0.6729	0.5838	0.8110	0.7527	0.8844	0.8223
Our	0.6729	0.6080	0.8110	0.7620	0.8844	0.8291

Figure 5. KG incomplete experimental comparison of Dataset1.

Figure 6. KG incomplete experimental comparison of Dataset2.

Table 6. Comparison of time consumption of models.

Model	Inference Time（ms）	Hits@1
knowledge graph construction process	3.8	-
“event-department” matching process	6.0	-
SKP	30.7	0.8669
Our	16.2	0.8866

Data availability statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.