EventOA: An Event Ontology Alignment Benchmark Based on FrameNet and Wikidata
Year: 2023

Abstract:
Event ontology provides a shared and formal specification about what happens in the real world and can benefit many natural language understanding tasks. However, the independent development of event ontologies often results in heterogeneous representations that raise the need for establishing alignments between semantically related events. There exists a series of works about ontology alignment (OA), but they only focus on the entity-based OA, and neglect the event-based OA. To fill the gap, we construct an Event Ontology Alignment (EventOA) dataset based on FrameNet and Wikidata, which consists of 900+ event type alignments and 8,000+ event argument alignments. Furthermore, we propose a multi-view event ontology alignment (MEOA) method, which utilizes description information (i.e., name, alias and definition) and neighbor information (i.e., subclass and superclass) to obtain richer representation of the event ontologies. Extensive experiments show that our MEOA outperforms the existing entity-based OA methods and can serve as a strong baseline for EventOA research.

heading: 1 Introduction
text: Event ontology is crucial for understanding human behavior and has become a new paradigm for describing knowledge in the Semantic Web by providing a shared and formal specification about what happens in the real world (Brown et al., 2017). As shown in Figure 1, event Attack accurately describes the action in which someone attempts to injure another organism with many-sided arguments such as “assailant”, “victim”, “weapon”, and so on. It has been recognized as useful for tasks like information extraction (Wimalasuriya and Dou, 2010), web service (Li and Yang, 2008) and automatic question answering (Lopez et al., 2011). Thus a remarkable number of event ontologies have been
∗Corresponding Author.
created such as FrameNet (Baker et al., 1998), VerbNet (Kipper et al., 2007), Wikidata (Erxleben et al., 2014) and ACE (Doddington et al., 2004). However, the independent development of event ontologies often results in heterogeneous representations that hinder the knowledge integration.
Driven by the ontology alignment evaluation initiative (OAEI) 1 (Pour et al., 2022), many datasets (Bodenreider et al., 2005; Svátek and Berka, 2005; Karam et al., 2020) and methods (Jiménez-Ruiz et al., 2013; Faria et al., 2013; Iyer et al., 2021) have been proposed for ontology alignment (OA). However, almost all datasets and methods so far focus on entity ontologies, which are known for sharing knowledge about entities such as people, organizations and products (Bodenreider et al., 2005; Zamazal and Svátek, 2017). In contrast, event ontologies, which provide nexus for related entities/arguments with a higher semantic granularity, are more useful for language understanding tasks (Brown et al., 2017), but there is little attempt to
1A public platform to collect datasets for ontology alignment tools, and a regular evaluation of those tools since 2004.
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tackle the problem of event-based OA.
To address the above issues, we take FrameNet (Baker et al., 1998) and Wikidata (Erxleben et al., 2014) as examples to explore the alignment between event ontologies. As illustrated in Figure 1, for Wikidata and FrameNet, OA systems need to establish correspondences between event types such as Assault vs. Attack, and correspondences between event arguments such as “armament” vs. “weapon”. We choose FrameNet and Wikidata as the data sources for the following reasons.
First, establishing correspondences between FrameNet and Wikidata is meaningful as it will help to obtain an integrated event ontology with high coverage and quality. On one hand, Wikidata is a widely used world knowledge base contributed by the community, which has a large number of events but with a confusing hierarchy (Pellissier Tanon et al., 2020). On the other hand, FrameNet is an excellent repository of linguistic knowledge designed by linguists, which has a logically clean hierarchy but with limited events. Specifically, Wikidata contains 290K events that cover a wide range of domains, including disaster, sport, election, etc. However, the hierarchy in Wikidata is confusing as anyone can edit relations between events. For example, Writing is a subclass of Artistic_creation, while Carving is a subclass of Change. So a query for Artistic_creation would find the Writing but not Carving. In fact, both Writing and Carving are Artistic_creation activities. In contrast, FrameNet has an agreed-upon hierarchy that cannot be changed unless by the agreement of linguists, but FrameNet does not cover latest major events such as 2022_FIFA_World_Cup. Thus an ontology that reconciles the rigorous hierarchy of FrameNet with the rich events of Wikidata is valuable for applications in the Semantic Web.
Second, establishing correspondences between FrameNet and Wikidata is challenging due to the semantic diversity of lexemes described the event type and argument (i.e., polysemy and synonymy). (i) Polysemy, which refers to the phenomenon that ontologies use the same lexeme to describe events with different purposes, e.g., Motion in FrameNet describes the everyday events of “Agents change in position over time”, while Motion_Q452237 in Wikidata describes “parliamentary motion” that happens throughout mankind history. Polysemy also occurs when the semantics of arguments vary
from event type to event type (Li et al., 2006). As shown in Figure 1, argument “perpetrator” of event Assault corresponds to “assailant” of event Attack, while argument “perpetrator” of event Invasion corresponds to “invader” of event Invading. How to identify different semantics of the same lexeme is a challenging issue. (ii) Synonymy, which refers to the phenomenon that FrameNet and Wikidata use different lexemes to refer the same event types or arguments. As shown in Figure 1, Assault and Attack express the same event type with different lexemes, meanwhile “armament” and “weapon” express the same event argument with different lexemes. Thus it is critical to build complex correspondences that are semantics related but with different lexemes.
To this end, in this paper, we build an event ontology alignment dataset based on FrameNet and Wikidata. This dataset is named as EventOA and composed of two sub-datasets: event type alignment and event argument alignment. We extensively evaluate existing OA methods, but they are far from solving EventOA. Thus we propose a multi-view event ontology alignment (MEOA) method by utilizing multi-view information of event ontologies, which we believe would serve as a strong baseline for EventOA. We further propose a reasonable evaluation metrics for EventOA with type alignment and argument alignment. Our contributions are as follows:
• We construct EventOA, a real world event ontology alignment dataset based on FrameNet and Wikidata, which consists of two subtasks, namely, event type alignment and event argument alignment. In addition, we devise evaluation metrics for the two subtasks to assess alignment quality.
• We propose a multi-view event ontology alignment (MEOA) method, which utilizes multiview information to model the representation of event ontology and thus can better resolve the semantic diversity problem.
• We conduct extensive evaluations of existing entity-based OA methods and our MEOA method. Experiment results show that our MEOA method outperforms the entity-based methods and achieves the SOTA performance, which can serve as a strong baseline for EventOA research. We also conduct a detailed error analysis to provide insights to future work.

heading: 2 Data Construction of EventOA
text: We construct our dataset in four stages: FrameNetbased ontology collection, Wikidata-based ontology collection, automatic alignment candidate selection and human annotation.

heading: 2.1 FrameNet-based Ontology Collection
text: FrameNet (Fillmore, 1976; Baker et al., 1998) is a linguistic resource constructed by linguists, which describes everyday events with agreed-upon inheritance relations. Thus we construct FrameNetbased ontology by collecting event types and arguments from FrameNet and building the hierarchy based on the inheritance relations.
In particular, Frame (Guan et al., 2021) is defined as schematic representation of a situation. Frame Elements (FEs) are frame-specific defined semantic roles. Lexical Units (LUs) are set of words grouped by their senses, and belong to a particular frame. Frames are linked by frame-toframe relations (F-to-F) such as “Inheritance” and “Subframe”. And the relations between FEs are the same as the corresponding relations between frames. For instance, in Figure 2, FE “assailant” inherits from FE “agent” as frame Attack inherits from frame Intentionally_affect.
Based on the FrameNet inheritance relations, we construct FrameNet-based ontology, where the frame can be viewed as event type, the FE can be viewed as event argument, and F-to-F and FE-toFE respectively reflect the relations among events and arguments. We build the RDFS schema for FrameNet according to FrameBase, which translates frame, frame element, F-to-F and FE-to-FE into RDFS counterparts (Rouces et al., 2015).

heading: 2.2 Wikidata-based Ontology Collection
text: Wikidata (Erxleben et al., 2014) is a community effort where anybody can contribute facts, resulting in a confusing knowledge base including circle
paths, useless events and incomplete arguments. Thus we construct Wikidata-based ontology by processing the above confusions as follows:
(1) Data acquisition. Inspired by Gottschalk and Demidova (2019), we run the SPARQL query in Figure 4 of Appendix B to select subclasses of Wikidata’s “occurrence” as our event dataset. (2) Circle-path filtration. For an event in the circle, we only retain the path with the smallest depth to the root “occurrence”. (3) Useless-events deletion. For each path, we discard the classes that have less than 10 direct instances and at the same time directly assert their children as subclasses of their parents for keeping the hierarchy. (4) Arguments completion. Given an event, we collect all its direct instances and use the union set of instance’s properties as its arguments, as shown in Figure 1, arguments of event Assault (e.g., “victim” and “perpetrator”) are obtained from its instances.

heading: 2.3 Automatic Alignment Candidate Selection
text: Given the two event ontologies (FrameNet and Wikidata), our goal is to identify correspondences between event type (frame and class) and event argument (FE and property) 2. To facilitate efficiency of annotation, we adopt some heuristic and automatic methods to select alignment candidates.
Event type candidate selection aims to select candidate frames in FrameNet for a given event class in Wikidata 3. We apply Frame-based and LUbased methods combined with Similarity-based method for event type candidate selection.
Frame-based method selects frames that are same as any forms of the event class in Wikidata as its candidates.
2In order to better distinguish the data sources, we’ll use specific concepts to represent event type (i.e., frame of FrameNet and class of Wikidata) and argument (i.e., frame element of FrameNet and property of Wikidata), respectively.
3It does no matter which resource is fixed, we fix Wikidata and select candidates from FrameNet in this work as the number of frames is smaller than classes in Wikidata.
LU-based method selects frames whose lexical units are same as any forms of the event class in Wikidata as its candidates.
Similarity-based method is used to amend the candidates number when the total number of candidates selected by Frame- and LU-based methods is less than 15. It selects candidates by computing similarity between class representation Sc and frame representation Sf . We use frame name Fn and lexical unit Flu to build the representation Sf (Guo et al., 2020). Flu representation is obtained by averaging the embedding of all LUs lu in a frame, i.e., Flu = 1M ∑M i=1 lui. M is the total number of LUs of the frame. Sc, Fn and lu are the pre-trained Glove (Pennington et al., 2014).
Event argument candidate selection attempts to construct candidates for each property in Wikidata with FEs. We apply a Relation-aware Attention Mechanism for argument candidate selection.
For each property in Wikidata, we use the FEs under the corresponding frame as candidates and rank FEs by calculating similarity between property p and FE fe. Specifically, we integrate the nominal and relational perspectives of a FE for a more comprehensive representation as shown in Equation (1). FEn represents the nominal perspective, and∑W
w=1 att(FEw) · FEw represents the relational perspective. We utilize FE-to-FE relations to model FEs relational perspective with attention schema. Given a FE, FE+ = {FE1, FE2, . . . , FEW } represents its expanded FEs, including all FEs that can be linked to FE through FE-to-FE relations. Note attention schemes have been designed to emphasize relevant FEs, avoiding the influence from less relevant but linked FEs.
fe = FEn + W∑
w=1
att(FEw) · FEw (1)
att(FEw) = exp(FEn · FEw)∑W k=1 exp(FEn · FEk) (2)
where FEn is FE name representation, and W stands for the total number of FEs in FE+. We utilize the same method to obtain the p.

heading: 2.4 Human Annotation
text: We obtain candidates through above process for an event, but the semantic distinctions among candidates are subtle, so it is difficult to automatically select the best alignment to ensure the quality.
To create a gold-standard dataset of event ontology alignment, three graduate students who are familiar with Wikidata and FrameNet are invited to label the class with appropriate frame and label property with appropriate FE using our internal annotation platform, a screenshot of annotation interface is shown in Appendix E. They work independently and we adopt the majority vote for deciding the final correspondences (if disagreement appears). The mean inter-annotation agreement computed by Cohen’s Kappa is 82.4%, indicating a high annotation quality. Examples of alignments are provided in Appendix A.

heading: 2.5 Data Analysis
text: Statistics of EventOA. As shown in Table 1, the FrameNet-based ontology contains 1,221 event types and 11,428 arguments, and the Wikidatabased ontology includes 12,159 event types and 257,498 arguments. By automated selection and human annotation, EventOA dataset contains 905 event type alignments and 8,650 event argument alignments, which is rich enough to promote the research of event ontology alignment. Comparison between entity and event OA datasets. We compare EventOA with existing widely-used EntityOA datasets in Table 2. From the table, we can observe that: (1) The size of FrameNet ontology and Wikidata ontology is significantly different. Concretely, the size of entity ontologies in each track have similar magnitudes (e.g.,
M ulti-view Representation
N A D
Sub
attack
subclass: superclass:
alias: event:
definition: offensive, strike...
an assailant physically attacks a victim
invading...
intentionally_affect...
assault
subclass: superclass:
alias: event:
definition: criminal assault...
physically attack of another person
stabbing...
violent_crime...
Sup
GCN-based Multi-view Fusion
Alignm ent Prediction Sim ilarity ScoreN
A D
Sub Sup
N Event Node Alias NodeA Definition NodeD
Superclass Node Superclass NodeSupSub
Notion Edge
Relation EdgeIntra-view Edge
N
A A
D
Sub
Sub Sub Sub
Sup
Sup
A
Sup
Sup
D A
N
Sub Sub
Sub
Figure 3: Illustration of Multi-view Event Ontology Alignment (MEOA) Method.
2,744 (AMA) vs. 3,304 (NCI-A)), while the size of Wikidata is larger than that of FrameNet (i.e., 1,211 vs. 12,159). This is because the construction methods of FrameNet and Wikidata are different (experts vs. community), which leads to diverse representation for events. (2) EventOA has rich arguments. Concretely, EntityOA datasets contain very little properties (e.g., 136 in ENVO), while our EventOA has a larger number of arguments (e.g., 257,489 in Wikidata). The reason is that arguments are defined specifically to each event type and thus lead to the diversity representation.

heading: 3 Multi-view Event Ontology Alignment (MEOA)
text: To solve the semantic diversity problems, we design MEOA, which establishes correspondences between event ontologies by utilizing multi-view information, as shown in Figure 3.

heading: 3.1 Multi-view Representation (MR)
text: MR aims to represent FrameNet and Wikidata from five different views, including name, alias, definition, subclass and superclass 4. We choose these views as they can well describe the description (name, alias and definition) and neighbor (subclass and superclass) information for an event.
Denote the different meaningful views as P = {P1, P2, . . . , Pi, . . . , PN}, and N is the number of views. Pi = {Pi1, Pi2, . . . , Pij , . . . , PiM}, Pij is the j-th element of view Pi. For Pij , we feed its information Pij = {wij1, wij2, . . . , wijk, . . . , wijK} into the transformer-based encoder (Vaswani et al., 2017) to generate pij .
4We model argument name representation with the help of its event type by directly summing up their name embeddings.
pij =
K∑
k=1
embedding(wijk) (3)
where wijk represents the k-th word in the Pij , and K is the total number of words.

heading: 3.2 Multi-view Fusion (MF)
text: MF aims to integrate multi-view embeddings to get a more meaningful representation.
Intuitively, the combination of multi-view embeddings can strengthen the event representation. To model the multi-view information and interactions among different views, a multi-view Event Ontology Graph (EOG) is constructed.
EOG has five different kinds of nodes that correspond to the five views described in Section 3.1. There are two types of edges in EOG:
Intra-view Edge: Nodes referring to the same view are connected with intra-view edges. In this way, the interaction among different nodes of the same view could be modeled.
Inter-view Edge: Different views are connected with inter-view edges if they belong to the same event, which can be further divided into Description edge and Neighbor edge.
Description Edge connects alias view and definition view to the name view. The rationale is that equivalent events tend to share similar or even the same notions.
Neighbor Edge connects superclass view and subclass view to the name view. The rationale is that equivalent events tend to be neighbored by equivalent events.
We apply Graph Convolution Network (GCN) (Kipf and Welling, 2017) on EOG to aggregate information. Formally, the hidden representation
for each node at (l+1)th layer is computed by:
H l+1 = ϕ(D̃− 1 2 ÃD̃− 1 2H lW l) (4)
where Ã = A + I is the adjacency matrix of the graph EOG with added self-connections, I is the identity matrix, D̃ is the diagonal node degree matrix of Ã, ϕ(·) is ReLU function, and W l denotes learn-able parameters in l-th layer.

heading: 3.3 Alignment Prediction (AP)
text: AP aims to establish correspondences between semantically related events from different ontologies.
For FrameNet (E1) and Wikidata (E2), we define the correspondence between two events e1 ∈ E1 and e2 ∈ E2 as the three-element tuple, i.e., T =< e1, e2, s >, where (f, fe) ∈ e1, (c, p) ∈ e2, and s ∈ [0, 1] is a score indicating the degree to which e1 and e2 are equivalent. Event type (f ,e) and argument (fe,p) representation are obtained from MF module. We respectively compute the confidence score of type alignment St and argument alignment Sa, and use mean squared error as the loss to train our model inspired by Iyer et al. (2021). We take event type alignment as an example to elaborate the process.
St(fi, cj) = cos_sim(fi, cj) (5)
Lt = 1
T
∑ (St(fi, cj)−Gt(fi, cj))2 (6)
where St(·) denotes the confidence score of event type alignment, and Gt(·) denotes the ground truth label which is 1 if fi ≡ cj and 0 otherwise. Note the process of argument alignment prediction is same as the process of type alignment, and the details can be found in Appendix C.

heading: 4 Experiments
text: This section provides experiment details, i.e., evaluation metrics, baselines, results, and their analysis.

heading: 4.1 Evaluation Metrics
text: Inspired by Faria et al. (2013) and Ji and Grishman (2008), we define two standards to determine the correctness of alignment (type and argument):
• An event type alignment is correctly identified if it matches a reference event type alignment.
• An event argument alignment is correctly identified if the event type alignment and argument alignment match any of the reference argument alignments.
Following prior work (Faria et al., 2013; Iyer et al., 2021), we use Precision (P), Recall (R) and F-measure (F1) to evaluate the performance.
P = MOut ∩MRA
MOut (7)
R = MOut ∩MRA
MRA (8)
F1 = 2 P ·R P +R
(9)
where MOut are the system’s output alignments and MRA are reference (a.k.a. gold) alignments.

heading: 4.2 Data Splitting and Baseline Models
text: We consider two settings for data splitting: (i) the entire data is treated as test set, which is suitable for comparing unsupervised OA methods; (ii) the entire data is split into training, validation and test sets in 70:10:20 ratio, which can be used for evaluating supervised OA methods.
We compare MEOA with various baselines: (i) unsupervised OA methods, namely AML (Faria et al., 2013), LogMap (Jiménez-Ruiz and Cuenca Grau, 2011; Jiménez-Ruiz et al., 2020) and Wiktionary (Portisch et al., 2019); (ii) supervised OA methods such as Word2Vec + classifier (He et al., 2022) and VeeAlign (Iyer et al., 2021). We choose these baselines based on their top performing and open-source availability. Note Word2Vec + classifier method concatenates embeddings of two event types or arguments and feeds them to a classifier trained on a training set to output an alignment score. For our unsupervised method MEOA-sum, we directly perform a sum operation rather than the GCN to unify different views into a single vector for each event ontology to calculate the alignment score.
Details about baselines and the implementation of our MEOA are provided in Appendix D.

heading: 4.3 Results and Discussion
text: We demonstrate the effectiveness of the proposed MEOA method and the challenges of EventOA.
Performance comparison of different methods on EventOA. Table 3 presents the performance of our MEOA model on EventOA (including event type and event argument alignment) compared with top performer unsupervised/supervised entitybased OA methods. From the table, we can see that: (1) Our MEOA method achieves the highest F-measure on EventOA and significantly outperforms the baselines for t-test (p-value<0.05),
suggesting that our MEOA can utilize multi-view information to model event ontology that is better suited for EventOA. (2) MEOA outperforms the MEOA-sum, which indicates that using GCN for multi-view fusion is, unsurprisingly, more effective than direct summation. (3) The performances of entity-based OA methods are lower and not satisfying on EventOA, which indicates that our EventOA dataset is challenging for the existing OA systems. The reason may be that entity-based OA methods mainly consider surface features such as overlapped sub-strings, and fail to capture the semantics information. However, the string features of events in EventOA such as Engagement and Hostile_encounter are not similar, but their semantics are related.
Performance of MEOA on different OA datasets. We test our MEOA on three entity-based datasets in Table 4. From the table, we can observe that: (1) our MEOA achieves better performance on entity-based OA datasets, which manifests the high generalization ability of our MEOA method. (2) Although MEOA achieves better performance on EventOA, experimental results still demonstrate a gap between event-based OA (65.86% F-measure)
and entity-based OA (e.g., 87.63% F-measure on Anatomy), indicating our EventOA is more challenging and more research efforts on event-based OA are needed in future work 5.

heading: 4.4 Ablation Study
text: We conduct ablation studies on event type alignment in Table 5. From the table, we can see that: (1) Eliminating any of the semantic views, namely alias, definition, superclass and subclass, would hurt the performance, which validates the effectiveness of several views in our model.(2) Comparing the four models with our MEOA model, we can observe that w/o alias hurts the performance most. This intuitively makes sense, since aliases are description representation that can directly reflect the semantics of an event ontology. (3) After ablating superclass and subclass, the F-measure drops by 4.66% and 3.37%, which demonstrates that the neighbor information is valuable for capturing the semantic correlation between event ontologies.
5The results of entity-based systems on the three entity-based datasets (i.e., Conference, Biodiv and Anatomy) can be obtained from OAEI 2022 campaign https://oaei.ontologymatching.org/2022/.

heading: 4.5 Case Study
text: To show the effects of our MEOA model, Table 6 shows some cases of alignments that are correctly predicted by our MEOA but not by AML or LogMap. We can clearly see that, our MEOA method can resolve semantic diversity problems by capturing the implicit connection between ontologies. For instance, MEOA knows War corresponds to Hostile_encounter according to the alias information (i.e., War matches an alias in Hostile_encounter), as well as “Armament” and “Weapon” by utilizing arguments’ definition and relation information. This demonstrates the strength of MEOA for modeling multi-view information to improve the performance of the alignment.

heading: 4.6 Error Analysis
text: Table 7 shows examples of error cases. Note that these cases are also incorrectly predicted by LogMap and AML. (1) Ambiguity, where distinctions between events can be relatively subtle. For instance, Medical_intervention differs from Cure mainly in the effect of the treatment, i.e., Medical_intervention deals only with attempts to alleviate a Medical_condition, whereas Cure deals with situations in which the Medical_condition has been cured. So it is difficult for a model to distinguish which event corresponds to event Treatment. (2) Compound Word, where event types are formed with two or more words that make it difficult to derive accurate representations for them. For example, Deliberate_murder refers to Killing as “deliberate” is used to modify “murder”. However, words in Surgical_operation are used together to take on a new meaning that refers to Medical_intervention. (3) Spurious Correlation, where relations of arguments are too fraudulent for models to see through their spurious relationships and consequently resulting in poor generalization, e.g., “Vehicle” relates to “Speed” in
many cases, so models learn this spurious correlation and cannot generalize to “Impact” where “Vehicle” refers to “Impactors”. (4) Same Category, where models fail to discriminate semantics among arguments whose categories are same. For “Director”, model outputs “Performer” when the gold argument is “Personnel” as both of them belong to people category, and models cannot further discriminate semantics between them.

heading: 5 Related Work
text: As this work involves datasets and methods about ontology alignment, we review key related works in these areas.
The OAEI has been the foremost venue for researchers focused on OA task, so we begin with a survey of datasets have been used in the OAEI. Anatomy is one of the longest running tracks in the OAEI, which consists of human and mouse anatomy ontologies from the biomedical domain and have been manually matched by medical experts (Bodenreider et al., 2005; Dragisic et al., 2017). Biodiv is particularly useful for biodiversity and ecology research (Karam et al., 2020). Conference is a collection of ontologies from the same domain of organizing conferences using complex definitions (Svátek and Berka, 2005). All of these datasets are about entity-based OA, but neglect the event-based OA.
Methods of OA can be classified into featurebased methods and deep learning based methods. Feature-based methods are typically based on lexical matching. Among these systems, AgreementMakerLight (AML) (Faria et al., 2013) and LogMap (Jiménez-Ruiz and Cuenca Grau, 2011) are two classic and leading systems in many OAEI tracks and other tasks (Kolyvakis et al., 2018). Wiktionary (Portisch et al., 2019) is another top performing system for multilingual OA. Recently, some works try to explore deep learning based OA methods. VeeAlign (Iyer et al., 2021) is one of the representative methods, which utilizes word embeddings to predict the alignment.
Although some OA datasets and methods have been investigated and developed, at present there are no well-established benchmarks for event ontology alignment. In this paper, we propose EventOA, an event ontology alignment dataset, which can be used for understanding the events and evaluating the performance of systems analyzing the real world events.

heading: 6 Conclusion
text: In this paper, we construct EventOA, an event ontology alignment dataset based on FrameNet and Wikidata. To overcome the challenges of the new task, we propose MEOA, which can utilize multiview information to acquire richer and deeper semantic representations of events. Experiment results demonstrate that our MEOA method achieves better performance on EventOA and can serve as a strong baseline for future research.
Limitations
The limitations of our work are as follows: (1) As we construct EventOA for a new task by manually annotating, the data size can be further extended. (2) Our study is limited to English sources, and we hope work can pay attention to event ontologies in other languages. Building datasets for multilingual event ontology alignment would have a positive impact on applications in other languages beyond English.

heading: Acknowledgements
text: We thank anonymous reviewers for their insightful comments and suggestions. This work is supported by the National Key Research and Development Program of China (No.2020AAA0106400), the National Natural Science Foundation of China (No.61976211, 6217625). This work is also supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No.XDA27020100), the Youth Innovation Promotion Association CAS, and Yunnan Provincial Ma-
jor Science and Technology Special Plan Projects (No.202202AD080004).

heading: A Examples of Event Ontology Alignment
text: We present the alignments of the event type and event argument in Table 8 and 9 to help understand event ontology alignment construction process.
In human annotation, the salary is determined by the average time of annotation and local labor compensation standard.

heading: B SPARQL Query to Collect Wikidata-based Ontology

heading: C Event Argument Alignment Prediction
text: We compute the confidence score of event argument alignment Sa by taking similarity between fe in FrameNet and p in Wikidata.
Sa(fei, pj) = cos_sim(fei, pj) (10)
We further use mean squared error as the loss to train our model (Iyer et al., 2021):
La = 1
A
∑ (Sa(fei, pj)−Ga(fei, pj))2 (11)
Where Sa(·) denotes the confidence score of event argument alignment, and Ga(·) denotes the ground truth label which is 1 if fei ≡ pj and 0 otherwise.
D Implementation Details and Baselines
We compare MEOA with various baselines. Specifically, AML (Faria et al., 2013) mixes various string-based matching methods to calculate matching scores. LogMap (Jiménez-Ruiz and Cuenca Grau, 2011; Jiménez-Ruiz et al., 2020) starts with a set of anchor mappings obtained from lexical comparison, then alternates between mapping repair and mapping discovery. Wiktionary (Portisch et al., 2019) is another top performing OA system that relies on the Wiktionary knowledge base. Word2Vec uses the vectors of their names and aliases to discover alignments. VeeAlign (Iyer et al., 2021) uses dual-attention mechanism to determine similarity between two ontologies.
We fine-tune MEOA for 6 epochs with a batch size of 48, and evaluated on the validation set for every 0.1 epoch, through which the best checkpoint is selected for prediction. The learning rate is set to 2e-5, while the loss functions used for EventOA is the mean squared error loss. The training uses a single NVIDIA GeForce RTX 3090 GPU.

heading: E Annotation Interface for Human Annotators
text: ACL 2023 Responsible NLP Checklist

heading: A For every submission:
text: 3 A1. Did you describe the limitations of your work?
Section 6
3 A2. Did you discuss any potential risks of your work? Section 4.6
3 A3. Do the abstract and introduction summarize the paper’s main claims? Section Abstract, Section 1
7 A4. Have you used AI writing assistants when working on this paper? Left blank.
B 3 Did you use or create scientific artifacts? Section 2, Section 3
3 B1. Did you cite the creators of artifacts you used? Section 2, Section 3
3 B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Section 2, Section 3
3 B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section 2, Section 3
3 B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Section 2
3 B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Section 2
3 B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 2.4, Section 4.2
C 3 Did you run computational experiments? Section 4
3 C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section Appendix E
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.
3 C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section Appendix E
3 C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 4.3, Section 4.4, Section 4.5
3 C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 4.2
D 3 Did you use human annotators (e.g., crowdworkers) or research with human participants? Section 2.4
3 D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Section Appendix B
3 D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? Section 2.4
3 D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Section 1, Section 2.1, Section 2.2
3 D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Section 2
3 D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Section 2.1, Section 2.2

heading: Future Work
text: Experiment results demonstrate that our MEOA method achieves better performance on EventOA and can serve as a strong baseline for future research. Limitations
The limitations of our work are as follows: (1) As we construct EventOA for a new task by manually annotating, the data size can be further extended. (2) Our study is limited to English sources, and we hope work can pay attention to event ontologies in other languages. Building datasets for multilingual event ontology alignment would have a positive impact on applications in other languages beyond English