E vent OA : An Event Ontology Alignment Benchmark Based on F rame N et and W ikidata (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.

## introduction 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 @xcite . 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 @xcite and automatic question answering @xcite . thus a remarkable number of event ontologies have been * corresponding author. created such as framenet @xcite , verb-net @xcite , wikidata @xcite and @xcite . 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 @xcite , many datasets @xcite @xcite and methods @xcite @xcite 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 @xcite . in contrast, event ontologies, which provide nexus for related entities/arguments with a higher semantic granularity, are more useful for language understanding tasks @xcite , but there is little attempt to tackle the problem of event-based oa. to address the above issues, we take framenet @xcite and wikidata @xcite 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 @xcite . 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 @xcite . 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 even-toa 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: ## data construction of eventoa we construct our dataset in four stages: framenetbased ontology collection, wikidata-based ontology collection, automatic alignment candidate selection and human annotation. ## multi-view event ontology alignment (meoa) 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 . ## experiments this section provides experiment details, i.e., evaluation metrics, baselines, results, and their analysis. ## related work 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 @xcite . biodiv is particularly useful for biodiversity and ecology research @xcite . conference is a collection of ontologies from the same domain of organizing conferences using complex definitions @xcite . 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, agree-mentmakerlight (aml) @xcite and logmap @xcite are two classic and leading systems in many oaei tracks and other tasks @xcite . wiktionary @xcite is another top performing system for multilingual oa. recently, some works try to explore deep learning based oa methods. veealign @xcite 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.