Dangling entity detection
WebWe propose three techniques for dangling entity detection that are based on the distribution of nearest-neighbor distances, i.e., nearest neighbor classification, marginal ranking and background ranking. After detecting and removing dangling entities, an incorporated entity alignment model in our framework can provide more robust … WebMar 9, 2024 · The dangling entity set is unavailable in most real-world scenarios, and manually mining the entity pairs that consist of entities with the same meaning is labor-consuming. In this paper, we...
Dangling entity detection
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WebMar 11, 2024 · In the experimental part, we first show the superiority of SoTead on a commonly-used entity alignment dataset. Besides, to analyze the ability for dangling … WebJun 28, 2024 · DBP2.0 DBP2.0 is a dataset for entity alignment with dangling cases, proposed by the ACL-2024 paper "Knowing the No-match: Entity Alignment with Dangling Cases". Browse Search Explore more content DBP2.0.zip(46.46 MB) File infoDownload file Fullscreen DBP2.0 CiteDownload(46.46 MB)ShareEmbed dataset
Web071 high-order proximity for dangling entity detection. 072 Despite a fairly high cosine similarity, the source 073 entity 4 is not the nearest neighbor of target entity 074 B, indicating that 4 is likely to be dangling. In con-075 trast, 1 and A are mutual nearest neighbors even 076 with a relatively low similarity, indicating that 1 is WebMar 9, 2024 · In this paper, we propose a novel accurate Unsupervised method for joint Entity alignment (EA) and Dangling entity detection (DED), called UED. The UED …
WebJun 4, 2024 · A novel accurate Unsupervised method for joint Entity alignment (EA) and Dangling entity detection (DED), called UED, which mines the literal semantic information to generate pseudo entity pairs and globally guided alignment information for EA and then utilizes the EA results to assist the DED. 1 Highly Influenced PDF WebJun 4, 2024 · We propose three techniques for dangling entity detection that are based on the distribution of nearest-neighbor distances, i.e., nearest neighbor classification, marginal ranking and background ...
WebWe propose three techniques for dangling entity detection that are based on the distribution of nearest-neighbor distances, i.e., nearest neighbor classification, marginal ranking and background ranking.
WebWe further discover that the dangling entity detection module can, in turn, improve alignment learning and the final performance. The contributed resource is publicly available to foster further research. This paper studies a new problem setting of entity alignment for knowledge graphs (KGs). Since KGs possess different sets of entities, there ... dewey vs library of congressWebMar 10, 2024 · The dangling entity set is unavailable in most real-world scenarios, and manually mining the entity pairs that consist of entities with the same meaning is labor … churchpartner suppliesWebSep 16, 2024 · Prevalence of Dangling Domains. With our dangling domain detector, we have detected 317,000 unsafe dangling domains in total. Figure 2 shows the breakdown … church party room rental near meWebMar 11, 2024 · To improve EA with dangling entities, we propose an unsupervised method called Semi-constraint Optimal Transport for Entity Alignment in Dangling cases (SoTead). Our main idea is to model the entity alignment between two KGs as an optimal transport problem from one KG's entities to the others. church pastoral aid society patronage trustWebMar 11, 2024 · In the experimental part, we first show the superiority of SoTead on a commonly-used entity alignment dataset. Besides, to analyze the ability for dangling entity detection with other baselines, we construct a medical cross-lingual knowledge graph dataset, MedED, where our SoTead also reaches state-of-the-art performance. PDF … church party games for adultsWebentity alignment with dangling cases, as illustrated inFig. 2. It has two jointly optimized modules, i.e., entity alignment and dangling entity detection. The entity alignment … dewey vs montessoriWebIn this way, we use the distribution as prole of the neighborhood of a source entity s, then we adopt a simple feed-forward neural network (FNN) bi- nary classier to determine whether s is dangling. The probability of s being a dangling entity can be calculated as p(y = 1 js) = sigmoid (FNN (d )). dewey v white 1827