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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 3 of 3 1 Can we predict new facts with open knowledge graph embeddings? A benchmark for open link prediction Gashteovski, Kiril; Gemulla, Rainer; Wang, Yanjie. - : Association for Computational Linguistics, 2020 BASE Show details 2 On evaluating embedding models for knowledge base completion Gemulla, Rainer; Wang, Yanjie; Broscheit, Samuel; Ruffiinelli, Daniel; Meilicke, Christian. - : Association for Computational Linguistics, 2019 Abstract: Knowledge graph embedding models have recently received significant attention in the literature. These models learn latent semantic representations for the entities and relations in a given knowledge base; the representations can be used to infer missing knowledge. In this paper, we study the question of how well recent embedding models perform for the task of knowledge base completion, i.e., the task of inferring new facts from an incomplete knowledge base. We argue that the entity ranking protocol, which is currently used to evaluate knowledge graph embedding models, is not suitable to answer this question since only a subset of the model predictions are evaluated. We propose an alternative entity-pair ranking protocol that considers all model predictions as a whole and is thus more suitable to the task. We conducted an experimental study on standard datasets and found that the performance of popular embeddings models was unsatisfactory under the new protocol, even on datasets that are generally considered to be too easy. Moreover, we found that a simple rule-based model often provided superior performance. Our findings suggest that there is a need for more research into embedding models as well as their training strategies for the task of knowledge base completion. Keyword: 004 Informatik URL: https://madoc.bib.uni-mannheim.de/52546/1/On%20Evaluating%20Embedding%20Models%20for%20Knowledge%20Base%20Completion.pdf https://madoc.bib.uni-mannheim.de/52546/ https://madoc.bib.uni-mannheim.de/52546 BASE Hide details 3 Contention of Lust, Caution: Sexuality, Visuality and Female Subjectivity Wang, Yanjie In: Asian and Asian American Studies Faculty Works (2010) BASE Show details

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