[PDF] Top 20 Contextualized ranking of entity types based on knowledge graphs

... coarser types are always prioritized, when computing the score of an entity type we take into consideration the entropy its children’s probabilities, and that of its sibling’s probabilities, ... See full document

... neural ranking model, K-NRM (Xiong et ...integrating knowledge graphs using EDRM significantly improves the neural model’s generalization ability on more difficult ... See full document

... entities based on various domain knowledge and interactions among these ...bipartite graphs where each pair of entity types ...as ranking of low- reputation entities and ... See full document

... the ranking that is discounted at lower ranks and is normalized by the ideal cumulative ...these ranking- based graded evaluation metrics at two cut-off points: position 1, corresponding to showing a ... See full document

... entities are linked to Freebase, which will identify entities that represent one object but with different instances. Secondly, the triple is associated with the parsed sentences and the document ID, which can provide ... See full document

... external knowledge into source ...the entity to its corresponding Wiki page (Bunes- cu and Pasca, 2006; Cucerzan, ...mentions based on the coherence among decisions. Despite the success, the ... See full document

... attribute ranking algorithms is extremely difficult in the absence of a standard ...human ranking annotations for entity ...that entity and that this ranking should be valid for generic ... See full document

... graph ranking, where all nodes are ranked according to the link ...Graph Ranking: The links between different candidates in the solution graph represent real world ...is based solely on the im- ... See full document

... Chemical patents are an important starting point for understanding of chemical compound purpose, properties, and novelty. New chemical compounds are often initially disclosed in patent documents; however it may take 1-3 ... See full document

... syntactic knowledge, we eliminated the identification of grammatical relations from our grid computation and recorded solely whether an entity is present or absent in a ... See full document

... and entity em- ...and entity embeddings to the word vec- tors on the token ...and entity embeddings for all entity men- tions (Figure ...adapted based on the semantically enriched ... See full document

... an entity filtering algorithm to return entity candidates that improves over trivial association ...each entity candidate is matched with a new representation built on a sub- graph centered on their ... See full document

... In order to select the promising genotypes from the experiment, ranking of genotypes was done based on the mean performance of the genotypes with respect to eight different biochemical c[r] ... See full document

... Anatomy Knowledge Graphs can be organized into several such sub-graphs, with each named graph instance having its own ...Named graphs of the same type can be orga- nized within an ontology as ... See full document

... We asked 3 language teaching experts for their opinion on the usefulness of such techniques for possible applications in language learning. The experts pointed to applications that enhance receptive (comprehension) and ... See full document

... new ranking scheme called collab- orative ranking with three specific forms, MiCR, MaCR and MiMaCR and demonstrated its effective- ness on entity linking ... See full document

... the entity-centric system with model stacking (Clark and Manning, 2015); and Non-Linear: the non- linear mention-ranking model with feature represen- tations (Wiseman et ...unsupervised ranking model ... See full document

... key based matching, the author propose a multi-pass strategy, running several time sorted neighborhood algorithm using a different key all the ...robust entity matching, as it would rely on ... See full document

... and Towsley, 2016; Hamilton et al., 2017; Veliˇckovi´c et al., 2018]. The Graph Convolutional Networks (GCNs) [Kipf and Welling, 2017] have recently emerged as a powerful deep learning-based approach for many ... See full document

... Word embeddings are a crucial component in many NLP approaches (Mikolov et al., 2013; Pen- nington et al., 2014) since they capture latent se- mantics of words and thus allow models to bet- ter train and generalize. ... See full document