Recently, it has gained lots of interests to jointly learn the embeddings of knowledge graph (KG) and text information. However, previous work fails to incorporate the complex structural signals (from structure representation) and semantic signals (from text representation). This paper proposes a novel text-enhanced knowledge graph representation model, which can utilize textual information to enhance the knowledge representations. Especially, a mutual attention mechanism between KG and text is proposed to learn more accurate textual representations for further improving knowledge graph representation, within a unified parameter sharing semantic space. Different from conventional joint models, no complicated linguistic analysis or strict alignments between KG and text are required to train our model. Besides, the proposed model could fully incorporate the multi-direction signals. Experimental results show that the proposed model achieves the state-of-the-art performance on both link prediction and triple classification tasks, and significantly outperforms previous text-enhanced knowledge representation models.
cornea cv. Yu Muer) is a new white variety of edible fungus that was selected from a mutant of Auricularia cornea by the Engineering Research Center of the Ministry of Education, Jilin Agricultural University. Yu Muer is in genus Auricularia, family Auriculariaceae, order Auriculariales, class Agaricomycetes, and phylum Basidiomycota. It is an edible fungus and is also used in medicine (Royse, 2014; Wang, Jiang, et al., 2019a; Wang, Li, et al., 2019b). The fruiting bodies of Yu Muer are thick, tender, and crispy tastes like jellyfish, and have a jade-like warm, soft color. It is rich in nutrients, including physiologically active substances such as polysaccharides,
Abstract. One of the most useful measurements of community detection quality is the modularity, which evaluates how a given division deviates from an expected random graph. This article demonstrates that (i) modularity maximization can be transformed into versions of the standard minimum-cut graph partitioning, and (ii) normalized version of modularity maximization is identical to normalized cut graph partitioning. Meanwhile, we innovatively combine the modularity theory with popular statistical inference method in two aspects: (i) transforming such statistical model into null model in modularity maximization; (ii) adapting the objective function of statistical inference method for our optimization. Based on the demonstrations above, this paper proposes an efficient algorithm for community detection by adapting the Laplacian spectral partitioning algorithm. The experiments, in both real-world and synthetic networks, show that both the quality and the running time of the proposed algorithm rival the previous best algorithms.
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