Academic literature on the topic 'Aggregate entity'

As a growing number of applications represent data as semantic graphs like RDF (Resource Description Format) and the many entity-attribute-value formats, query languages for such data are being required to support operations beyond graph pattern matching and inference queries. Specifically the ability to express aggregate queries is an important feature which is either lacking or is implemented with little attention to the peculiarities of the data model. In this paper, we study the meaning and implementation of grouping and aggregate queries over RDF graphs. We first define grouping and aggregate operators algebraically and then show how the SPARQL query language can be extended to express grouping and aggregate queries.

In order to improve utilization rate of high dimensional data features, an ensemble learning method based on feature selection for entity resolution is developed. Entity resolution is regarded as a binary classification problem, an optimization model is designed to maximize each classifier’s classification accuracy and dissimilarity between classifiers and minimize cardinality of features. A modified multiobjective ant colony optimization algorithm is employed to solve the model for each base classifier, two pheromone matrices are set up, weighted product method is applied to aggregate values of two pheromone matrices, and feature’s Fisher discriminant rate of records’ similarity vector is calculated as heuristic information. A solution which is called complementary subset is selected from Pareto archive according to the descending order of three objectives to train the given base classifier. After training all base classifiers, their classification outputs are aggregated by max-wins voting method to obtain the ensemble classifiers’ final result. A simulation experiment is carried out on three classical datasets. The results show the effectiveness of our method, as well as a better performance compared with the other two methods.

Few-shot knowledge graph completion (FKGC) task aims to infer missing entities or relations by using few-shot support instances in the knowledge graph. Existing FKGC methods focus on the learning of few-shot relation representations, which are obtained by aggregating the neighbor information of each entity. However, most of these models take the entity's neighbor relations and entities as the same hierarchy and do not make fine-grained distinctions, resulting in entity embeddings with low expressiveness, which may further decrease the quality of learned few-shot relation embeddings. Moreover, many of those models directly use the concatenation of the entity embeddings as the relation representations, and neglect the valuable interaction between relations. In this paper, we propose a fine-grained relational learning framework IDEAL for few-shot knowledge graph completion task. Specifically, we first propose a unique hierarchical attention encoder to aggregate the neighbor information of each entity from two levels, i.e., the entity-relation level and the relation-entity level. Then a relation recoding validator is proposed to formulate the interaction between different relations. Instead of obtaining the few-shot relation representations by using the entity embeddings, the relation recoding validator module aggregates the neighbor relations of each entity to encode the few-shot relation, which can reduce the over-dependence on specific entities in the few-shot relation encoding phase. The relation recoding module is also extended with respect to the excellent performance of the transformer in modeling sequence information. We then introduce a transformer encoder to extract underlying and valuable sequence information between relations. Extensive experiments are conducted on two datasets, i.e., NELL and Wiki. The experimental results demonstrate that our model outperforms state-of-the-art FKGC methods. Besides, we devise the ablation study to demonstrate the effectiveness of each key component. The case study also shows the interpretability of our model intuitively.

In this article I engage with the provisions of the Insolvency Act 24 of 1936 regulating the dissolution of the universal partnership upon insolvency. Our common law prefers an aggregate approach to partnerships, meaning that a partnership enjoys no separate legal personality distinct from its composing partners. The lack of separate legal personality of a partnership is described by some academics as a ‘remarkable defect’. The Insolvency Act however creates an exception to this general rule by deeming a partnership to be a separate legal entity. The Insolvency Act’s deviation from the common-law rule and creation of a ‘juristic ghost’ is explored in this article. The ‘dual priorities’ rule, the aggregate theory and the entity theory are explained in this article. Furthermore, the judicial debates about the Act’s deviation are discussed. In conclusion, it is suggested that the presumption that legislation does not intend to change existing law should not apply when dealing with the Insolvency Act, as the legislature has expressly deviated from the common-law aggregate approach.