公开(公告)号：US20140156231A1

公开(公告)日：2014-06-05

申请号：US13690071

申请日：2012-11-30

Applicant: XEROX CORPORATION

Inventor： Shengbo Guo ,  Boris Chidlovskii ,  Cedric Archambeau ,  Guillaume Bouchard ,  Dawei Yin

IPC: G06F17/18

CPC classification number: G06F17/18 ,  G06N7/005

Abstract: A multi-relational data set is represented by a probabilistic multi-relational data model in which each entity of the multi-relational data set is represented by a D-dimensional latent feature vector. The probabilistic multi-relational data model is trained using a collection of observations of relations between entities of the multi-relational data set. The collection of observations includes observations of at least two different relation types. A prediction is generated for an observation of a relation between two or more entities of the multi-relational data set based on a dot product of the optimized D-dimensional latent feature vectors representing the two or more entities. The training may comprise optimizing the D-dimensional latent feature vectors to maximize likelihood of the collection of observations, for example by Bayesian inference performed using Gibbs sampling.

Abstract translation: 多关系数据集由概率多关系数据模型表示，其中多关系数据集的每个实体由D维潜在特征向量表示。 概率多关系数据模型使用多关系数据集的实体之间的关系的观察集来训练。 观察的收集包括至少两种不同关系类型的观察。 生成用于基于代表两个或多个实体的优化的D维潜在特征向量的点积来观察多关系数据集的两个或多个实体之间的关系的预测。 该训练可以包括优化D维潜在特征向量以最大化观察的收集的可能性，例如通过使用吉布斯抽样执行的贝叶斯推理。

公开(公告)号：US20140156579A1

公开(公告)日：2014-06-05

申请号：US13689955

申请日：2012-11-30

Applicant: XEROX CORPORATION

Inventor： Guillaume Bouchard ,  Dawei Yin ,  Shengbo Guo

IPC: G06N5/02

CPC classification number: G06F17/16 ,  G06F17/30867 ,  G06N5/043

Abstract: A method operates on observed relationship data between pairs of entities of a set of entities including entities of at least two (and optionally at least three) different entity types. An observed collective symmetric matrix is constructed in which element (n,m)=element (m,n) stores the observed relationship between entities indexed n and m when the observed relationship data includes this observed relationship. A prediction collective symmetric matrix is optimized in order to minimize a loss function comparing the observed collective symmetric matrix and the prediction collective symmetric matrix. A relationship between two entities of the set of entities is predicted using the optimized prediction collective symmetric matrix. Entities of the same entity type may be indexed using a contiguous set of indices such that the entity type maps to a contiguous set of rows and corresponding contiguous set of columns in the observed collective symmetric matrix.

Abstract translation: 一种方法对包括至少两个（且可选地至少三个）不同实体类型的实体的一组实体对的实体对之间的观察关系数据进行操作。 构建观察到的集体对称矩阵，其中元素（n，m）=元素（m，n）存储当观察到的关系数据包括该观察到的关系时，被索引为n和m的实体之间观察到的关系。 优化预测集体对称矩阵，以便将观察到的集体对称矩阵和预测集体对称矩阵进行比较来最小化损失函数。 使用优化的预测集体对称矩阵来预测该组实体的两个实体之间的关系。 可以使用连续的索引集索引相同实体类型的实体，使得实体类型映射到观察到的集体对称矩阵中的连续的行集合和对应的连续的列集合。

公开(公告)号：US09058303B2

公开(公告)日：2015-06-16

申请号：US13689955

申请日：2012-11-30

Applicant: Xerox Corporation

Inventor： Guillaume Bouchard ,  Dawei Yin ,  Shengbo Guo

IPC: G06F17/16 ,  G06N5/04 ,  G06F17/30

CPC classification number: G06F17/16 ,  G06F17/30867 ,  G06N5/043

Abstract: A method operates on observed relationship data between pairs of entities of a set of entities including entities of at least two (and optionally at least three) different entity types. An observed collective symmetric matrix is constructed in which element (n,m)=element (m,n) stores the observed relationship between entities indexed n and m when the observed relationship data includes this observed relationship. A prediction collective symmetric matrix is optimized in order to minimize a loss function comparing the observed collective symmetric matrix and the prediction collective symmetric matrix. A relationship between two entities of the set of entities is predicted using the optimized prediction collective symmetric matrix. Entities of the same entity type may be indexed using a contiguous set of indices such that the entity type maps to a contiguous set of rows and corresponding contiguous set of columns in the observed collective symmetric matrix.

Abstract translation: 一种方法对包括至少两个（且可选地至少三个）不同实体类型的实体的一组实体对的实体对之间的观察关系数据进行操作。 构建观察到的集体对称矩阵，其中元素（n，m）=元素（m，n）存储当观察到的关系数据包括该观察到的关系时，被索引为n和m的实体之间观察到的关系。 优化预测集体对称矩阵，以便将观察到的集体对称矩阵和预测集体对称矩阵进行比较来最小化损失函数。 使用优化的预测集体对称矩阵来预测该组实体的两个实体之间的关系。 可以使用连续的索引集索引相同实体类型的实体，使得实体类型映射到观察到的集体对称矩阵中的连续的行集合和对应的连续的列集合。