Probabilistic Knowledge Graph Construction

Abstract

Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical analysis on the extracted information (knowledge completion). In many cases, insufficient external resources in the knowledge population hinder the subsequent statistical inference. The gap between these two processes can be reduced by an incremental population approach. We propose a new probabilistic knowledge graph factorisation method that benefits from the path structure of existing knowledge (e.g. syllogism) and enables a common modelling approach to be used for both incremental population and knowledge completion tasks. More specifically, the probabilistic formulation allows us to develop an incremental population algorithm that trades off exploitation-exploration. Experiments on three benchmark datasets show that the balanced exploitation-exploration helps the incremental population, and the additional path structure helps to predict missing information in knowledge completion.

Autonomic computing and incremental learning for the management of big services

“…To build the BSKG, we followed two main steps: population and completion. 56,57 In our work, we performed the population step by extracting relevant data and useful knowledge from various domain-specific sources, such as service description documents and data. While for the completion operation, we aim to enrich the knowledge graph by predicting the latent relationships (see dashed lines in Figure 4) between the different entities within the BSKG.…”

“…To build the BSKG, we followed two main steps: population and completion 56,57 . In our work, we performed the population step by extracting relevant data and useful knowledge from various domain‐specific sources, such as service description documents and data.…”

An Evidence Theory Based Embedding Model for the Management of Smart Water Environments

“…Incorporating such models (e.g., latent probabilistic models, latent Dirichlet allocation, probabilistic matrix factorization, probability relevance, and probability ranking principles) to decision support systems has been a promising approach. Probabilistic knowledge graph embedding has been applied in some domain-independent approaches [ 28 , 29 ]. However, this technique has not yet been exploited in water management.…”

“…In real-world uncertain knowledge graphs such as ConceptNet, NELL, and ProBase, relations and facts are associated with a confidence score [ 30 ]. Currently, there are few alternatives to capture uncertainty information with knowledge graph embeddings [ 28 , 29 ]. To achieve the goal of water monitoring under uncertain water zones’ contexts, it is important to encode additional information (e.g., truth degrees of water measurements) to preserve uncertainty.…”

“…Probabilistic models have gained widespread acceptance in different domains, particularly recommender systems [ 31 , 32 , 33 , 34 , 35 ]. Probabilistic knowledge graph embedding has also been applied in some domain-independent approaches [ 28 , 29 ]. However, uncertain and probabilistic embedding have not yet been exploited in the field of water monitoring.…”