Materializing Inferred and Uncertain Knowledge in RDF Datasets

AAAI Conferences

There is a growing need for efficient and scalable semantic web queries that handle inference. There is also a growing interest in representing uncertainty in semantic web knowledge bases. In this paper, we present a bit vector schema specifically designed for RDF (Resource Description Framework) datasets. We propose a system for materializing and storing inferred knowledge using this schema. We show experimental results that demonstrate that our solution drastically improves the performance of inference queries. We also propose a solution for materializing uncertain information and probabilities using multiple bit vectors and thresholds.


Materializing and Persisting Inferred and Uncertain Knowledge in RDF Datasets

AAAI Conferences

As the semantic web grows in popularity and enters the mainstream of computer technology, RDF (Resource Description Framework) datasets are becoming larger and more complex. Advanced semantic web ontologies, especially in medicine and science, are developing. As more complex ontologies are developed, there is a growing need for efficient queries that handle inference. In areas such as research, it is vital to be able to perform queries that retrieve not just facts but also inferred knowledge and uncertain information. OWL (Web Ontology Language) defines rules that govern provable inference in semantic web datasets. In this paper, we detail a database schema using bit vectors that is designed specifically for RDF datasets. We introduce a framework for materializing and storing inferred triples. Our bit vector schema enables storage of inferred knowledge without a query performance penalty. Inference queries are simplified and performance is improved. Our evaluation results demonstrate that our inference solution is more scalable and efficient than the current state-of-the-art. There are also standards being developed for representing probabilistic reasoning within OWL ontologies. We specify a framework for materializing uncertain information and probabilities using these ontologies. We define a multiple vector schema for representing probabilities and classifying uncertain knowledge using thresholds. This solution increases the breadth of information that can be efficiently retrieved.


RDFKB: A Semantic Web Knowledge Base

AAAI Conferences

There are many significant research projects focused on providing semantic web repositories that are scalable and efficient. However, the true value of the semantic web architecture is its ability to represent meaningful knowledge and not just data. Therefore, a semantic web knowledge base should do more than retrieve collections of triples. We propose RDFKB (Resource Description Knowledge Base), a complete semantic web knowledge case. RDFKB is a solution for managing, persisting and querying semantic web knowledge. Our experiments with real world and synthetic datasets demonstrate that RDFKB achieves superior query performance to other state-of-the-art solutions. The key features of RDFKB that differentiate it from other solutions are: 1) a simple and efficient process for data additions, deletions and updates that does not involve reprocessing the dataset; 2) materialization of inferred triples at addition time without performance degradation; 3) materialization of uncertain information and support for queries involving probabilities; 4) distributed inference across datasets; 5) ability to apply alignments to the dataset and perform queries against multiple sources using alignment. RDFKB allows more knowledge to be stored and retrieved; it is a repository not just for RDF datasets, but also for inferred triples, probability information, and lineage information. RDFKB provides a complete and efficient RDF data repository and knowledge base.


On Reasoning with RDF Statements about Statements using Singleton Property Triples

arXiv.org Artificial Intelligence

The Singleton Property (SP) approach has been proposed for representing and querying metadata about RDF triples such as provenance, time, location, and evidence. In this approach, one singleton property is created to uniquely represent a relationship in a particular context, and in general, generates a large property hierarchy in the schema. It has become the subject of important questions from Semantic Web practitioners. Can an existing reasoner recognize the singleton property triples? And how? If the singleton property triples describe a data triple, then how can a reasoner infer this data triple from the singleton property triples? Or would the large property hierarchy affect the reasoners in some way? We address these questions in this paper and present our study about the reasoning aspects of the singleton properties. We propose a simple mechanism to enable existing reasoners to recognize the singleton property triples, as well as to infer the data triples described by the singleton property triples. We evaluate the effect of the singleton property triples in the reasoning processes by comparing the performance on RDF datasets with and without singleton properties. Our evaluation uses as benchmark the LUBM datasets and the LUBM-SP datasets derived from LUBM with temporal information added through singleton properties.


Exploiting Incremental Reasoning in Healthcare Based on Hadoop and Amazon Cloud

AAAI Conferences

With a large volume of semantic data and their fast growth in semantic cities, significant challenges in performing efficient and scalable reasoning has emerged in diverse domains. When dealing with large-scale ontologies, the performance of traditional centralized reasoning methods is not sufficient, distributed reasoning methods have thus emerged to improve the scalability and efficiency of inferences. In this paper, an incremental and distributed reasoning method for large-scale ontologies is proposed to realize high-performance reasoning and online query. A novel representation method, transfer reasoning tree and underived assertional triples, is presented to store the incremental ontologies more efficiently, based on which the reasoning process is accelerated and ontology inconsistency is recovered. Finally, a system is implemented on Hadoop and Amazon Cloud, and its application in healthcare validates the effectiveness of the proposed approach.