Predicate Path Research Articles

Automated extraction of attributes of IFC objects based on graph theory and SPARQL query

Building Information Modelling (BIM) has been widely adopted as an effective means for supporting information exchange in Architectural, Engineering and Construction (AEC) industry based on unified and platform-independent standards. Among them, the Industry Foundation Classes (IFC), an ISO standard for BIM, is the most widely used one. However, due to the complexity and flexibility of IFC, the extraction of the attributes of IFC objects is a challenging process for practitioners and software developers in AEC industry, which impedes in-depth application of BIM in many cases. To solve the problem, an approach is proposed in this paper based on graph theory and SPARQL query to automate and simplify the process. The approach consists of four operators, i.e., IFC-to-OWL Convertor, P-Path Acquirer, SPARQL Query Generator, and Ontology Reasoner. IFC-to-OWL Convertor translates IFC instance model into ifcOWL instance model, which can be queried by using SPARQL. P-Path Acquirer obtains all possible Predicate Paths (P-Paths) from an object entity to an attribute entity in the graph created based on ifcOWL schema model. Then SPARQL Query Generator generates SPARQL queries for extracting attributes based on the P-Paths. Finally, the ifcOWL instance model and the SPARQL queries are input into Ontology Reasoner to query attributes of IFC objects. The approach is validated by conducting a case study. The approach contributes to the convenient application of IFC standards in AEC industry.

Discriminative predicate path mining for fact checking in knowledge graphs

Traditional fact checking by experts and analysts cannot keep pace with the volume of newly created information. It is important and necessary, therefore, to enhance our ability to computationally determine whether some statement of fact is true or false. We view this problem as a link-prediction task in a knowledge graph, and present a discriminative path-based method for fact checking in knowledge graphs that incorporates connectivity, type information, and predicate interactions. Given a statement S of the form (subject, predicate, object), for example, (Chicago, capitalOf, Illinois), our approach mines discriminative paths that alternatively define the generalized statement (U.S. city, predicate, U.S. state) and uses the mined rules to evaluate the veracity of statement S. We evaluate our approach by examining thousands of claims related to history, geography, biology, and politics using a public, million node knowledge graph extracted from Wikipedia and PubMedDB. Not only does our approach significantly outperform related models, we also find that the discriminative predicate path model is easily interpretable and provides sensible reasons for the final determination.

A multiple-population genetic algorithm for branch coverage test data generation

The software testing phase in the software development process is considered a time-consuming process. In order to reduce the overall development cost, automatic test data generation techniques based on genetic algorithms have been widely applied. This research explores a new approach for using genetic algorithms as test data generators to execute all the branches in a program. In the literature, existing approaches for test data generation using genetic algorithms are mainly focused on maintaining a single-population of candidate tests, where the computation of the fitness function for a particular target branch is based on the closeness of the input execution path to the control dependency condition of that branch. The new approach utilizes acyclic predicate paths of the program's control flow graph containing the target branch as goals of separate search processes using distinct island populations. The advantages of the suggested approach is its ability to explore a greater variety of execution paths, and in certain conditions, increasing the search effectiveness. When applied to a collection of programs with a moderate number of branches, it has been shown experimentally that the proposed multiple-population algorithm outperforms the single-population algorithm significantly in terms of the number of executions, execution time, time improvement, and search effectiveness.

PEWS: A New Language for Building Web Service Interfaces

Recent proposals in the domain of interface description languages for web services stress the importance of specifying the dynamic, behavioral aspects of the services. The goal of this paper is to introduce a new interface description language, called PEWS, that uses predicate path expressions to define web service behaviours. Our proposal represents a simple but expressive way to describe order and conditional constraints over web service operations. PEWS aims to be used not only to the speci- fication of simple web services but also to be a tool for describing service composition. In this paper, we use the Action Semantics framework to present the syntax and seman- tics of the most significant parts of PEWS and we introduce XPEWS, the XML-based version of PEWS used to publish service behaviours for future searches and composi- tion. The definition of XPEWS is done by giving the XML Schema that defines the syntax of XPEWS programs.