Template-Based SPARQL Query and Visualization on Knowledge Graphs

AbstractKnowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most notably RDF database systems that provide a SPARQL query interface. Surprisingly, machine learning tools for knowledge graphs do not use SPARQL, despite the obvious advantages of using a database system. This is due to the mismatch between SPARQL and machine learning tools in terms of data model and programming style. Machine learning tools work on data in tabular format and process it using an imperative programming style, while SPARQL is declarative and has as its basic operation matching graph patterns to RDF triples. We posit that a good interface to knowledge graphs from a machine learning software stack should use an imperative, navigational programming paradigm based on graph traversal rather than the SPARQL query paradigm based on graph patterns. In this paper, we present RDFFrames, a framework that provides such an interface. RDFFrames provides an imperative Python API that gets internally translated to SPARQL, and it is integrated with the PyData machine learning software stack. RDFFrames enables the user to make a sequence of Python calls to define the data to be extracted from a knowledge graph stored in an RDF database system, and it translates these calls into a compact SPQARL query, executes it on the database system, and returns the results in a standard tabular format. Thus, RDFFrames is a useful tool for data preparation that combines the usability of PyData with the flexibility and performance of RDF database systems.

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Fact Checking via Evidence Patterns

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/522 ◽

2018 ◽

Cited By ~ 3

Author(s):

Valeria Fionda ◽

Giuseppe Pirrò

Keyword(s):

Query Language ◽

State Of The Art ◽

Sparql Query ◽

Efficient Algorithms ◽

The State ◽

The Core ◽

Numerical Assessment ◽

Comparable Accuracy ◽

Fact Checking ◽

Knowledge Graphs

We tackle fact checking using Knowledge Graphs (KGs) as a source of background knowledge. Our approach leverages the KG schema to generate candidate evidence patterns, that is, schema-level paths that capture the semantics of a target fact in alternative ways. Patterns verified in the data are used to both assemble semantic evidence for a fact and provide a numerical assessment of its truthfulness. We present efficient algorithms to generate and verify evidence patterns, and assemble evidence. We also provide a translation of the core of our algorithms into the SPARQL query language. Not only our approach is faster than the state of the art and offers comparable accuracy, but it can also use any SPARQL-enabled KG.

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Predicting the relationships between gut microbiota and mental disorders with knowledge graphs

Health Information Science and Systems ◽

10.1007/s13755-020-00128-2 ◽

2020 ◽

Vol 9 (1) ◽

Author(s):

Ting Liu ◽

Xueli Pan ◽

Xu Wang ◽

K. Anton Feenstra ◽

Jaap Heringa ◽

...

Keyword(s):

Mental Disorders ◽

Gut Microbiota ◽

Research Effort ◽

Sparql Query ◽

Knowledge Graph ◽

Test Cases ◽

Relevant Research ◽

Structured Knowledge ◽

Knowledge Graphs ◽

The Relationship

AbstractGut microbiota produce and modulate the production of neurotransmitters which have been implicated in mental disorders. Neurotransmitters may act as ‘matchmaker’ between gut microbiota imbalance and mental disorders. Most of the relevant research effort goes into the relationship between gut microbiota and neurotransmitters and the other between neurotransmitters and mental disorders, while few studies collect and analyze the dispersed research results in systematic ways. We therefore gather the dispersed results that in the existing studies into a structured knowledge base for identifying and predicting the potential relationships between gut microbiota and mental disorders. In this study, we propose to construct a gut microbiota knowledge graph for mental disorder, which named as MiKG4MD. It is extendable by linking to future ontologies by just adding new relationships between existing information and new entities. This extendibility is emphasized for the integration with existing popular ontologies/terminologies, e.g. UMLS, MeSH, and KEGG. We demonstrate the performance of MiKG4MD with three SPARQL query test cases. Results show that the MiKG4MD knowledge graph is an effective method to predict the relationships between gut microbiota and mental disorders.

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Ontology-Mediated SPARQL Query Answering over Knowledge Graphs

Big Data Research ◽

10.1016/j.bdr.2020.100177 ◽

2020 ◽

pp. 100177

Author(s):

Guohui Xiao ◽

Julien Corman

Keyword(s):

Sparql Query ◽

Query Answering ◽

Knowledge Graphs

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Mobile Software Assurance Informed through Knowledge Graph Construction: The OWASP Threat of Insecure Data Storage

Journal of Computer Science Research ◽

10.30564/jcsr.v2i2.1765 ◽

2020 ◽

Vol 2 (2) ◽

Author(s):

Suzanna Schmeelk ◽

Lixin Tao

Keyword(s):

Data Storage ◽

Program Analysis ◽

Web Application ◽

Security Analysis ◽

Knowledge Graph ◽

Healthcare Applications ◽

Sensitive Data ◽

Knowledge Graphs ◽

Mobile Malware Detection ◽

Software Assurance

Many organizations, to save costs, are movinheg to t Bring Your Own Mobile Device (BYOD) model and adopting applications built by third-parties at an unprecedented rate. Our research examines software assurance methodologies specifically focusing on security analysis coverage of the program analysis for mobile malware detection, mitigation, and prevention. This research focuses on secure software development of Android applications by developing knowledge graphs for threats reported by the Open Web Application Security Project (OWASP). OWASP maintains lists of the top ten security threats to web and mobile applications. We develop knowledge graphs based on the two most recent top ten threat years and show how the knowledge graph relationships can be discovered in mobile application source code. We analyze 200+ healthcare applications from GitHub to gain an understanding of their software assurance of their developed software for one of the OWASP top ten moble threats, the threat of “Insecure Data Storage.” We find that many of the applications are storing personally identifying information (PII) in potentially vulnerable places leaving users exposed to higher risks for the loss of their sensitive data.

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