Ontology-Mediated SPARQL Query Answering over Knowledge Graphs

2020 ◽  
pp. 100177
Author(s):  
Guohui Xiao ◽  
Julien Corman
Keyword(s):  
Sparql Query ◽  
Query Answering ◽  
Knowledge Graphs

scholarly journals RDFFrames: knowledge graph access for machine learning tools

2021 ◽  
Author(s):  
Aisha Mohamed ◽  
Ghadeer Abuoda ◽  
Abdurrahman Ghanem ◽  
Zoi Kaoudi ◽  
Ashraf Aboulnaga
Keyword(s):  
Machine Learning ◽  
Database Systems ◽  
Database System ◽  
Sparql Query ◽  
Knowledge Graph ◽  
Learning Tools ◽  
Tabular Format ◽  
Knowledge Graphs ◽  
Rdf Database ◽  
Learning Software

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.

scholarly journals Fact Checking via Evidence Patterns

2018 ◽  
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.

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

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.

scholarly journals SUMA: A Partial Materialization-Based Scalable Query Answering in OWL 2 DL

2021 ◽  
Author(s):  
Xiaoyu Qin ◽  
Xiaowang Zhang ◽  
Muhammad Qasim Yasin ◽  
Shujun Wang ◽  
Zhiyong Feng ◽  
...  
Keyword(s):  
Large Datasets ◽  
Sparql Query ◽  
Query Answering ◽  
Query Rewriting ◽  
Prototype System ◽  
Implicit Information ◽  
Ontology Language ◽  
Query Engine ◽  
The Cost ◽  
Partial Materialization

AbstractOntology-mediated querying (OMQ) provides a paradigm for query answering according to which users not only query records at the database but also query implicit information inferred from ontology. A key challenge in OMQ is that the implicit information may be infinite, which cannot be stored at the database and queried by off -the -shelf query engine. The commonly adopted technique to deal with infinite entailments is query rewriting, which, however, comes at the cost of query rewriting at runtime. In this work, the partial materialization method is proposed to ensure that the extension is always finite. The partial materialization technology does not rewrite query but instead computes partial consequences entailed by ontology before the online query. Besides, a query analysis algorithm is designed to ensure the completeness of querying rooted and Boolean conjunctive queries over partial materialization. We also soundly and incompletely expand our method to support highly expressive ontology language, OWL 2 DL. Finally, we further optimize the materialization efficiency by role rewriting algorithm and implement our approach as a prototype system SUMA by integrating off-the-shelf efficient SPARQL query engine. The experiments show that SUMA is complete on each test ontology and each test query, which is the same as Pellet and outperforms PAGOdA. Besides, SUMA is highly scalable on large datasets.

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