This document discusses hybrid enterprise knowledge graphs and the metaphactory platform. It describes how metaphactory uses a knowledge graph as an integration hub, connecting to various data sources like databases, APIs, and machine learning models through its Ephedra federation engine. Ephedra allows querying over these different data sources together using SPARQL 1.1 federation. It provides examples of use cases involving similarity search, sensor data, chemical structures, and demonstrates federation between Wikidata and other sources.

1. Hybrid Enterprise Knowledge Graphs
Peter Haase
ISWC 2019, Industry Track
28.10.2019, Auckland, New Zealand

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COMPANY FACTS
• metaphacts GmbH
• Founded in Q4 2014
• Headquartered in Walldorf, Germany
• Currently ~20 people
metaphacts at a Glance
• Independent software vendor
• Privately-held, owner-managed company
• Platform for knowledge graphs and
knowledge graph applications

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metaphactory Platform Architecture

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Knowledge graph as integration hub
”Master data” about business-relevant entities
Interlinked and federated with:
Hybrid Enterprise Knowledge Graphs
Graph database Machine learning model Custom APISpecialized indicesRelational Database
OBDA / ETL
Variety of data sources
• Local/remote RDF stores
• Virtual graphs over e.g. relational
• Enterprise APIs
• Data streams, sensor data
Variety of data modalities
• Text
• Temporal, geospatial
• Images
• Custom indices
Variety of data processing techniques
• Graph analytics
• Machine learning, statistics
• Domain-specific, e.g. genome
sequence similarity
Deep Learning
Service

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Hybrid Enterprise Knowledge Graphs in metaphactory
Relational Database
OBDA/ETL
Graph Database
SPARQL 1.1
Hybrid Services
REST APIs
• R2RML mappings
or query patterns
• Multiple graph
databases
• External and internal
data
• Machine Learning
Algorithms
• Data Feeds for
augmentation
Ephedra Federation Engine: Main Principles
• Extends RDF4J API
• Data sources and compute services are wrapped
into “virtual” RDF4J repositories
• Graph patterns are transformed into API calls
• SPARQL 1.1 federation using the SERVICE keyword
• Service wrapper repositories are explicitly
described in the service registry
• Explicit mappings between graph patterns and
service input/output parameters
• Built-in wrappers for relational, REST API
• Easy SDK for implementing own Service
wrappers
• Static and runtime optimizations for hybrid
queries

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• Explicit description of resources in
the knowledge graph
• Enrichment of the graph with an
implicit model, often a learned
model using machine learning
• Typical example: Similarity of
products, items, ...
• Range of different models
• Graph embeddings, word
embeddings
• Integration of model via API
• Can be computed at runtime or
pre-trained
Use Case: Similarity Search & Machine Learning
# Select a company similar to BMW
SELECT ?company WHERE {
SERVICE ephedra:word2vec {
:BMW word2vec:isSimilar ?company .
}
?company :headquarters ?location
}
word2vec
embeddings
in: URI out: URI[] ?company = :Audi:BMW

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• Knowledge graphs of smart
objects, example: trains
• Sensors make tempo-spatial
observations
• Interpretation of sensor and
image data requires
• Time and location
• Weather conditions
• Federation service to include
weather data via Dark Sky API
• Contextualization of sensor data
with weather information
Use Case: Sensor Data
SELECT ?weather WHERE {
?observation geo:lat ?latitude; geo:long ?longitude; :time ?time
SERVICE ephedra:weatherapi {
?results weatherapi:latitude ?latitude ;
weatherapi:longitude ?longitude;
weatherapi:time ?time;
weatherapi:summary ?weather.
}
}

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• Life science knowledge graphs e.g.
in drug research: compounds,
proteins, ...
• Integration of knowledge graph
with special purpose databases
operating on the chemical
structures
• Chemical Structure Search API
(REST) integrated via Service
wrapper
• Example: finding exact, similar, or
sub- structures
Use Case: Chemical Structure Search
SELECT ?substance ?similarity ?id ?inchi WHERE {
SERVICE ephedra:chemsimilarity {
?results chem:hasSMILES ?smilesCode .
?results chem:hasSimilarityThreshold ?similarityThreshold .
?results chem:hasMoleculeChEMBLID ?id .
?results chem:hasSimilarity ?similarity .
?results chem:hasStandardInChIKey ?inchi .
}
?substance :chemblID ?id
}

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Demo: Wikidata & Federated Sources
https://wikidata.metaphacts.com/
Poster & Demo Session
Today at 18:00

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• Hybrid enterprise knowledge graphs
• Core knowledge graph in RDF as integration hub
• Integration with enterprise sources (databases, APIs)
• Semi-/Unstructured: text, images, geo, …
• Compute services
• Machine learning models
• Ephedra: Federation over (virtual) SPARQL
endpoints and compute services
• Explicit mappings between SPARQL nodes and
service input/output parameters
• Static and runtime optimizations for hybrid queries
• Using SPARQL 1.1 federation
Future work
• SPARQL 1.2 federation
• Community working group
• Generalization of SERVICE for
non-SPARQL endpoints
• Integration with FedX federation
framework
• Recent integration into RDF4J
• Enabling transparent federation
• Improved optimization
techniques
Summary & Outlook

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metaphacts GmbH
Daimlerstraße 36
69190 Walldorf
Germany
p +49 6227 6989965
m +49 157 50152441
e info@metaphacts.com
@metaphacts
Get in Touch!

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# Select a company similar to BMW
SELECT ?company WHERE {
SERVICE ephedra:word2vec {
:BMW word2vec:isSimilar ?company .
}
?company :headquarters ?location
}
Describing services
word2vec
embeddings
in: URI out: URI[]
# Service type descriptor (extended SPIN)
ephedra:word2vec a eph:Service ;
eph:hasSPARQLPattern [
sp:subject :_inputURI ;
sp:predicate word2vec:hasSimilar ;
sp:object :_outputURI .
] ;
spin:constraint [ spl:predicate _inputURI ] ;
spin:column [ spl:predicate _outputURI ] .
Service
Registry
:BMW
?product =
:Audi
Matching service inputs/outputs to
SPARQL patterns