Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven Recipes

making sense of text and data
Atanas Kiryakov
Webinar, July 2020
Reasoning with Big Knowledge Graphs:
Choices, Pitfalls and Proven Recipes

Who are we?
o Leader
ü Semantic technology vendor established year 2000
ü Part of Sirma Group: 400 persons, listed at Sofia Stock Exchange
o Profitable and growing
ü Global: 80% of revenue from London and New York
ü Clients: S&P, BBC, FT, Top-5 US Bank, UK Parliament, Fujitsu, …
ü Verticals: Financial services, Health care and Life sciences, Publishing, Manufacturing
o Innovator
ü Attracted over $15M in innovation funding
ü Member of W3C, EDMC, ODI, STI and LDBC, developing next gen. standards

…, the market leaders in this space
continue to be Neo4J and Ontotext
(GraphDB), which are graph and RDF
database providers respectively.
These are the longest established
vendors in this space (both founded
in 2000) so they have a longevity and
experience that other suppliers
cannot yet match.
Bloor Research
Graph Database Market Update 2020
Ontotext GraphDB™ - the Flagship Product

Presentation Outline
o Reasoning Introduction: Benefits and Pitfalls
o Reasoning Use Cases and Demos
o RDFS and OWL 2 Profiles
o Reasoning Implementation Choices
o Reasoning With GraphDB

Knowledge Graphs = Rich Data in Context
KGs put data in context via
linking and semantic metadata
We help enterprises get profound insights
via interlinking, analyzing and exploring:
o diverse databases
o text documents and other content
o proprietary & global data

What is a Knowledge Graph?
o The KG represents a collection
of interlinked descriptions
of concepts and entities
ü Concepts describe each other
ü Connections provide context
ü Context helps comprehension!
o A KG can be used as:
ü Database: can be queried
ü Graph: can be analyzed as network
ü Knowledge base: new facts can be inferred
Read more: https://www.ontotext.com/knowledgehub/fundamentals/what-is-a-knowledge-graph/

What is Semantics?
o Formal semantics allows new valid
facts to be inferred
ü Both data and schema can be interpreted
ü Semantic schema = ontology
ü Languages: RDF Schema (RDFS), OWL
o Only the relevant semantics is
formalized in the schema
ü The meaning of relativeOf is not fully described by
defining it as owl:SymmetricProperty
ü The best model is the simplest one that can do the
work. But not simpler! myData: Maria
ptop:Agent
ptop:Person
ptop:Woman
ptop:childOf
ptop:parentOf
rdfs:range
owl:inverseOf
inferred
myData:Ivan
owl:relativeOf
owl:inverseOfowl:SymmetricProperty
rdfs:subPropertyOf
owl:inverseOf
owl:inverseOf
rdf:type
rdf:type
rdf:type

Reasoning Benefits
o Schema alignment and easy querying in diverse datasets
ü Across sources similar relationships can be modeled in a different way - one can use parentOf, another
childOf and a third one just the more general relativeOf
ü The database will return Ivan as a result of the query (Maria relativeOf ?x) when the fact derived from
the source and asserted is (Ivan childOf Maria)
o Getting deeper and more complete results
ü Finding patterns and inferring new relationships
ü Instant discovery of hidden relationships scattered across multiple sources
o Consistency checking and quality validation
ü RDF Shapes ensure graph consistency and quality

The Pitfalls of Reasoning
o Over-engineered ontologies
ü Too expressive ontology language
ü Results of inference hard to understand and verify
ü Performance penalties far greater than the benefits
o Inappropriate reasoning support
ü Inference implementations that work well with taxonomies and conceptual models of few
thousands of concepts, but cannot cope with KG of millions of entities
o Inappropriate data layer architecture
ü One such example is reasoning with virtual KG, which is often infeasible

Search in British Museum’s Collection
o Artefacts are described via the granular ontology CIDOC CRM
o Searching in such collection requires Fundamental Relations
ü Aggregation of large number of paths through CRM data into a smaller number of searchable relations
o E.g.: FR "Thing from Place"

British Museum’s Collection: Volumetrics
o Museum objects: 2,051,797
ü Thesaurus entries: 415,509
o Explicit statements: 195,208,156
o Total statements: 916,735,486
ü Expansion ratio is 4.7x, i.e., for each statement, 3.7 more are inferred
ü Nodes (unique URLs and literals): 53,803,189
o Loading time (including materialization):
ü 22.2h on RAM drive
ü 32.9h on non-SSD hard drives

GraphDB Benchmarking
o LDBC: TPC-like benchmarks for graph databases
o Members include: Ontotext, OpenLink, neo4j, CWI, UPM, ORACLE,
IBM, *Sparsity
o LDBC Semantic Publishing Benchmark
ü Based on BBC’s Dynamic Semantic Publishing editorial workflow
ü Updates, adding new content metadata or updating the reference knowledge (e.g., new people)
ü Aggregation queries retrieve content according to various criteria (e.g., to generate a topic web page)
ü The only benchmark that involves reasoning and updates

LDBC SPB Results of GraphDB
Clients
reading / writing Reads/s Writes/s
0 / 1 0.0000 11.4067
0 / 2 0.0000 14.3033
0 / 4 0.0000 14.6700
0 / 8 0.0000 15.1067
1 / 0 17.8258 0.0000
4 / 0 43.0833 0.0000
8 / 0 70.3767 0.0000
16 / 0 83.2633 0.0000
8 / 2 52.5667 9.2867
8 / 4 54.0233 9.6167
8 / 8 54.9067 9.5733
10 / 2 59.9467 8.5333
10 / 4 62.2867 8.4767
10 / 8 61.7167 8.6067
16 / 2 68.8100 5.0600
16 / 4 70.3900 5.1067
16 / 8 70.2300 4.9967
16 / 16 70.9467 5.0567
o CPU: 1 x E5-1650
o RAM: 20G heap
o Dataset: LDBC SPB 256
o DB: GraphDB SE 8.0, RDF Statements:
254,948,985 (explicit), 480,405,141 (total)
OWL-Horst-optimized rule set
o Creative works: 8,821,535

FactForge: Data Integration
o DBpedia (the English version) 496M
o GeoNames (all geographic features on Earth) 150M
o owl:sameAs links between DBpedia and Geonames 471K
o GLEI (global company register data) 3M
o Panama Papers DB (#LinkedLeaks) 20M
o Other datasets and ontologies: WordNet, WorldFacts, FIBO
o News metadata (2000 articles/day enriched by NOW) 1 023M
o Total size (2.2B explicit + 328M inferred statements) 2 522М

FIBO: Financial Industry Business Ontology
o Developed by EDMC, https://spec.edmcouncil.org/fibo/
o We loaded FIBO Foundations and BE
ü About 35 RDF files all together (old version)
o Reasoning profile: OWL 2 RL
o Loading takes 2-3 sec.
o Number of explicit statements: 5 696
o Number of total statements, including inferred: 15 713
ü About 10k statements materialized

FIBO-PROTON Mapping
o PROTON is an upper-level ontology
ü 500 classes, 200 properties; developed by Ontotext since 2004
ü used semantic annotation and LOD integration services, e.g, FactForge
ü mapped to DBPedia, Freebase, GeoNames
o A very basic mapping for public companies and few related
properties was loaded in 4 hours in FactForge:
fb:business.issuer rdfs:subClassOf pext:PublicCompany.
pext:PublicCompany rdfs:subClassOf fibo-be-corp-corp:PubliclyHeldCompany.
ptop:Organization rdfs:subClassOf fibo-fnd-org-fm:FormalOrganization.
dbp-prop:industry rdfs:subPropertyOf pext:industryOf.
pext:industryOf rdfs:subPropertyOf fibo-fnd-rel-rel:isClassifiedBy.
dbp-ont:subsidiary rdfs:subPropertyOf ptop:controls.
ptop:controls rdfs:subPropertyOf fibo-fnd-rel-rel:controls.

Rule-Based Reasoning
o Description Logic (DL) doesn’t scale
ü Satisfiability checking is not tractable
ü Complexity grows exponentially with size
o Rule-based inference engine
ü R-Entailment rules, PROLOG-style, as defined in [1]
o Sound and complete in PSPACE
ü Under some constraints: do not introduce
blank nodes, bound size of the rule bodies,
ground RDF graph, [1]
[1] Combining RDF and Part of OWL with Rules: Semantics, Decidability, Complexity
Herman J. ter Horst ,Published in International Semantic Web Conference 2005
More at: http://graphdb.ontotext.com/documentation/standard/reasoning.html
Complexity*
DLRules, LP
OWL Full
OWL DL
OWL Lite
RDFS
SWRL
Datalog
OWL 2 QL
Expressivity supported
by GraphDB
OWL 2 RL
OWL Horst

Forward-Chaining and Materialization
o All possible inferences are made upon update and are stored
ü The inferred statements are stored and indexed along the explicit ones
ü Interferences that are no longer supported upon delete are retracted
o Forward-chaining works, subject to conscious modeling
ü The overheads of the materialization approach are bearable
ü Say, 2x index size and 2x slower loading and updates
ü Marginal (if any) slowdown of queries

Query-time Reasoning and Backward-Chaining
o Perform reasoning query-time
ü No overhead upon data loading and updates
ü Two basic approaches: Backward-chaining and Query rewriting
o Backward-chaining slows down query evaluation dramatically
ü Alike PROLOG unification, the engine “dives” recursively, in order to exhaust all alternative
ways to find bindings for each separate triple pattern in the query
ü There is no way to guess before the actual evaluation the cardinality of the results for each
triple pattern
ü This makes query plan optimization impossible and ruins query performance

Query Rewriting
o Each pattern in the query is rewritten as disjunction of several
alternatives, based on reasoning on the schema/ontology/TBox
<?a rdf:type ptop:Person> query pattern will be expanded to something like
<?a rdf:type ptop:Person> OR
(<?p rdfs:range ptop:Person> AND <?b ?p ?a>) OR
(<?a rdf:type ?c> AND <?c rdfs:subClassOf ptop:Person >) …
o Execution of 10s combinations of variants is slow
ü Imagine a query with two patterns: the first one expands into 5 variants and the second into 6
variants. The engine will have to evaluate 30 alternative combinations
ü Think of implementing the semantics of owl:sameAs via query rewriting
o Query rewriting also delivers incomplete results
ü Recursion is not possible with SPARQL query rewriting

o Reasoning Introduction: Benefits and Pitfalls
o Reasoning Use Cases and Demos
o RDFS and OWL 2 Profiles
o Reasoning Implementation Choices
o GraphDB
o Reasoning with GraphDB
o Reasoning Optimizations in GraphDB

GraphDB Essentials
o Scalable RDF / SPARQL engine
ü W3C standards support
ü NEW: RDF* support, property annotations
o Platform independent (100% Java)
o Open source API
ü Main contributor to the RDF4J project
o Reasoning and consistency checking
ü UNIQUE! Efficient reasoning support for big data
sets across the full lifecycle of the data: load, query, updates

Architecture
GraphDB Workbench
User friendly interface for database
administration
GraphDB Engine
REST API for database access
Plugin / Connectors

GraphDB Workbench
o SPARQL editor & autocomplete
o Schema visualization
o Graph exploration
o Database monitoring and administration
9/10/20

Features Free Standard Enterprise
RDF 1.1 support
SPARQL 1.1 support
RDFS, OWL2 RL and QL reasoning
Efficient query execution
Workbench interface
Community support
Unlimited number of CPU cores
Commercial support
Connectors for Elasticsearch & SOLR
High-availability cluster
Managed service
GraphDB Enterprise: Resilience & Availability

Reasoning in GraphDB
o Fast forward-chaining materialization
ü Allows for efficient query evaluation on big datasets
o Incremental for both inserts and deletes
ü Inferred closure is updated transparently upon commit of transaction
o Sample rules:
ENTAILMENT CONSISITENCY
p <rdf:type> <owl:FunctionalProperty> x owl:sameAs y
x p y x owl:differentFrom y
x p z ------------------------
-------------------------------
y <owl:sameAs> z

OWL 2 Reasoning
o Built-in rule-sets for: RDFS, OWL-Horst, OWL2-RL, OWL2-QL
o Custom rule-sets easily defined
ü Ruleset optimizer/profiler
o Configurations with multiple rule-sets
ü E.g. one with consistency checking to be used for internal data and another one
with „open-world“ semantics for LOD and other external datasets
o NEW: Proof plug-in provides inference explanation

Predefined Rule-Sets
Ruleset Description
Empty No reasoning
rdfs Standard RDFS: subClassOf, subPropertyOf, domain and range of properties
rdfs-plus RDFS plus symmetric, transitive and inverse properties
owl-horst (pD*) sameAs, equivalentClass, equivalentProperty, SymmetricProperty,
TransitiveProperty, inverseOf, FunctionalProperty, InverseFunctionalProperty.
Partial support for: intersectionOf, someValuesFrom, hasValue, allValuesFrom
owl-max See the spec http://graphdb.ontotext.com/documentation/standard/reasoning.html
owl-rl (DL-LiteR) AsymmetricProperty, IrreflexiveProperty, propertyChainAxiom,
AllDisjointProperties, hasKey, unionOf, complementOf, oneOf, differentFrom,
AllDisjointClasses and all the property cardinality primitives. Adds more complete
support for intersectionOf, someValuesFrom, hasValue, allValuesFrom
owl-ql Partial compliance. See the spec https://www.w3.org/TR/owl2-profiles

Optimized Rule-Sets
o These versions exclude some RDFS reasoning rules, which are not useful
for most of the applications, but add substantial reasoning overheads
o “Optimized” ruleset versions suppress this rule
Id: rdf1_rdfs4a_4b
x a y
-------------------------------
x <rdf:type> <rdfs:Resource>
a <rdf:type> <rdfs:Resource>
y <rdf:type> <rdfs:Resource>

Efficient Retraction of Inferred Facts
o Materialization causes troubles upon delete
ü It is not trivial to figure out which inferred statements are no longer supported
o Deletion without recomputing the inference closure is needed
ü Without it forward-chaining is not feasible for dynamic environments
o GraphDB retracts statements via a unique algorithm
ü Forward-chaining to find potentially affected inferences
ü Backward-chaining to test which inferences are still supported
ü No truth maintenance information overheads
ü Fast – the same order of magnitude as materialization upon insert

The Honey of owl:sameAs Equivalence
o owl:sameAs links the datasets in the Linked Open Data cloud
o owl:sameAs declares that two different URIs denote one and the same object
ü Aligns different identifiers of the same real-world entity used in different data sources
o For example, let’s say that we have three different URIs for Bulgaria and two for
Sofia (its capital)
dbpedia:Sofia owl:sameAs geonames:727011
geonames:727011 geo-ont:parentFeature geonames:732800
dbpedia:Bulgaria owl:sameAs geonames:732800
dbpedia:Bulgaria owl:sameAs opencyc-en:Bulgaria

The Sting of owl:sameAs Equivalence
o According to the standard semantics of owl:sameAs
ü It is a transitive and symmetric relationship
ü Statements, asserted using one of the equivalent URIs, should be inferred to appear with all equivalent
URIs placed in the same position
ü Thus the 4 statements in the example lead to 10 inferred statements :
geonames:727011 owl:sameAs dbpedia:Sofia
geonames:732800 owl:sameAs dbpedia:Bulgaria
geonames:732800 owl:sameAs opencyc-en:Bulgaria
opencyc-en:Bulgaria owl:sameAs dbpedia:Bulgaria
opencyc-en:Bulgaria owl:sameAs geonames:732800
dbpedia:Sofia geo-ont:parentFeature geonames:732800
dbpedia:Sofia geo-ont:parentFeature opencyc-en:Bulgaria
dbpedia:Sofia geo-ont:parentFeature dbpedia:Bulgaria
geonames:727011 geo-ont:parentFeature opencyc-en:Bulgaria
geonames:727011 geo-ont:parentFeature dbpedia:Bulgaria

The Honey and the Sting of owl:sameAs
E11 E22
E12 E21
E23
geonames:727011
dbpedia:Sofia
geonames:732800
dbpedia:Bulgaria
opencyc-en:Bulgaria
geo-ont:parentFeature

owl:sameAs Optimization
o GraphDB features an optimization of owl:sameAs
ü It can use a single master-node in its indices to represent a class of sameAs-equivalent URIs
o Avoids inflating the indices with multiple equivalent statements
ü Imagine a statement that has 5 sameAs-equivalents of its subject, 2 of its predicate and 3 of its object.
Such statement would have 30 replicas in the indices after forward-chaining if such an optimization is
not used
o Helps presenting compact query results
ü The owl:sameAs equivalence can result in multiplication of the bindings of the variables in the process
of query evaluation with both forward- and backward-chaining. This leads to expansion of the result-
set with rows that differ only by referring to different URIs, which are sameAs-equivalent
ü Optionally, query results can be expanded, as if there is no optimization

Questions?
Experience the technology with our demonstrators
FactForge: Knowledge graph of linked open data and news
about People and Organizations http://factforge.net
RANK: News popularity ranking for companies http://rank.ontotext.com
NOW: Semantic News Portal http://now.ontotext.com
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