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ONTOLOGIES FORKNOWLEDGE GRAPHS?Markus Krötzsch†
reporting on joint work with Stefan Bischoff, Fredo Erxleben, Michael Günther,Maximilian Marx†, Julian Mendez, Ana Ozaki†, Axel Polleres, Sebastian Rudolph,Veronika Thost†, and Denny Vrandecic
† Knowledge-Based Systems
TU Dresden
DL Workshop 2017
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The Semantic Web (2007)
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2012: The Knowledge Graph
“. . . one of the key breakthroughsbehind the future of search”
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More Knowledge Graphs
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What is a Knowledge Graph?
More than “a database used in an AI application”?
Charateristics of today’s KGs:
Normalised: Data decomposed into small units (“edges”)
Connected: Knowledge represented by relationships be-tween these units
Annotated: Enriched with contextual information to recordmeta-data and auxiliary details
• Typical for many KG applications
• Often comes with a promise of declarative processing
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What is a Knowledge Graph?
More than “a database used in an AI application”?
Charateristics of today’s KGs:
Normalised: Data decomposed into small units (“edges”)
Connected: Knowledge represented by relationships be-tween these units
Annotated: Enriched with contextual information to recordmeta-data and auxiliary details
• Typical for many KG applications
• Often comes with a promise of declarative processing
Markus Krötzsch Ontologies for Knowledge Graphs? slide 5 of 33
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What is a Knowledge Graph?
More than “a database used in an AI application”?
Charateristics of today’s KGs:
Normalised: Data decomposed into small units (“edges”)
Connected: Knowledge represented by relationships be-tween these units
Annotated: Enriched with contextual information to recordmeta-data and auxiliary details
• Typical for many KG applications
• Often comes with a promise of declarative processing
Markus Krötzsch Ontologies for Knowledge Graphs? slide 5 of 33
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SummaryKnowledge graphs• introduce graph-based data models• requiring declarative analytics• that make non-local connections
reasoning on graphs
Conclusion
Symbolic KR is the key technologyin modern data management
especially in AI applications
Not really
happening
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SummaryKnowledge graphs• introduce graph-based data models• requiring declarative analytics• that make non-local connections
reasoning on graphs
Conclusion
Symbolic KR is the key technologyin modern data management
especially in AI applications
Not really
happening
Markus Krötzsch Ontologies for Knowledge Graphs? slide 7 of 33
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SummaryKnowledge graphs• introduce graph-based data models• requiring declarative analytics• that make non-local connections
reasoning on graphs
Conclusion
Symbolic KR is the key technologyin modern data management
especially in AI applications
Not really
happening
Markus Krötzsch Ontologies for Knowledge Graphs? slide 7 of 33
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SummaryKnowledge graphs• introduce graph-based data models• requiring declarative analytics• that make non-local connections
reasoning on graphs
Conclusion
Symbolic KR is the key technologyin modern data management
especially in AI applications
Not really
happening
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A Free Knowledge Graph
Wikidata• Wikipedia’s knowledge graph
• Free, community-built database
• Large graph(July 2017: >165M statements on >29M entities)
• Large, active community(July 2017: >175,000 logged-in human editors)
• Many applications
Freely available, relevant, and active knowledge graph
[Vrandecic & K; Comm. ACM 2014]Markus Krötzsch Ontologies for Knowledge Graphs? slide 9 of 33
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I’m in ur phone . . .
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Statements in Wikidata
Wikidata’s basic information units• Built from Wikidata items (“CERN”, “Vint Cerf”),
Wikidata properties (“award received”, “end time”), anddata values (“2013”)
• Based on directed edges(“Tim Berners-Lee −employer→ CERN”)
• Annotated with property-value pairs (“end time: 1994”)– same property can have multiple annotation values
(“together with: Robert Kahn, Vint Cerf, . . . ”)– same properties/values used in directed edges and annotations
• Items and properties can be subjects/values instatements
• Multi-graph
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Fig.: Taylor standing in multiple relations; from https://tools.wmflabs.org/sqid/#/view?id=Q34851
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Wikidata Statements in Terms of Graphs
“Property Graph”: Taylor Burtonspouse
start time: 1975-10-10end time: 1976-07-29
“RDF”: Taylor Burtonspousein spouseout
1975-10-10 1976-07-29
start time end time
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Wikidata Statements in Terms of Graphs
“Property Graph”: Taylor Burtonspouse
start time: 1975-10-10end time: 1976-07-29
“RDF”: Taylor Burtonspousein spouseout
1975-10-10 1976-07-29
start time end time
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Wikidata Statements in Terms of Graphs
“Property Graph”: Taylor Burtonspouse
start time: 1975-10-10end time: 1976-07-29
“RDF”: Taylor Burtonspousein spouseout
1975-10-10 1976-07-29
start time end time
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Ontological Modelling in Wikidata
Classification• 25,298,346 instance of statements (for 84.9% of entities)
• 2,056,181 subclass of statements (for 4.5% of entities)
Property characteristics/constraints• symmetric property (17 instances)
• transitive property (8 instances)
• 12,595 statements specifying other constraints(domain, range, disjointness, . . . )
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Ontological Modelling in Wikidata
Classification• 25,298,346 instance of statements (for 84.9% of entities)
• 2,056,181 subclass of statements (for 4.5% of entities)
Property characteristics/constraints• symmetric property (17 instances)
• transitive property (8 instances)
• 12,595 statements specifying other constraints(domain, range, disjointness, . . . )
Markus Krötzsch Ontologies for Knowledge Graphs? slide 15 of 33
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Queries on WikidataSPARQL query service: https://query.wikidata.org• officially maintained, live data• based on RDF mapping [Erxleben et al., ISWC 2014]• heavily used: 60M–135M queries per month
Initial analysis of the non-public logs:• ≤1% queries from human traffic (400–500K per month)• ≥99% service calls from tools and robots• Irregular distributions and biases – hard to analyse
Property paths used for transitivity reasoning
• used in about 50% of human subclass-of queries (20K)
• over 500K queries with subclass-of paths overall
(statistics for May 2017)
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Queries on WikidataSPARQL query service: https://query.wikidata.org• officially maintained, live data• based on RDF mapping [Erxleben et al., ISWC 2014]• heavily used: 60M–135M queries per month
Initial analysis of the non-public logs:• ≤1% queries from human traffic (400–500K per month)• ≥99% service calls from tools and robots• Irregular distributions and biases – hard to analyse
Property paths used for transitivity reasoning
• used in about 50% of human subclass-of queries (20K)
• over 500K queries with subclass-of paths overall
(statistics for May 2017)
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Queries on WikidataSPARQL query service: https://query.wikidata.org• officially maintained, live data• based on RDF mapping [Erxleben et al., ISWC 2014]• heavily used: 60M–135M queries per month
Initial analysis of the non-public logs:• ≤1% queries from human traffic (400–500K per month)• ≥99% service calls from tools and robots• Irregular distributions and biases – hard to analyse
Property paths used for transitivity reasoning
• used in about 50% of human subclass-of queries (20K)
• over 500K queries with subclass-of paths overall
(statistics for May 2017)
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OBQA via SPARQL
SPARQL is actually powerful enough for OWL QL reasoning[Bischoff et al., ISWC 2014]
. . . but the queries then are getting lengthy . . .
Fig.: A query that checks if x is equivalent to ⊥ (abbreviated)
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Beyond OWL QL
SPARQL cannot support arbitrary OWL reasoning:• computing power limited by data complexity
• SPARQL can only perform reasoning in NL
Queries with higher data complexities?• Datalog: PTime-complete data complexity
• Datalog can be used for “query-based” EL reasoning[K, IJCAI 2011]
Query-Based Reasoning:
• ontologicl information as part of data
• logic for meta-reasoning on top
• same data can be viewed under different semantics
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Beyond OWL QL
SPARQL cannot support arbitrary OWL reasoning:• computing power limited by data complexity
• SPARQL can only perform reasoning in NL
Queries with higher data complexities?• Datalog: PTime-complete data complexity
• Datalog can be used for “query-based” EL reasoning[K, IJCAI 2011]
Query-Based Reasoning:
• ontologicl information as part of data
• logic for meta-reasoning on top
• same data can be viewed under different semantics
Markus Krötzsch Ontologies for Knowledge Graphs? slide 18 of 33
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Beyond OWL QL
SPARQL cannot support arbitrary OWL reasoning:• computing power limited by data complexity
• SPARQL can only perform reasoning in NL
Queries with higher data complexities?• Datalog: PTime-complete data complexity
• Datalog can be used for “query-based” EL reasoning[K, IJCAI 2011]
Query-Based Reasoning:
• ontologicl information as part of data
• logic for meta-reasoning on top
• same data can be viewed under different semantics
Markus Krötzsch Ontologies for Knowledge Graphs? slide 18 of 33
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Ontologies for Wikidata?
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A Simple Example
Wikidata declares the spouse property to be symmetric:
Taylor Burtonspousein spouseout
1975-10-10 1976-07-29
start time end time ⇒
Taylor Burtonspouseinspouseout
1975-10-10 1976-07-29
start time end time
ABox:spousein(taylor, s) spouseout(s, burton)
start(s, 1975-10-10) end(s, 1976-07-29)
An axiom of symmetry:
∀x, y, z1, z2, v.spousein(x, v) ∧ spouseout(v, y) ∧ start(v, z1) ∧ end(v, z2)
→ ∃w.spousein(y, w) ∧ spouseout(x, y) ∧ start(w, z1) ∧ end(w, z2)
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A Simple Example
Wikidata declares the spouse property to be symmetric:
Taylor Burtonspousein spouseout
1975-10-10 1976-07-29
start time end time ⇒
Taylor Burtonspouseinspouseout
1975-10-10 1976-07-29
start time end time
ABox:spousein(taylor, s) spouseout(s, burton)
start(s, 1975-10-10) end(s, 1976-07-29)
An axiom of symmetry:
∀x, y, z1, z2, v.spousein(x, v) ∧ spouseout(v, y) ∧ start(v, z1) ∧ end(v, z2)
→ ∃w.spousein(y, w) ∧ spouseout(x, y) ∧ start(w, z1) ∧ end(w, z2)
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A Simple Example
Wikidata declares the spouse property to be symmetric:
Taylor Burtonspousein spouseout
1975-10-10 1976-07-29
start time end time ⇒
Taylor Burtonspouseinspouseout
1975-10-10 1976-07-29
start time end time
ABox:spousein(taylor, s) spouseout(s, burton)
start(s, 1975-10-10) end(s, 1976-07-29)
An axiom of symmetry:
∀x, y, z1, z2, v.spousein(x, v) ∧ spouseout(v, y) ∧ start(v, z1) ∧ end(v, z2)
→ ∃w.spousein(y, w) ∧ spouseout(x, y) ∧ start(w, z1) ∧ end(w, z2)
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Existential rules
spousein(x, v) ∧ spouseout(v, y) ∧ start(v, z1) ∧ end(v, z2)
→ ∃w.spousein(y, w) ∧ spouseout(x, y) ∧ start(w, z1) ∧ end(w, z2)
This axiom is an existential rule
• it is not expressible in Datalog
• it is not expressible in DL
• it is not linear
• it is not (frontier) guarded
• it is not acyclic (w.r.t. predicate dependencies)
but it might be weakly acyclic/frontier guarded(depends on other axioms)
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Existential rules
spousein(x, v) ∧ spouseout(v, y) ∧ start(v, z1) ∧ end(v, z2)
→ ∃w.spousein(y, w) ∧ spouseout(x, y) ∧ start(w, z1) ∧ end(w, z2)
This axiom is an existential rule
• it is not expressible in Datalog
• it is not expressible in DL
• it is not linear
• it is not (frontier) guarded
• it is not acyclic (w.r.t. predicate dependencies)
but it might be weakly acyclic/frontier guarded(depends on other axioms)
Markus Krötzsch Ontologies for Knowledge Graphs? slide 21 of 33
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Existential rules
spousein(x, v) ∧ spouseout(v, y) ∧ start(v, z1) ∧ end(v, z2)
→ ∃w.spousein(y, w) ∧ spouseout(x, y) ∧ start(w, z1) ∧ end(w, z2)
This axiom is an existential rule
• it is not expressible in Datalog
• it is not expressible in DL
• it is not linear
• it is not (frontier) guarded
• it is not acyclic (w.r.t. predicate dependencies)
but it might be weakly acyclic/frontier guarded(depends on other axioms)
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Existential rules
spousein(x, v) ∧ spouseout(v, y) ∧ start(v, z1) ∧ end(v, z2)
→ ∃w.spousein(y, w) ∧ spouseout(x, y) ∧ start(w, z1) ∧ end(w, z2)
This axiom is an existential rule
• it is not expressible in Datalog
• it is not expressible in DL
• it is not linear
• it is not (frontier) guarded
• it is not acyclic (w.r.t. predicate dependencies)
but it might be weakly acyclic/frontier guarded(depends on other axioms)
Markus Krötzsch Ontologies for Knowledge Graphs? slide 21 of 33
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Existential rules
spousein(x, v) ∧ spouseout(v, y) ∧ start(v, z1) ∧ end(v, z2)
→ ∃w.spousein(y, w) ∧ spouseout(x, y) ∧ start(w, z1) ∧ end(w, z2)
This axiom is an existential rule
• it is not expressible in Datalog
• it is not expressible in DL
• it is not linear
• it is not (frontier) guarded
• it is not acyclic (w.r.t. predicate dependencies)
but it might be weakly acyclic/frontier guarded(depends on other axioms)
Markus Krötzsch Ontologies for Knowledge Graphs? slide 21 of 33
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Existential rules
spousein(x, v) ∧ spouseout(v, y) ∧ start(v, z1) ∧ end(v, z2)
→ ∃w.spousein(y, w) ∧ spouseout(x, y) ∧ start(w, z1) ∧ end(w, z2)
This axiom is an existential rule
• it is not expressible in Datalog
• it is not expressible in DL
• it is not linear
• it is not (frontier) guarded
• it is not acyclic (w.r.t. predicate dependencies)
but it might be weakly acyclic/frontier guarded(depends on other axioms)
Markus Krötzsch Ontologies for Knowledge Graphs? slide 21 of 33
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Existential rules
spousein(x, v) ∧ spouseout(v, y) ∧ start(v, z1) ∧ end(v, z2)
→ ∃w.spousein(y, w) ∧ spouseout(x, y) ∧ start(w, z1) ∧ end(w, z2)
This axiom is an existential rule
• it is not expressible in Datalog
• it is not expressible in DL
• it is not linear
• it is not (frontier) guarded
• it is not acyclic (w.r.t. predicate dependencies)
but it might be weakly acyclic/frontier guarded(depends on other axioms)
Markus Krötzsch Ontologies for Knowledge Graphs? slide 21 of 33
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Breaking the Rules
Observation: Normalisation may destroy syntactic properties[K & Thost; ISWC 2016]
• Acyclicity properties are mostly ften preserved
• Linearity and guardedness are lost (syntactically)
• Can sometimes recover by denormalising rules
Existential rules are a first step, but:• Normalised rules are hard to read and write
• Not expressive enough, e.g., cannot copy arbitraryannotation sets
• Loss of structure by flattening annotations, e.g., cannothave closed-world negation on annotation sets
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Breaking the Rules
Observation: Normalisation may destroy syntactic properties[K & Thost; ISWC 2016]
• Acyclicity properties are mostly ften preserved
• Linearity and guardedness are lost (syntactically)
• Can sometimes recover by denormalising rules
Existential rules are a first step, but:• Normalised rules are hard to read and write
• Not expressive enough, e.g., cannot copy arbitraryannotation sets
• Loss of structure by flattening annotations, e.g., cannothave closed-world negation on annotation sets
Markus Krötzsch Ontologies for Knowledge Graphs? slide 22 of 33
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Annotated Logics
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MARSIdea: Change from relational structures to “relationalstructures with annotated tuples” [Marx, K, Thost, IJCAI 2017]
Multi-Attributed Relational Structures (MARS):
• standard interpretation domain ∆I
• finite annotation sets S ∈ Pfin(∆I × ∆I)• n-ary relations r interpreted as
rI ⊆ (∆I)n × Pfin(∆I × ∆I)
Multi-Attributed Predicate Logic (MAPL)• Ground fact:
spouse(taylor, burton)@{start : 1975, end : 1976}• Object and set variables:∀x, y, Z.spouse(x, y)@Z → spouse(y, x)@Z
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MARSIdea: Change from relational structures to “relationalstructures with annotated tuples” [Marx, K, Thost, IJCAI 2017]
Multi-Attributed Relational Structures (MARS):
• standard interpretation domain ∆I
• finite annotation sets S ∈ Pfin(∆I × ∆I)• n-ary relations r interpreted as
rI ⊆ (∆I)n × Pfin(∆I × ∆I)
Multi-Attributed Predicate Logic (MAPL)• Ground fact:
spouse(taylor, burton)@{start : 1975, end : 1976}• Object and set variables:∀x, y, Z.spouse(x, y)@Z → spouse(y, x)@Z
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MARSIdea: Change from relational structures to “relationalstructures with annotated tuples” [Marx, K, Thost, IJCAI 2017]
Multi-Attributed Relational Structures (MARS):
• standard interpretation domain ∆I
• finite annotation sets S ∈ Pfin(∆I × ∆I)• n-ary relations r interpreted as
rI ⊆ (∆I)n × Pfin(∆I × ∆I)
Multi-Attributed Predicate Logic (MAPL)• Ground fact:
spouse(taylor, burton)@{start : 1975, end : 1976}• Object and set variables:∀x, y, Z.spouse(x, y)@Z → spouse(y, x)@Z
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Expressivity of MAPL
Theorem: MAPL is equivalent to Weak Second-Order Logic,hence reasoning is not semi-decidable.
Research goal: Identify practical fragments
A decidable fragment:
MAPL Rules (MARPL)
• Horn rules, with all variables universally quantified
• all set variables bound in body atoms
Example: ∀x, y, Z.spouse(x, y)@Z → spouse(y, x)@Z
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Expressivity of MAPL
Theorem: MAPL is equivalent to Weak Second-Order Logic,hence reasoning is not semi-decidable.
Research goal: Identify practical fragments
A decidable fragment:
MAPL Rules (MARPL)
• Horn rules, with all variables universally quantified
• all set variables bound in body atoms
Example: ∀x, y, Z.spouse(x, y)@Z → spouse(y, x)@Z
Markus Krötzsch Ontologies for Knowledge Graphs? slide 25 of 33
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Expressivity of MAPL
Theorem: MAPL is equivalent to Weak Second-Order Logic,hence reasoning is not semi-decidable.
Research goal: Identify practical fragments
A decidable fragment:
MAPL Rules (MARPL)
• Horn rules, with all variables universally quantified
• all set variables bound in body atoms
Example: ∀x, y, Z.spouse(x, y)@Z → spouse(y, x)@Z
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MARPL: Additional FeaturesWe really need more expressive features
Conditions on annotation sets Z• [start : 1975, end : ∗](Z):
“Z has given start and some end, but nothing more”• bstart : 1975c(Z): “Z has given start, and possibly more”
{ supported in MARPL rule bodies
Inferring new annotation sets• Support declarative definition of deterministic functions
that derive new sets• Example:
employer(x, cern)@Z ∧ bpos : fellowc(Z)
→ cernFellow(x)@[start : Z.start, end : Z.end]
{ supported in MARPL rule heads
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MARPL: Additional FeaturesWe really need more expressive features
Conditions on annotation sets Z• [start : 1975, end : ∗](Z):
“Z has given start and some end, but nothing more”• bstart : 1975c(Z): “Z has given start, and possibly more”
{ supported in MARPL rule bodies
Inferring new annotation sets• Support declarative definition of deterministic functions
that derive new sets• Example:
employer(x, cern)@Z ∧ bpos : fellowc(Z)
→ cernFellow(x)@[start : Z.start, end : Z.end]
{ supported in MARPL rule heads
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MARPL: Additional FeaturesWe really need more expressive features
Conditions on annotation sets Z• [start : 1975, end : ∗](Z):
“Z has given start and some end, but nothing more”• bstart : 1975c(Z): “Z has given start, and possibly more”
{ supported in MARPL rule bodies
Inferring new annotation sets• Support declarative definition of deterministic functions
that derive new sets• Example:
employer(x, cern)@Z ∧ bpos : fellowc(Z)
→ cernFellow(x)@[start : Z.start, end : Z.end]
{ supported in MARPL rule headsMarkus Krötzsch Ontologies for Knowledge Graphs? slide 26 of 33
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MARPL Complexity
Theorem: Conjunctive query answering ove MARPL ontolo-gies is ExpTime-complete, both for combined complexityand for data complexity.
Problem?• Not really: hardness requires annotation sets of
unbounded size (not a practical concern)
• Actually, it’s a feature: high data complexity enablespowerful meta-reasoning in query-based approaches
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MARPL Complexity
Theorem: Conjunctive query answering ove MARPL ontolo-gies is ExpTime-complete, both for combined complexityand for data complexity.
Problem?
• Not really: hardness requires annotation sets ofunbounded size (not a practical concern)
• Actually, it’s a feature: high data complexity enablespowerful meta-reasoning in query-based approaches
Markus Krötzsch Ontologies for Knowledge Graphs? slide 27 of 33
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MARPL Complexity
Theorem: Conjunctive query answering ove MARPL ontolo-gies is ExpTime-complete, both for combined complexityand for data complexity.
Problem?• Not really: hardness requires annotation sets of
unbounded size (not a practical concern)
• Actually, it’s a feature: high data complexity enablespowerful meta-reasoning in query-based approaches
Markus Krötzsch Ontologies for Knowledge Graphs? slide 27 of 33
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Attributed Description Logics
MARPL is a simple rule language(“Datalog for annotated logic”)
How about DLs?
Attributed Description Logicssee DL talk later today [K et al., ISWC 2017]
How about attributed existential rules?
{ future work
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Attributed Description Logics
MARPL is a simple rule language(“Datalog for annotated logic”)
How about DLs?
Attributed Description Logicssee DL talk later today [K et al., ISWC 2017]
How about attributed existential rules?
{ future work
Markus Krötzsch Ontologies for Knowledge Graphs? slide 28 of 33
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Attributed Description Logics
MARPL is a simple rule language(“Datalog for annotated logic”)
How about DLs?
Attributed Description Logicssee DL talk later today [K et al., ISWC 2017]
How about attributed existential rules?
{ future work
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The Future of KR
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Problem solved?
So all we need to marry KG and KR are attributed logics?
Surely not – many other areas need more work!
We also need to change some of our premises:
Traditional KR View vs. Knowledge Graphs View
schema first data first
unique purpose multi-purpose
fixed application emerging applications
closed expert team open community/many teams
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Problem solved?
So all we need to marry KG and KR are attributed logics?
Surely not – many other areas need more work!
We also need to change some of our premises:
Traditional KR View vs. Knowledge Graphs View
schema first data first
unique purpose multi-purpose
fixed application emerging applications
closed expert team open community/many teams
Markus Krötzsch Ontologies for Knowledge Graphs? slide 30 of 33
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Problem solved?
So all we need to marry KG and KR are attributed logics?
Surely not – many other areas need more work!
We also need to change some of our premises:
Traditional KR View vs. Knowledge Graphs View
schema first data first
unique purpose multi-purpose
fixed application emerging applications
closed expert team open community/many teams
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Still Looking for the “Unifying Logic”?
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Conclusions
Summary• Knowledge Graphs are enriched graphs
• Wikidata: large ABox / “ontology” / path queries
• Query-based reasoning: plug’n’play semantics for data
• Existential rules & DLs: struggling with annotations
• Attributed logics: MAPL & MARPL (& attributed DLs)
What next?
View KR as a declarative computing paradigm & start facingthe competition in this space!
Revisit “Computing in Logic” (but don’t go back to Prolog!)
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References
• [Bischoff et al., ISWC 2014] Stefan Bischoff, Markus Krötzsch, Axel Polleres,Sebastian Rudolph: Schema-Agnostic Query Rewriting for SPARQL 1.1.ISWC 2014.
• [Erxleben et al., ISWC 2014] Fredo Erxleben, Michael Günther, MarkusKrötzsch, Julian Mendez, Denny Vrandecic: Introducing Wikidata to theLinked Data Web. ISWC 2014.
• [K, IJCAI 2011] Markus Krötzsch: Efficient Rule-Based Inferencing forOWL EL. IJCAI 2011.
• [K et al., ISWC 2017] Markus Krötzsch, Maximilian Marx, Ana Ozaki,Veronika Thost: Attributed Description Logics: Ontologies forKnowledge Graphs. ISWC 2017 (to appear).
• [K & Thost; ISWC 2016] Markus Krötzsch, Veronika Thost: Ontologies forKnowledge Graphs: Breaking the Rules. ISWC 2016.
• [Marx, K, Thost, IJCAI 2017] Maximilian Marx, Markus Krötzsch, VeronikaThost: Logic on MARS: Ontologies for Generalised Property Graphs.IJCAI 2017 (to appear).
• [Vrandecic & K; Comm. ACM 2014] Denny Vrandecic, Markus Krötzsch:Wikidata: A Free Collaborative Knowledgebase. Comm. ACM, 57:10,2014.
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