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Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
Don’t like RDF Reification? Making Statements about Statements Using Singleton Property
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Don’t like RDF Reification? Making Statements about Statements Using Singleton Property

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Statements about RDF statements, or meta triples, provide additional information about individual triples, such as the source, the occuring time or place, or the certainty. Integrating such meta …

Statements about RDF statements, or meta triples, provide additional information about individual triples, such as the source, the occuring time or place, or the certainty. Integrating such meta triples into semantic knowledge bases would enable the querying and reasoning mechanisms to be aware of provenance, time, location, or certainty of triples. How- ever, an efficient RDF representation for such meta knowledge of triples remains challenging. The existing reification approach allows such meta knowledge of RDF triples to be expressed using RDF by two steps. The first step is representing the triple by a Statement instance which has subject, predicate, and object indicated separately in three different triples. The second step is creating assertions about that instance as if it is a statement. While reification is simple and intuitive, this approach does not have formal semantics and is not commonly used in practice as described in the RDF Primer.
In this paper, we propose a novel approach called Singleton Property for representing meta triples and provide a for- mal semantics for it. We explain how this singleton property approach fits well with the existing syntax and formal semantics of RDF, and the syntax of SPARQL query lan- guage. We also demonstrate the use of singleton property in the representation and querying of meta knowledge in two examples of Semantic Web knowledge bases: YAGO2 and BKR. This approach, which is also simple and intuitive, can be easily adopted for representing and querying statements about statements in other knowledge bases.

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    • 1. Don’t like RDF Reification? Making Statements about Statements Using Singleton Property Vinh Nguyen Kno.e.sis Wright State University Olivier Bodenreider National Library of Medicine National Institute of Health Amit Sheth Kno.e.sis Wright State University WWW 2014, Seoul
    • 2. Linked Open Data • > 70% Metadata • Relation Extraction from unstructured text (PubMed, Wiki) • Evidences • Judgement 2
    • 3. Motivation Scenario Starts Ends 1965-11-22 1977-06-29 1986-06-## 1992-10-## Facts: Meta Queries: Query type Sample query Provenance P1. Where is this fact from? P2. When was it created? P3. Who created this fact? Time T1. When did this fact occur? T2. What is the time span of this fact? T3. Which events happened in the same year? Location L1. What is the location associated with this fact? L2. Which events happened at the same place? Certainty C1. What is the author confidence of this fact? 3 Subject Predicate Object Bob Dylan marriedTo Sarah Lownds Bob Dylan marriedTo Carolyn Dennis
    • 4. Form of Triples: Standard RDF Reification Subject Predicate Object Starts Ends Bob Dylan marriedTo Sarah Lownds 1965-11-22 1977-06-29 Standard RDF Reification Pros: 1. Intuitive, easy to understand Cons: 1. Takes 3N triples (4N if including Statement typing) to represent a statement => Not scalable 2. No formal semantics defined => Semantics is unclear 3. Discouraged in LOD! Time-aware Facts: 4 Subject Predicate Object #stmt1 type Statement #stmt1 hasSubject BobDylan #stmt1 hasProperty marriedTo #stmt1 hasObject Sara Lownds Bob Dylan marriedTo Sarah Lownds #stmt1 starts 1965-11-22 #stmt1 ends 1977-06-29
    • 5. RDF Reification vs. Singleton Property Time-aware Facts: Subject Predicate Object Starts Ends Bob Dylan marriedTo Sarah Lownds 1965-11-22 1977-06-29 Standard RDF Reification Subject Predicate Object #stmt1 type Statement #stmt1 hasSubject BobDylan #stmt1 hasProperty marriedTo #stmt1 hasObject Sara Lownds Bob Dylan marriedTo Sarah Lownds #stmt1 starts 1965-11-22 #stmt1 ends 1977-06-29 Singleton Property Subject Predicate Object marriedTo#1 rdf:sp marriedTo BobDylan marriedTo#1 Sarah Lownds marriedTo#1 starts 1965-11-22 marriedTo#1 ends 1977-06-29 5
    • 6. Form of Triples: PaCE Subject Predicate Object Source DateExtracted Bob Dylan marriedTo Sarah Lownds wikipage:Bob_Dylan 2009-06-07 Pros: 1. Save ~50% number of triples compared to reification thanks to the repeated subject, predicate, and object. Cons: 1. Not intuitive, hard to understand 2. Limited expressiveness Provenance-aware Facts: 6 Provenance-aware Context Entity Subject Predicate Object BobDylan_wp rdf:type Bob Dylan SaraLownds_wp rdf:type Sara Lownds BobDylan_wp marriedTo SaraLownds_wp BobDylan_wp hasSource wiki:Bob_Dylan BobDylan_wp hasDateExt 2009-06-07 Satya S. Sahoo, Olivier Bodenreider, Pascal Hitzler, Amit Sheth, and Krishnaprasad Thirunarayan. 2010. Provenance context entity (PaCE): scalable provenance tracking for scientific RDF data. In Proceedings of the 22nd international conference on Scientific and statistical database management (SSDBM'10),
    • 7. Facts and Provenance: Subject Predicate Object Source DateExtracted Bob Dylan marriedTo Sarah Lownds wikipage:Bob_Dylan 2009-06-07 Provenance-aware Context Entity Subject Predicate Object BobDylan_wp rdf:type Bob Dylan SaraLownds_wp rdf:type Sara Lownds BobDylan_wp marriedTo SaraLownds_wp BobDylan_wp hasSource wiki:Bob_Dylan BobDylan_wp hasDateExt 2009-06-07 7 PaCE vs. Singleton Property Singleton Property Subject Predicate Object marriedTo#1 rdf:sp marriedTo BobDylan marriedTo#1 Sarah Lownds marriedTo#1 hasSource wp:Bob_Dylan marriedTo#1 hasDateExt 2009-06-07
    • 8. Form of Quadruples: Named Graph Subject Predicate Object Starts Ends Bob Dylan marriedTo Sarah Lownds 1965-11-22 1977-06-29 Named Graph Subject Predicate Object NG Bob Dylan marriedTo Sarah Lownds ng_1 ng_1 starts 1965-11-22 Prov_graph ng_2 ends 1977-06-29 Prov_graph Pros: 1. Intuitive --creating # named graphs for # sources 2. Attach metadata for a set of triples 3. SPARQL supported Cons : 1. Defined for provenance only 2. Ambiguous semantics while associating different types of metadata at triple level Time-aware Facts: 8 * Carroll, Jeremy J., et al. "Named graphs, provenance and trust." Proceedings of the 14th international conference on World Wide Web. ACM, 2005.
    • 9. Named Graph vs. Singleton Property Time-aware Facts: Subject Predicate Object Starts Ends Bob Dylan marriedTo Sarah Lownds 1965-11-22 1977-06-29 Named Graph Subject Predicate Object NG Bob Dylan marriedTo Sarah Lownds ng_1 ng_1 starts 1965-11-22 Prov_graph ng_2 ends 1977-06-29 Prov_graph Singleton Property Subject Predicate Object marriedTo#1 rdf:sp marriedTo Bob Dylan marriedTo#1 Sarah Lownds marriedTo#1 starts 1965-11-22 marriedTo#1 ends 1977-06-29 9
    • 10. Facts and Temporal Information: RDF+: Form of Quintuples: RDF+ Subject Predicate Object Meta Property Meta value Bob Dylan marriedTo Sarah Lownds starts 1965-11-22 Bob Dylan marriedTo Sarah Lownds ends 1977-06-29 Cons 1. The r:epresentation is not in the form of RDF. Statement identifiers are used internally. Require the mappings from RDF to RDF+ and vice versa. 2. The SPARQL query syntax and semantics need to be extended to support RDF+ * Dividino, Renata, et al. "Querying for provenance, trust, uncertainty and other meta knowledge in RDF." Web Semantics: Science, Services and Agents on the World Wide Web 7.3 (2009): 204-219. 10 Subject Predicate Object Starts Ends Bob Dylan marriedTo Sarah Lownds 1965-11-22 1977-06-29
    • 11. Overall Goal A mechanism to make statements about statements should meet these requirements: 1. Intuitive, easy to understand 2. Formal semantics defined 3. Scalable, e.g., to LOD 4. Compatible with existing standards 5. Multiple types of metadata 11
    • 12. Generic Property vs. Singleton Property Facts and Provenance: Subject Predicate Object Source MarriageDate Bob Dylan marriedTo Sarah Lownds wikipage:Bob_Dylan 1965-11-22 BarackObama marriedTo MichelleObama wikipage:Barack_Obama 1992-10-03 Generic Property: 1. marriedTo is an RDF property instanceOf 2. marriedTo => { (Bob Dylan, Sarah Dylan), (Barack Obama, Michelle Obama), … … } 3. Any assertion to marriedTo is applicable to all pairs of entities! Singleton Property: 1. marriedTo#1, marriedTo#2 are RDF property 2. Different property instances: marriedTo#1, marriedTo#2, … marriedTo#n 3. Any assertion to marriedTo#1/marriedTo#2/…/mar riedTo#n is applicable to only ONE pair <= KEY 12
    • 13. Model-Theoretic Semantics Original* Simple Interpretation I : • Given a vocabulary V, New simple Interpretation I : satisfies additional criteria as follows: • IPS: a subset of IR, called the set of singleton properties of I, • IS_EXT (ps): is a function assigning to each singleton property a pair of entities from IR. New RDF Interpretation I : satisfies additional criteria as follows: • xs ∈ IPs if ⟨xs, rdf:SingletonPropertyI⟩ ∈ IEXT (rdf:typeI) • IR: a non-empty set of resources, alternatively called domain or universe of discourse of I. • IP: the set of generic properties of I • IEXT: a function assigning to each property a set of pairs from IR where IEXT (p) is called the extension of property p • IEXT : IP → 2IR X IR • IS: a function, mapping URIs from V into the union set of IR and IP, • IL: a function from the typed literals from V into the set of resources IR, • LV: a subset of IR, called the set of literal values. IS_EXT : IPS→ IR X IR. • xs ∈ IPs if ⟨xs, xI⟩ ∈ IEXT (rdf:singletonPropertyOfI), and x∈IP, IS_EXT (xs) = <s1, s2> 13
    • 14. Model-Theoretic Semantics: Example IR = {α, β, γ, δ, θ, λ, σ, ϕ} IP = {δ, θ, λ, σ, ϕ} LV = {1965-11-22, 1977-06-29, 1986-06-##, 1992-10-##} IEXT = θ → {⟨α, β⟩} λ → {⟨α, γ⟩} σ → {⟨θ, 1965-11-22 ⟩, ⟨λ, 1986-06-## ⟩} φ → {⟨θ, 1977-06-29⟩, ⟨λ, 1992-10-## ⟩} rdf:sp → {⟨θ, δ⟩, ⟨λ, δ⟩} δ → {⟨α, β⟩, ⟨α, γ⟩} IPS = {θ, λ} IS_EXT= θ→⟨α,β⟩ λ → ⟨α,γ⟩ Example of vocabulary VEX: RDF Interpretation of VEX: Subject Predicate Object BobDylan isMarriedTo Sarah Lownds BobDylan isMarriedTo#1 SaraLownds isMarriedTo#1 rdf:sp isMarriedTo isMarriedTo#1 hasStart 1965-11-22 isMarriedTo#1 hasEnd 1977-06-29 BobDylan isMarriedTo CarolynDennis BobDylan isMarriedTo#2 CarolynDennis isMarriedTo#2 rdf:sp isMarriedTo isMarriedTo#2 hasStart 1986-06-## isMarriedTo#2 hasEnd 1992-10-## IS: BobDylan → α SaraLownds → β CarolynDennis → γ isMarriedTo → δ isMarriedTo#1 → θ isMarriedTo#2 → λ hasStart → σ hasEnd → φ 14
    • 15. Querying Meta Triples Using SPARQL Singleton Graph Pattern Triple Type Subject Predicate Object Instantiating singleton property predicate_i rdf:sp predicate Singleton triple subject predicate_i object Meta triple predicate_i meta-predicate_j meta-value_j Data Query: 1. Who married whom? 2. SPARQL query SELECT ?person1 ?person2 WHERE { ?person1 ?married_sp ?person2 . ?married_sp rdf:sp :marriedTo . } Meta Query: 1. Who married whom and when? 2. SPARQL query SELECT ?person1 ?person2 ?time WHERE { ?person1 ?married_sp ?person2 . ?married_sp rdf:sp :marriedTo . ?married_sp :happenedOn ?date . } 15
    • 16. Use Case: Temporal and Spatial YAGO2S 16 FactID in Yago2s FactID Subject Predicate Object #1 GratefulDead performed TheClosingOfWinterLand #2 #1 occursIn SanFrancisco #3 #1 occursOn 1978-12-31 Singleton Property Subject Predicate Object performed_12345 rdf:singletonPropertyOf performed GratefulDead performed_12345 TheClosingOfWinterLand performed_12345 occursIn SanFrancisco performed_12345 occursOn 1978-12-31
    • 17. Experiment: BKR with Provenance • Five data sets generated from the same seed BKR  Singleton Property (SP)  Reification (R)  PaCE C1 (C1)  PaCE C2 (C2)  PaCE C3 (C3) All datasets are available at http://wiki.knoesis.org/index.php/Singleton_Property 17
    • 18. Experiment Results (A) random-value queries vs. fixed-value queries in msec. (B) query length and execution time in msec. 18
    • 19. Conclusion Does the singleton property approach meet these 3. Scalable, e.g., to LOD requirements? 1. Intuitive, easy to understand 2. Formal semantics defined 4. Compatible with existing standards 5. Multiple types of metadata 19
    • 20. Further information, please visit http://wiki.knoesis.org/index.php/Singleton_Property 20

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