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Property Alignment on Linked Open Data

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Artificial Intelligence Institute at UofSC

The document presents a schema-independent approach to property alignment in linked data, emphasizing the importance of aligning equivalent properties across different datasets for effective data integration. It evaluates the proposed method against various existing techniques using statistical equivalence derived from owl:equivalentproperty and shows the method's effectiveness through experiments with datasets like DBpedia and Freebase. The findings indicate that the extension-based algorithm consistently outperformed traditional approaches in aligning properties, demonstrating significant improvements in precision and recall.

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A Statistical and Schema Independent Approach to Identify Equivalent Properties on Linked Data †Kno.e.sis Center Wright State University Dayton OH, USA ‡IBM T J Watson Research Center Yorktown Heights New York NY, USA Kalpa Gunaratna†, Krishnaprasad Thirunarayan†, Prateek Jain‡, Amit Sheth†, and Sanjaya Wijeratne† {kalpa,tkprasad,amit,sanjaya}@knoesis.org, jainpr@us.ibm.com iSemantics 2013, Graz, Austria
09/06/2013 2 Motivation Why we need property alignment and it is so important? iSemantics 2013
09/06/2013 3 Many datasets. We can query! iSemantics 2013
09/06/2013 3iSemantics 2013
09/06/2013 3 Same information in different names. Therefore, data integration for better presentation is required. iSemantics 2013
Background Statistical Equivalence of properties Evaluation Discussion, interesting facts, and future directions Conclusion 09/06/2013 4 Roadmap iSemantics 2013
Existing techniques for property alignment fall into three categories. I. Syntactic/dictionary based – Uses string manipulation techniques, external dictionaries and lexical databases like WordNet. II. Schema dependent – Uses schema information such as, domain and range, definitions. III. Schema independent – Uses instance level information for the alignment.  Our approach falls under schema independent. 09/06/2013 5 Background iSemantics 2013
Properties capture meaning of triples and hence they are complex in nature.    09/06/2013 6iSemantics 2013
Properties capture meaning of triples and hence they are complex in nature. Syntactic or dictionary based approaches analyze property names for equivalence. But in LOD, name heterogeneities exist.   09/06/2013 6iSemantics 2013
Properties capture meaning of triples and hence they are complex in nature. Syntactic or dictionary based approaches analyze property names for equivalence. But in LOD, name heterogeneities exist. Therefore, syntactic or dictionary based approaches have limited coverage in property alignment.  09/06/2013 6iSemantics 2013
Properties capture meaning of triples and hence they are complex in nature. Syntactic or dictionary based approaches analyze property names for equivalence. But in LOD, name heterogeneities exist. Therefore, syntactic or dictionary based approaches have limited coverage in property alignment. Schema dependent approaches including processing domain and range, class level tags do not capture semantics of properties well. 09/06/2013 6iSemantics 2013
Background Statistical Equivalence of properties Evaluation Discussion, interesting facts, and future directions Conclusion 09/06/2013 7 Roadmap iSemantics 2013
 Statistical Equivalence is based on analyzing owl:equivalentProperty.  owl:equivalentProperty - properties that have same property extensions.          09/06/2013 8 Statistical Equivalence of properties iSemantics 2013
 Statistical Equivalence is based on analyzing owl:equivalentProperty.  owl:equivalentProperty - properties that have same property extensions. Example 1: Property P is defined by the triples, { a P b, c P d, e P f } Property Q is defined by the triples, { a Q b, c Q d, e Q f } P and Q are owl:equivalentProperty, because they have the same extension, { {a,b}, {c,d}, {e,f} }     09/06/2013 8 Statistical Equivalence of properties iSemantics 2013
 Statistical Equivalence is based on analyzing owl:equivalentProperty.  owl:equivalentProperty - properties that have same property extensions. Example 1: Property P is defined by the triples, { a P b, c P d, e P f } Property Q is defined by the triples, { a Q b, c Q d, e Q f } P and Q are owl:equivalentProperty, because they have the same extension, { {a,b}, {c,d}, {e,f} } Example 2: Property P is defined by the triples, { a P b, c P d, e P f } Property Q is defined by the triples, { a Q b, c Q d, e Q h } Then, P and Q are not owl:equivalentProperty, because their extensions are not the same. But they provide statistical evidence in support of equivalence. 09/06/2013 8 Statistical Equivalence of properties iSemantics 2013
Intuition  Higher rate of subject-object matches in extensions leads to equivalent properties. In practice, it is hard to have exact same extensions for matching properties. Because, – Datasets are incomplete. – Same instance may be modelled differently in different datasets.  Therefore, we analyze the property extensions to identify equivalent properties between datasets.  We define the following notions. Let the statement below be true for all the definitions. S1P1O1 and S2P2O2 be two triples in Dataset D1 and D2 respectively. 09/06/2013 iSemantics 2013 9
 09/06/2013 iSemantics 2013 10
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09/06/2013 iSemantics 2013 12 Dataset 2Dataset 1 Candidate Matching Algorithm Process
09/06/2013 iSemantics 2013 12 I1 Dataset 2Dataset 1 I1=d1:Willis_Lamb Candidate Matching Algorithm Process
09/06/2013 iSemantics 2013 12 I1 I2 owl:sameAs Dataset 2Dataset 1 I1=d1:Willis_Lamb I2 =d2:willis_lamb Step 1 Candidate Matching Algorithm Process
09/06/2013 iSemantics 2013 12 I1 I2 I2 owl:sameAs P1=d1:doctoralStudent P2=d2:education. academic.advisees Dataset 2Dataset 1 d2:theodore_harold_maiman I1=d1:Willis_Lamb I2 =d2:willis_lamb I1 I1 d1:Theodore_Maiman triple 1 triple 2 triple 3 triple 4 triple 5 Step 1 Step2 Step2 Candidate Matching Algorithm Process
09/06/2013 iSemantics 2013 12 I1 I2 I2 matching resources owl:sameAs P1=d1:doctoralStudent P2=d2:education. academic.advisees Dataset 2Dataset 1 property P1 and property P2 are a candidate match d2:theodore_harold_maiman I1=d1:Willis_Lamb I2 =d2:willis_lamb I1 I1 d1:Theodore_Maiman triple 1 triple 2 triple 3 triple 4 triple 5 Step 1 Step2 Step2 Step 3 Candidate Matching Algorithm Process
09/06/2013 iSemantics 2013 13 Complexity: If the average number of properties for an entity is x and for each property, average number of objects is j. For n subjects, it requires n*j2*x2+2n comparisons. Since n > j, n > x, and x and j are independent of n, O(n).
Example: 09/06/2013 iSemantics 2013 14
 09/06/2013 iSemantics 2013 28
Background Statistical Equivalence of properties Evaluation Discussion, interesting facts, and future directions Conclusion 09/06/2013 16 Roadmap iSemantics 2013
Objectives of the evaluation – Show the effectiveness of the approach in linked datasets – Compare with existing aligning techniques   09/06/2013 iSemantics 2013 17 Evaluation
Objectives of the evaluation – Show the effectiveness of the approach in linked datasets – Compare with existing aligning techniques We selected 5000 instance samples from DBpedia, Freebase, LinkedMDB, DBLP L3S , and DBLP RKB Explorer datasets.  09/06/2013 iSemantics 2013 17 Evaluation
Objectives of the evaluation – Show the effectiveness of the approach in linked datasets – Compare with existing aligning techniques We selected 5000 instance samples from DBpedia, Freebase, LinkedMDB, DBLP L3S , and DBLP RKB Explorer datasets. These datasets have, – Complete data for instances in different viewpoints – Many inter-links – Complex properties 09/06/2013 iSemantics 2013 17 Evaluation
Experiment details – α = 0.5 for all experiments (works for LOD) except DBpedia and Freebase movie alignment where it was 0.7. 09/06/2013 iSemantics 2013 18
Experiment details – α = 0.5 for all experiments (works for LOD) except DBpedia and Freebase movie alignment where it was 0.7. – k was set as 14, 6, 2, 2, and 2 respectively for Person, Film and Software between DBpedia and Freebase, Film between LinkedMDB and DBpedia, and article between DBLP datasets. 09/06/2013 iSemantics 2013 18
Experiment details – α = 0.5 for all experiments (works for LOD) except DBpedia and Freebase movie alignment where it was 0.7. – k was set as 14, 6, 2, 2, and 2 respectively for Person, Film and Software between DBpedia and Freebase, Film between LinkedMDB and DBpedia, and article between DBLP datasets. – k can be estimated using the data as follows, – Set α = 0.5 and k = 2 (lowest positive values). – Get exact matching property (property names) pairs not identified by the algorithm and their μ – Get the average of those μ values 09/06/2013 iSemantics 2013 18
Experiment details – α = 0.5 for all experiments (works for LOD) except DBpedia and Freebase movie alignment where it was 0.7. – k was set as 14, 6, 2, 2, and 2 respectively for Person, Film and Software between DBpedia and Freebase, Film between LinkedMDB and DBpedia, and article between DBLP datasets. – k can be estimated using the data as follows, – Set α = 0.5 and k = 2 (lowest positive values). – Get exact matching property (property names) pairs not identified by the algorithm and their μ – Get the average of those μ values – 0.92 for string similarity algorithms. 09/06/2013 iSemantics 2013 18
Experiment details – α = 0.5 for all experiments (works for LOD) except DBpedia and Freebase movie alignment where it was 0.7. – k was set as 14, 6, 2, 2, and 2 respectively for Person, Film and Software between DBpedia and Freebase, Film between LinkedMDB and DBpedia, and article between DBLP datasets. – k can be estimated using the data as follows, – Set α = 0.5 and k = 2 (lowest positive values). – Get exact matching property (property names) pairs not identified by the algorithm and their μ – Get the average of those μ values – 0.92 for string similarity algorithms. – 0.8 for WordNet similarity. 09/06/2013 iSemantics 2013 18
Measure type DBpedia – Freebase (Person) DBpedia – Freebase (Film) DBpedia – Freebase (Software) DBpedia – LinkedMDB (Film) DBLP_RKB – DBLP_L3S (Article) Average Extension Based Algorithm Precision 0.8758 0.9737 0.6478 0.7560 1.0000 0.8427 Recall 0.8089* 0.5138 0.4339 0.8157 1.0000 0.7145 F measure 0.8410* 0.6727 0.5197 0.7848 1.0000 0.7656 WordNet Similarity Precision 0.5200 0.8620 0.7619 0.8823 1.0000 0.8052 Recall 0.4140* 0.3472 0.3018 0.3947 0.3333 0.3582 F measure 0.4609* 0.4950 0.4324 0.5454 0.5000 0.4867 Dice Similarity Precision 0.8064 0.9666 0.7659 1.0000 0.0000 0.7078 Recall 0.4777* 0.4027 0.3396 0.3421 0.0000 0.3124 F measure 0.6000* 0.5686 0.4705 0.5098 0.0000 0.4298 Jaro Similarity Precision 0.6774 0.8809 0.7755 0.9411 0.0000 0.6550 Recall 0.5350* 0.5138 0.3584 0.4210 0.0000 0.3656 F measure 0.5978* 0.6491 0.4903 0.5818 0.0000 0.4638 09/06/2013 iSemantics 2013 19 Alignment results * Marks estimated values for experiment 1 because of very large comparisons to check manually. Boldface marks highest result for each experiment.
Example identifications 09/06/2013 iSemantics 2013 20 Property pair types Dataset 1 (DBpedia) Dataset 2 (Freebase) Simple string similarity matches db:nationality fb:nationality db:religion fb:religion Synonymous matches db:occupation fb:profession db:battles fb:participated_in_conflicts Complex matches db:screenplay fb:written_by db:doctoralStudent fb:advisees
Example identifications 09/06/2013 iSemantics 2013 20 Property pair types Dataset 1 (DBpedia) Dataset 2 (Freebase) Simple string similarity matches db:nationality fb:nationality db:religion fb:religion Synonymous matches db:occupation fb:profession db:battles fb:participated_in_conflicts Complex matches db:screenplay fb:written_by db:doctoralStudent fb:advisees WordNet similarity failed to identify any of these
Background Statistical Equivalence of properties Evaluation Discussion, interesting facts, and future directions Conclusion 09/06/2013 21 Roadmap iSemantics 2013
 Our experiment covered multi-domain to multi-domain, multi- domain to specific domain and specific-domain to specific-domain dataset property alignment.     09/06/2013 iSemantics 2013 22 Discussion, interesting facts, and future directions
 Our experiment covered multi-domain to multi-domain, multi- domain to specific domain and specific-domain to specific-domain dataset property alignment.  In every experiment, the extension based algorithm outperformed others (F measure). F measure gain is in the range of 57% to 78%.    09/06/2013 iSemantics 2013 22 Discussion, interesting facts, and future directions
 Our experiment covered multi-domain to multi-domain, multi- domain to specific domain and specific-domain to specific-domain dataset property alignment.  In every experiment, the extension based algorithm outperformed others (F measure). F measure gain is in the range of 57% to 78%.  Some properties that are identified are intentionally different, e.g., db:distributor vs fb:production_companies. – This is because many companies produce and also distribute their films.   09/06/2013 iSemantics 2013 22 Discussion, interesting facts, and future directions
 Our experiment covered multi-domain to multi-domain, multi- domain to specific domain and specific-domain to specific-domain dataset property alignment.  In every experiment, the extension based algorithm outperformed others (F measure). F measure gain is in the range of 57% to 78%.  Some properties that are identified are intentionally different, e.g., db:distributor vs fb:production_companies. – This is because many companies produce and also distribute their films.  Some identified pairs are incorrect due to errors in data modeling. – For example, db:issue and fb:children.  09/06/2013 iSemantics 2013 22 Discussion, interesting facts, and future directions
 Our experiment covered multi-domain to multi-domain, multi- domain to specific domain and specific-domain to specific-domain dataset property alignment.  In every experiment, the extension based algorithm outperformed others (F measure). F measure gain is in the range of 57% to 78%.  Some properties that are identified are intentionally different, e.g., db:distributor vs fb:production_companies. – This is because many companies produce and also distribute their films.  Some identified pairs are incorrect due to errors in data modeling. – For example, db:issue and fb:children.  owl:sameAs linking issues in LOD (not linking exact same thing), e.g., linking London and Greater London. – We believe few misused links wont affect the algorithm as it decides on a match after analyzing many matches for a pair. 09/06/2013 iSemantics 2013 22 Discussion, interesting facts, and future directions
 Less number of interlinks. – Evolve over time. – Look for possible other types of ECR links (i.e., rdf:seeAlso).   09/06/2013 iSemantics 2013 23
 Less number of interlinks. – Evolve over time. – Look for possible other types of ECR links (i.e., rdf:seeAlso).  Properties do not have uniform distribution in a dataset. – Hence, some properties do not have enough matches or appearances. – This is due to rare classes and domains they belong to. – We can run the algorithm on instances that these less frequent properties appear iteratively.  09/06/2013 iSemantics 2013 23
 Less number of interlinks. – Evolve over time. – Look for possible other types of ECR links (i.e., rdf:seeAlso).  Properties do not have uniform distribution in a dataset. – Hence, some properties do not have enough matches or appearances. – This is due to rare classes and domains they belong to. – We can run the algorithm on instances that these less frequent properties appear iteratively. Current limitations, – Requires ECR links – Requires overlapping datasets – Object-type properties – Inability to identify property – sub property relationships 09/06/2013 iSemantics 2013 23
Background Statistical Equivalence of properties Evaluation Discussion, interesting facts, and future directions Conclusion 09/06/2013 24 Roadmap iSemantics 2013
We approximate owl:equivalentProperty using Statistical Equivalence of properties by analyzing property extensions, which is schema independent.    09/06/2013 iSemantics 2013 25 Conclusion
We approximate owl:equivalentProperty using Statistical Equivalence of properties by analyzing property extensions, which is schema independent. This novel extension based approach works well with interlinked datasets.   09/06/2013 iSemantics 2013 25 Conclusion
We approximate owl:equivalentProperty using Statistical Equivalence of properties by analyzing property extensions, which is schema independent. This novel extension based approach works well with interlinked datasets. The extension based approach outperforms syntax or dictionary based approaches. F measure gain in the range of 57% - 78%.  09/06/2013 iSemantics 2013 25 Conclusion
We approximate owl:equivalentProperty using Statistical Equivalence of properties by analyzing property extensions, which is schema independent. This novel extension based approach works well with interlinked datasets. The extension based approach outperforms syntax or dictionary based approaches. F measure gain in the range of 57% - 78%. It requires many comparisons, but can be easily parallelized evidenced by our Map-Reduce implementation. 09/06/2013 iSemantics 2013 25 Conclusion
26 Thank You http://knoesis.wright.edu/researchers/kalpa kalpa@knoesis.org Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton, Ohio, USA Questions ? 09/06/2013 iSemantics 2013

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Property Alignment on Linked Open Data

  • 1. A Statistical and Schema Independent Approach to Identify Equivalent Properties on Linked Data †Kno.e.sis Center Wright State University Dayton OH, USA ‡IBM T J Watson Research Center Yorktown Heights New York NY, USA Kalpa Gunaratna†, Krishnaprasad Thirunarayan†, Prateek Jain‡, Amit Sheth†, and Sanjaya Wijeratne† {kalpa,tkprasad,amit,sanjaya}@knoesis.org, jainpr@us.ibm.com iSemantics 2013, Graz, Austria
  • 2. 09/06/2013 2 Motivation Why we need property alignment and it is so important? iSemantics 2013
  • 3. 09/06/2013 3 Many datasets. We can query! iSemantics 2013
  • 5. 09/06/2013 3 Same information in different names. Therefore, data integration for better presentation is required. iSemantics 2013
  • 6. Background Statistical Equivalence of properties Evaluation Discussion, interesting facts, and future directions Conclusion 09/06/2013 4 Roadmap iSemantics 2013
  • 7. Existing techniques for property alignment fall into three categories. I. Syntactic/dictionary based – Uses string manipulation techniques, external dictionaries and lexical databases like WordNet. II. Schema dependent – Uses schema information such as, domain and range, definitions. III. Schema independent – Uses instance level information for the alignment.  Our approach falls under schema independent. 09/06/2013 5 Background iSemantics 2013
  • 8. Properties capture meaning of triples and hence they are complex in nature.    09/06/2013 6iSemantics 2013
  • 9. Properties capture meaning of triples and hence they are complex in nature. Syntactic or dictionary based approaches analyze property names for equivalence. But in LOD, name heterogeneities exist.   09/06/2013 6iSemantics 2013
  • 10. Properties capture meaning of triples and hence they are complex in nature. Syntactic or dictionary based approaches analyze property names for equivalence. But in LOD, name heterogeneities exist. Therefore, syntactic or dictionary based approaches have limited coverage in property alignment.  09/06/2013 6iSemantics 2013
  • 11. Properties capture meaning of triples and hence they are complex in nature. Syntactic or dictionary based approaches analyze property names for equivalence. But in LOD, name heterogeneities exist. Therefore, syntactic or dictionary based approaches have limited coverage in property alignment. Schema dependent approaches including processing domain and range, class level tags do not capture semantics of properties well. 09/06/2013 6iSemantics 2013
  • 12. Background Statistical Equivalence of properties Evaluation Discussion, interesting facts, and future directions Conclusion 09/06/2013 7 Roadmap iSemantics 2013
  • 13.  Statistical Equivalence is based on analyzing owl:equivalentProperty.  owl:equivalentProperty - properties that have same property extensions.          09/06/2013 8 Statistical Equivalence of properties iSemantics 2013
  • 14.  Statistical Equivalence is based on analyzing owl:equivalentProperty.  owl:equivalentProperty - properties that have same property extensions. Example 1: Property P is defined by the triples, { a P b, c P d, e P f } Property Q is defined by the triples, { a Q b, c Q d, e Q f } P and Q are owl:equivalentProperty, because they have the same extension, { {a,b}, {c,d}, {e,f} }     09/06/2013 8 Statistical Equivalence of properties iSemantics 2013
  • 15.  Statistical Equivalence is based on analyzing owl:equivalentProperty.  owl:equivalentProperty - properties that have same property extensions. Example 1: Property P is defined by the triples, { a P b, c P d, e P f } Property Q is defined by the triples, { a Q b, c Q d, e Q f } P and Q are owl:equivalentProperty, because they have the same extension, { {a,b}, {c,d}, {e,f} } Example 2: Property P is defined by the triples, { a P b, c P d, e P f } Property Q is defined by the triples, { a Q b, c Q d, e Q h } Then, P and Q are not owl:equivalentProperty, because their extensions are not the same. But they provide statistical evidence in support of equivalence. 09/06/2013 8 Statistical Equivalence of properties iSemantics 2013
  • 16. Intuition  Higher rate of subject-object matches in extensions leads to equivalent properties. In practice, it is hard to have exact same extensions for matching properties. Because, – Datasets are incomplete. – Same instance may be modelled differently in different datasets.  Therefore, we analyze the property extensions to identify equivalent properties between datasets.  We define the following notions. Let the statement below be true for all the definitions. S1P1O1 and S2P2O2 be two triples in Dataset D1 and D2 respectively. 09/06/2013 iSemantics 2013 9
  • 21. 09/06/2013 iSemantics 2013 12 Dataset 2Dataset 1 Candidate Matching Algorithm Process
  • 22. 09/06/2013 iSemantics 2013 12 I1 Dataset 2Dataset 1 I1=d1:Willis_Lamb Candidate Matching Algorithm Process
  • 23. 09/06/2013 iSemantics 2013 12 I1 I2 owl:sameAs Dataset 2Dataset 1 I1=d1:Willis_Lamb I2 =d2:willis_lamb Step 1 Candidate Matching Algorithm Process
  • 24. 09/06/2013 iSemantics 2013 12 I1 I2 I2 owl:sameAs P1=d1:doctoralStudent P2=d2:education. academic.advisees Dataset 2Dataset 1 d2:theodore_harold_maiman I1=d1:Willis_Lamb I2 =d2:willis_lamb I1 I1 d1:Theodore_Maiman triple 1 triple 2 triple 3 triple 4 triple 5 Step 1 Step2 Step2 Candidate Matching Algorithm Process
  • 25. 09/06/2013 iSemantics 2013 12 I1 I2 I2 matching resources owl:sameAs P1=d1:doctoralStudent P2=d2:education. academic.advisees Dataset 2Dataset 1 property P1 and property P2 are a candidate match d2:theodore_harold_maiman I1=d1:Willis_Lamb I2 =d2:willis_lamb I1 I1 d1:Theodore_Maiman triple 1 triple 2 triple 3 triple 4 triple 5 Step 1 Step2 Step2 Step 3 Candidate Matching Algorithm Process
  • 26. 09/06/2013 iSemantics 2013 13 Complexity: If the average number of properties for an entity is x and for each property, average number of objects is j. For n subjects, it requires n*j2*x2+2n comparisons. Since n > j, n > x, and x and j are independent of n, O(n).
  • 29. Background Statistical Equivalence of properties Evaluation Discussion, interesting facts, and future directions Conclusion 09/06/2013 16 Roadmap iSemantics 2013
  • 30. Objectives of the evaluation – Show the effectiveness of the approach in linked datasets – Compare with existing aligning techniques   09/06/2013 iSemantics 2013 17 Evaluation
  • 31. Objectives of the evaluation – Show the effectiveness of the approach in linked datasets – Compare with existing aligning techniques We selected 5000 instance samples from DBpedia, Freebase, LinkedMDB, DBLP L3S , and DBLP RKB Explorer datasets.  09/06/2013 iSemantics 2013 17 Evaluation
  • 32. Objectives of the evaluation – Show the effectiveness of the approach in linked datasets – Compare with existing aligning techniques We selected 5000 instance samples from DBpedia, Freebase, LinkedMDB, DBLP L3S , and DBLP RKB Explorer datasets. These datasets have, – Complete data for instances in different viewpoints – Many inter-links – Complex properties 09/06/2013 iSemantics 2013 17 Evaluation
  • 33. Experiment details – α = 0.5 for all experiments (works for LOD) except DBpedia and Freebase movie alignment where it was 0.7. 09/06/2013 iSemantics 2013 18
  • 34. Experiment details – α = 0.5 for all experiments (works for LOD) except DBpedia and Freebase movie alignment where it was 0.7. – k was set as 14, 6, 2, 2, and 2 respectively for Person, Film and Software between DBpedia and Freebase, Film between LinkedMDB and DBpedia, and article between DBLP datasets. 09/06/2013 iSemantics 2013 18
  • 35. Experiment details – α = 0.5 for all experiments (works for LOD) except DBpedia and Freebase movie alignment where it was 0.7. – k was set as 14, 6, 2, 2, and 2 respectively for Person, Film and Software between DBpedia and Freebase, Film between LinkedMDB and DBpedia, and article between DBLP datasets. – k can be estimated using the data as follows, – Set α = 0.5 and k = 2 (lowest positive values). – Get exact matching property (property names) pairs not identified by the algorithm and their μ – Get the average of those μ values 09/06/2013 iSemantics 2013 18
  • 36. Experiment details – α = 0.5 for all experiments (works for LOD) except DBpedia and Freebase movie alignment where it was 0.7. – k was set as 14, 6, 2, 2, and 2 respectively for Person, Film and Software between DBpedia and Freebase, Film between LinkedMDB and DBpedia, and article between DBLP datasets. – k can be estimated using the data as follows, – Set α = 0.5 and k = 2 (lowest positive values). – Get exact matching property (property names) pairs not identified by the algorithm and their μ – Get the average of those μ values – 0.92 for string similarity algorithms. 09/06/2013 iSemantics 2013 18
  • 37. Experiment details – α = 0.5 for all experiments (works for LOD) except DBpedia and Freebase movie alignment where it was 0.7. – k was set as 14, 6, 2, 2, and 2 respectively for Person, Film and Software between DBpedia and Freebase, Film between LinkedMDB and DBpedia, and article between DBLP datasets. – k can be estimated using the data as follows, – Set α = 0.5 and k = 2 (lowest positive values). – Get exact matching property (property names) pairs not identified by the algorithm and their μ – Get the average of those μ values – 0.92 for string similarity algorithms. – 0.8 for WordNet similarity. 09/06/2013 iSemantics 2013 18
  • 38. Measure type DBpedia – Freebase (Person) DBpedia – Freebase (Film) DBpedia – Freebase (Software) DBpedia – LinkedMDB (Film) DBLP_RKB – DBLP_L3S (Article) Average Extension Based Algorithm Precision 0.8758 0.9737 0.6478 0.7560 1.0000 0.8427 Recall 0.8089* 0.5138 0.4339 0.8157 1.0000 0.7145 F measure 0.8410* 0.6727 0.5197 0.7848 1.0000 0.7656 WordNet Similarity Precision 0.5200 0.8620 0.7619 0.8823 1.0000 0.8052 Recall 0.4140* 0.3472 0.3018 0.3947 0.3333 0.3582 F measure 0.4609* 0.4950 0.4324 0.5454 0.5000 0.4867 Dice Similarity Precision 0.8064 0.9666 0.7659 1.0000 0.0000 0.7078 Recall 0.4777* 0.4027 0.3396 0.3421 0.0000 0.3124 F measure 0.6000* 0.5686 0.4705 0.5098 0.0000 0.4298 Jaro Similarity Precision 0.6774 0.8809 0.7755 0.9411 0.0000 0.6550 Recall 0.5350* 0.5138 0.3584 0.4210 0.0000 0.3656 F measure 0.5978* 0.6491 0.4903 0.5818 0.0000 0.4638 09/06/2013 iSemantics 2013 19 Alignment results * Marks estimated values for experiment 1 because of very large comparisons to check manually. Boldface marks highest result for each experiment.
  • 39. Example identifications 09/06/2013 iSemantics 2013 20 Property pair types Dataset 1 (DBpedia) Dataset 2 (Freebase) Simple string similarity matches db:nationality fb:nationality db:religion fb:religion Synonymous matches db:occupation fb:profession db:battles fb:participated_in_conflicts Complex matches db:screenplay fb:written_by db:doctoralStudent fb:advisees
  • 40. Example identifications 09/06/2013 iSemantics 2013 20 Property pair types Dataset 1 (DBpedia) Dataset 2 (Freebase) Simple string similarity matches db:nationality fb:nationality db:religion fb:religion Synonymous matches db:occupation fb:profession db:battles fb:participated_in_conflicts Complex matches db:screenplay fb:written_by db:doctoralStudent fb:advisees WordNet similarity failed to identify any of these
  • 41. Background Statistical Equivalence of properties Evaluation Discussion, interesting facts, and future directions Conclusion 09/06/2013 21 Roadmap iSemantics 2013
  • 42.  Our experiment covered multi-domain to multi-domain, multi- domain to specific domain and specific-domain to specific-domain dataset property alignment.     09/06/2013 iSemantics 2013 22 Discussion, interesting facts, and future directions
  • 43.  Our experiment covered multi-domain to multi-domain, multi- domain to specific domain and specific-domain to specific-domain dataset property alignment.  In every experiment, the extension based algorithm outperformed others (F measure). F measure gain is in the range of 57% to 78%.    09/06/2013 iSemantics 2013 22 Discussion, interesting facts, and future directions
  • 44.  Our experiment covered multi-domain to multi-domain, multi- domain to specific domain and specific-domain to specific-domain dataset property alignment.  In every experiment, the extension based algorithm outperformed others (F measure). F measure gain is in the range of 57% to 78%.  Some properties that are identified are intentionally different, e.g., db:distributor vs fb:production_companies. – This is because many companies produce and also distribute their films.   09/06/2013 iSemantics 2013 22 Discussion, interesting facts, and future directions
  • 45.  Our experiment covered multi-domain to multi-domain, multi- domain to specific domain and specific-domain to specific-domain dataset property alignment.  In every experiment, the extension based algorithm outperformed others (F measure). F measure gain is in the range of 57% to 78%.  Some properties that are identified are intentionally different, e.g., db:distributor vs fb:production_companies. – This is because many companies produce and also distribute their films.  Some identified pairs are incorrect due to errors in data modeling. – For example, db:issue and fb:children.  09/06/2013 iSemantics 2013 22 Discussion, interesting facts, and future directions
  • 46.  Our experiment covered multi-domain to multi-domain, multi- domain to specific domain and specific-domain to specific-domain dataset property alignment.  In every experiment, the extension based algorithm outperformed others (F measure). F measure gain is in the range of 57% to 78%.  Some properties that are identified are intentionally different, e.g., db:distributor vs fb:production_companies. – This is because many companies produce and also distribute their films.  Some identified pairs are incorrect due to errors in data modeling. – For example, db:issue and fb:children.  owl:sameAs linking issues in LOD (not linking exact same thing), e.g., linking London and Greater London. – We believe few misused links wont affect the algorithm as it decides on a match after analyzing many matches for a pair. 09/06/2013 iSemantics 2013 22 Discussion, interesting facts, and future directions
  • 47.  Less number of interlinks. – Evolve over time. – Look for possible other types of ECR links (i.e., rdf:seeAlso).   09/06/2013 iSemantics 2013 23
  • 48.  Less number of interlinks. – Evolve over time. – Look for possible other types of ECR links (i.e., rdf:seeAlso).  Properties do not have uniform distribution in a dataset. – Hence, some properties do not have enough matches or appearances. – This is due to rare classes and domains they belong to. – We can run the algorithm on instances that these less frequent properties appear iteratively.  09/06/2013 iSemantics 2013 23
  • 49.  Less number of interlinks. – Evolve over time. – Look for possible other types of ECR links (i.e., rdf:seeAlso).  Properties do not have uniform distribution in a dataset. – Hence, some properties do not have enough matches or appearances. – This is due to rare classes and domains they belong to. – We can run the algorithm on instances that these less frequent properties appear iteratively. Current limitations, – Requires ECR links – Requires overlapping datasets – Object-type properties – Inability to identify property – sub property relationships 09/06/2013 iSemantics 2013 23
  • 50. Background Statistical Equivalence of properties Evaluation Discussion, interesting facts, and future directions Conclusion 09/06/2013 24 Roadmap iSemantics 2013
  • 51. We approximate owl:equivalentProperty using Statistical Equivalence of properties by analyzing property extensions, which is schema independent.    09/06/2013 iSemantics 2013 25 Conclusion
  • 52. We approximate owl:equivalentProperty using Statistical Equivalence of properties by analyzing property extensions, which is schema independent. This novel extension based approach works well with interlinked datasets.   09/06/2013 iSemantics 2013 25 Conclusion
  • 53. We approximate owl:equivalentProperty using Statistical Equivalence of properties by analyzing property extensions, which is schema independent. This novel extension based approach works well with interlinked datasets. The extension based approach outperforms syntax or dictionary based approaches. F measure gain in the range of 57% - 78%.  09/06/2013 iSemantics 2013 25 Conclusion
  • 54. We approximate owl:equivalentProperty using Statistical Equivalence of properties by analyzing property extensions, which is schema independent. This novel extension based approach works well with interlinked datasets. The extension based approach outperforms syntax or dictionary based approaches. F measure gain in the range of 57% - 78%. It requires many comparisons, but can be easily parallelized evidenced by our Map-Reduce implementation. 09/06/2013 iSemantics 2013 25 Conclusion
  • 55. 26 Thank You http://knoesis.wright.edu/researchers/kalpa kalpa@knoesis.org Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton, Ohio, USA Questions ? 09/06/2013 iSemantics 2013

Editor's Notes

  • #34: k in some datasets are low because there are low appearing property pairs as well as popular property pairs. Because of this, sometimes lower k values as 2 produces better results but may be with low confidence.
  • #35: k in some datasets are low because there are low appearing property pairs as well as popular property pairs. Because of this, sometimes lower k values as 2 produces better results but may be with low confidence.
  • #36: k in some datasets are low because there are low appearing property pairs as well as popular property pairs. Because of this, sometimes lower k values as 2 produces better results but may be with low confidence.
  • #37: k in some datasets are low because there are low appearing property pairs as well as popular property pairs. Because of this, sometimes lower k values as 2 produces better results but may be with low confidence.
  • #38: k in some datasets are low because there are low appearing property pairs as well as popular property pairs. Because of this, sometimes lower k values as 2 produces better results but may be with low confidence.