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Curating the Specificity of Ontological Descriptions under Ontology Evolution

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Journal on Data Semantics

Abstract

Semantic Web Ontologies are not static, but evolve, however, this evolution usually happens independently of the ontological instance descriptions (usually referred as “metadata”) which are stored in the various Metadata Repositories or Knowledge Bases. Nevertheless, it is a common practice for a MR/KB to periodically update its ontologies to their latest versions by “migrating” the available instance descriptions to the latest ontology versions. Such migrations incur gaps regarding the specificity of the migrated metadata, i.e. inability to distinguish those descriptions that should be reexamined (for possible specialization as consequence of the migration) from those for which no reexamination is justified. Consequently, there is a need for principles, techniques, and tools for managing the uncertainty incurred by such migrations, specifically techniques for (a) identifying automatically the descriptions that are candidates for specialization, (b) computing, ranking and recommending possible specializations, and (c) flexible interactive techniques for updating the available descriptions (and their candidate specializations), after the user (curator of the repository) accepts/rejects such recommendations. This problem is especially important for curated knowledge bases which have increased quality requirements (as in E-science). In this paper, we formalize the problem and propose principles, rules, and algorithms for realizing the aforementioned scenario, for the RDF/S framework. Finally, we report experimental results demonstrating the feasibility of the approach.

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Notes

  1. In this paper, by the term of ontology we refer only to schema information.

  2. Note that according to RDF/S semantics [13], \(\le ^*_{cl}\) and \(\le ^*_{pr}\) are reflexive and transitive relations.

  3. A lower set (else called downward closed set) is a subset \(Y\) of a given partially ordered set \((X,\le )\) s.t. for all elements \(x\) and \(y,\) if \(x \le y\) and \(y\) is an element of \(Y,\) then \(x\) is also in \(Y.\)

  4. Upper set is the dual notion of lower set.

  5. Definition 5(ii) is satisfied because if it holds that \(\lnot valid(o, pr, o^{\prime }, K)\) and \(pr^{\prime } \le _{pr}^* pr\) then certainly it holds that \(\lnot valid(o, pr^{\prime }, o^{\prime }, K),\) due to (ii) of Definition 2. Thus, \( SubTriples(Invalid(K))=Invalid(K) .\) Definition 5(iii) is obvious.

  6. We discuss the notion of validity at Sect. 11 along with the discussion of the related works that focus on that issue.

  7. If \((o \;type \;c) \in K^{\prime }\) then \((c \;type \; Class ) \in \mathcal{C }(K^{\prime })\) and if \((o \;pr \;o^{\prime }) \in K^{\prime }\) then \((pr \;type \;Property) \in \mathcal{C }(K^{\prime }).\)

  8. NBC stands for non-backwards compatible.

  9. The relationships \(\le ^*_{cl}\) and \(\le ^*_{pr}\) hold in \(K^{\prime }.\)

  10. Note that \(t\not \in B_{K^{\prime }},\) if the class \(c\) or property \(pr\) appearing in \(t\) have been deleted in \(K^{\prime }.\)

  11. To keep notations simple, here and below we omit the subscripts \(K\) from the notations of \(\mathcal{C },\) \(P\) and \(M.\)

  12. In the figure, \(posCl(\mathtt{{John}})\) denotes the possible classes of \(\mathtt{{John}}.\)

  13. More information about the prototype is available through its web page: http://www.ics.forth.gr/isl/RIMQA.

  14. http://athena.ics.forth.gr:9090/SWKM.

  15. All experiments were carried out in an ordinary laptop with processor Pentium(R) Dual-Core CPU T4200 @2.0 Ghz, 2 GB Ram, running Windows Vista.

  16. http://www.musicontology.com.

  17. VR stands for Value vs Rank (it measures the relationship between the \(i^{th}\) biggest value and its rank \(i,\) assuming a descending order).

  18. PDF stands for Probability Density Function.

  19. CIDOC CRM (ISO 21127) is a core ontology describing the underlying semantics of data schemata and structures from all museum disciplines and archives (its RDF representation contains 78 classes and 250 properties from which 7 are literal-valued) (available from http://www.cidoc-crm.org/).

  20. For example, there are tools that scan Java bytecode for method calls and create a description of the dependencies between classes and the package/archive encoded in RDF. Other tools transform Maven POM (Project Object Model) files into RDF.

  21. http://leo.hua.gr/palc2012/?q=en.

  22. http://www.cidoc-crm.org/.

  23. XML Schema Definition.

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Acknowledgments

Work done in the context of NoE APARSEN (Alliance Permanent Access to the Records of Science in Europe, FP7, Proj. No 269977, 2011–2014). Many thanks to Xristina Lantzaki who participated in the user study.

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Authors and Affiliations

  1. Institute of Computer Science, FORTH-ICS, Crete, Greece

    Yannis Tzitzikas, Mary Kampouraki & Anastasia Analyti

  2. Department of Computer Science, University of Crete, Crete, Greece

    Yannis Tzitzikas

Authors
  1. Yannis Tzitzikas
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  2. Mary Kampouraki
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Correspondence to Anastasia Analyti.

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Appendices

Appendix A: List of symbols

See Table 12.

Table 12 Symbols and description
Full size table

Appendix B: Example of Applying the Algorithm for Backwards Compatible Migration

Example 9

Consider the example of Fig. 5 and suppose that \(P_K=\{\mathtt{(Fiat{\_}1\;type\;Vehicle)},\;\mathtt{(Bob\,uses\,BMW{\_}1)},\;\mathtt{(Alice\,works\,at\,FORTH)}\}.\) Executing Algorithm 1, step by step, we have that:

(1) \(K^{\prime } = (S_{K^{\prime }}, I_K);\)

(2) \(P_{K\_Add} = \emptyset ;\)

(3) \(P_{K\_Del} = \emptyset ;\)

(4) \(NC = \{\mathtt{Van,\;Jeep,\;Adult,\;Institute,\;University}\};\)

(6) \(P_{{K}{\_}Add} = P_{{K}{\_}Add} \cup \{\mathtt{(Fiat{\_}1\,type\,Van)}, \mathtt{(BMW{\_}1\,type\,Van)},\;\mathtt{(Fiat{\_}1\,type\,Jeep)}, \mathtt{(BMW{\_}1\,type\,Jeep)}, \mathtt{(Bob\,type\,Adult)}, \mathtt{(Alice type Adult)}, \mathtt{(Computer Science Department type } \mathtt{Institute)}, \mathtt{(FORTH } \mathtt{type Institute)}, \mathtt{(Computer Science Department type } \mathtt{University)}, \mathtt{(FORTH type University)}\};\)

(8) \(P_{K{\_}Del} = P_{K{\_}Del} \cup \{\emptyset \};\)

(9) \(NP = \{\mathtt{paid\,from}\};\)

(11) \(P_{K\_Add} = P_{K\_Add} \cup \{\mathtt{(Alice\,paid\;from\,Computer\,Science\,Department)}, \mathtt{(Bob\,paid\,from\,FORTH)}\};\)

(13) \(P_{K{\_}Del} = P_{K{\_}Del} \cup \{\mathtt{(Alice\,works\,at\,FORTH)}\};\)

Note that, \(\mathtt{(Alice related to FORTH)} \notin (P_K \cup \mathcal C _i(K^{\prime }))\) and \(\mathtt{works\;at}\) \(\le _{cl}\) \(\mathtt{related\;to}\) holds in \(K^{\prime }.\) So, we have to move \(\mathtt{(Alice\;works\;at\;FORTH)}\) from \(P_K\) to \(F_{K^{\prime }}\) (due to Rule \(R2\)).

(14) \(P_{K\_Del} = P_{K\_Del} \cup \{\mathtt{(Fiat\_1\;type\;Vehicle)},\;(\mathtt{Bob\;uses\;BMW\_1})\};\)

Note that \(P_{K\_Del}\) is updated by those triples that belong to \(P_K\) and now belong to \(\mathcal C _i(K^{\prime }).\) So, we have to remove them from \(P_{K}.\)

(15) \(P_{K^{\prime }} = P_K \setminus P_{K\_Del}\;=\)

\(\{\mathtt{(Fiat\_1\;type\;Vehicle),\;(Bob\;uses\;BMW\_1)},\;\mathtt{(Alice\;works\;at\;FORTH)}\} \setminus \)

\(\{\mathtt{(Fiat\_1\;type\;Vehicle)},\;\mathtt{(Bob\;uses\;\mathtt BMW\_1)},\;\mathtt{(Alice\;works\;at\;FORTH)}\} = \emptyset ;\)

(16) \(P_{K^{\prime }} = \{\mathtt{(Fiat\_1\;type\;Van)},\;\mathtt{(BMW\_1\;type\;Van)},\;\mathtt{(Fiat\_1\;type\;Jeep)},\)

\(\mathtt{(BMW\_1\;type\;Jeep)},\;\mathtt{(Bob\;type\;Adult)},\;\mathtt{(Alice\;type\;Adult)},\)

\(\mathtt{(Computer\;Science\;Department\;type\;Institute)},\;\mathtt{(FORTH\;type\;Institute)},\)

\(\mathtt{(Computer\;Science\;Department\;type\;University)},\;\mathtt{(FORTH\;type\;University)},\)

\(\mathtt{(Alice\;paid\;from\;Computer\;Science\;Department)},\;\mathtt{(Bob\;paid\;from\;FORTH)}\}\)

(17) Return \(P_{K^{\prime }};\)

In order to explain line 8 in part B of Algorithm 1, consider Fig. 5. Suppose that we have another version \(S_{K^{\prime \prime }}=S_{K^{\prime }} \cup \{(\mathtt{Van}\) \(\le _{cl}\) \(\mathtt{LoadCarrying\;Vehicle})\},\) where we have a new specialization relationship, i.e. \(\mathtt{Van}\) \(\le _{cl}\) \(\mathtt{LoadCarrying\;Vehicle}.\) Then, according to line 8 of Algorithm 1, we have that \(P_{K\_Del} = P_{K\_Del} \cup \{(\mathtt Fiat\_1\;type\;Van)\}.\) This is because it holds that \(\mathtt{(Fiat\_1}\) \(\mathtt{\;type\;LoadCarrying\;}\) \(\mathtt{Vehicle)}\not \in P_{K^{\prime }}\cup \mathcal C _i(K^{\prime \prime })\) and \((\mathtt{Van}\) \( \le _{cl}\) \(\mathtt{LoadCarrying\;}\) \(\mathtt{Vehicle})\in S_{K^{\prime \prime }}.\) So, we have to move \(\mathtt{(Fiat\_1\;}\) \(\mathtt{type\;Van)}\) from \(P_{K^{\prime }}\) to \(F_{K^{\prime \prime }}\) (due to Rule \(R1\)).

Note that if \((o\;type\;c) \in \mathcal C _i(K^{\prime })\) and \((o\;type\;c^{\prime })\not \in \mathcal C _i(K^{\prime }) \cup P_{K^{\prime }},\) for all \(c^{\prime }\le ^*_{cl} c\) and \(c^{\prime } \not = c,\) then the \( MSA \) property holds for the class instance triple \((o\;type\;c).\) Similarly, if \((o\;pr\;o^{\prime }) \in \mathcal C _i(K^{\prime })\) and \((o\;pr^{\prime }\;o^{\prime })\not \in \mathcal C _i(K^{\prime }) \cup P_{K^{\prime }},\) for all \(pr^{\prime }\le ^*_{pr} pr\) and \(pr^{\prime } \not = pr,\) then the \( MSA \) property holds for the property instance triple \((o\;pr\;o^{\prime }).\)

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Tzitzikas, Y., Kampouraki, M. & Analyti, A. Curating the Specificity of Ontological Descriptions under Ontology Evolution. J Data Semant 3, 75–106 (2014). https://doi.org/10.1007/s13740-013-0027-z

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