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Increasingly semantic web applications are consuming data from
different sources. It is desirable to automatically detect errors and
potential issues in semantic web data before using the data. Semantic
web instance data is growing and is dominating the Semantic Web on the
web [2].
In this work, we identify a new research problem semantic web instance data
evaluation. The work, while closely related to ontology
evaluation, is different in that it focuses on potential and actual
compatibility of the instance data with the term definitions in the
corresponding ontologies. Unlike ontology evaluation which checks
logical consistency of the term definitions, this effort includes
issues of representation style in addition to provable errors of syntax
and semantics.
We found no existing work dedicated to semantic web instance data evaluation. Previous related work aimed at general knowledge base environments mainly for ontology evaluation (e.g., [6],[4],[1],[3]). Related work on OWL DL ontology debugging (e.g., [7],[10],[9]) concentrates on diagnosing semantic consistency issues. Semantic web instance data evaluation raises two challenges: (i) to identify issues in instance data; and (ii) to develop a customizable and extensible approach to meet the diverse evaluation requirements required by different semantic web applications.
Figure1 depicts our service-oriented architecture. The semantic web instance data evaluation process is composed from interactions of independent evaluation services that communicate using a simple data interface. Each service focuses on one evaluation task and generates the corresponding evaluation report. An evaluation report entry typically covers the severity, symptom diagnosis, and optional repair instructions.
In order to fulfill the separation requirement, we need to identify the scope of instance data and ontologies. Our evaluation architecture differentiated three collections of semantic web data: the instance data to be evaluated, the automatically-loaded referenced ontologies that define the classes and properties being instantiated in the instance data, and the user-provided optional ontologies that add definitions and restrictions to the referenced ontologies. Instance data evaluation considers only the issues introduced by the instance data and ignores the issues that previously existed within the referenced ontologies and/or the optional ontologies.
We identified six types of services under the three main categories suggested by previous work on ontology evaluation[7],[9].
Our service-oriented architecture is designed to be customizable and extensible. The two topmost types of service in Figure1 must be executed first in sequence, and the remaining services, which take the same input, can be executed in any combination and any order. Moreover, our architecture is extensible, for example, an OWL species classification service can be implemented using different OWL reasoners; and new user-defined services for domain specific style evaluation can be added as plug-ins.
In our Inference Web[5] project, we have implemented our architecture in a tool called PmlValidator for evaluating instance data encoded using the Proof Markup Language (PML)[8]. PmlValidator implemented and integrated evaluation services at the JAVA API level, and it is available online as a web service at http://onto.rpi.edu/iw2api/doc_pmlvalidator.
PML instance data is encoded using PML ontologies. Since the PML ontologies use only OWL DL, we can leverage many existing tools to provide syntax and semantics evaluation services. RDF parsing and validation is implemented using Jena, which is also used by the W3C RDF Validator Service. OWL species classification and OWL DL semantics validation are implemented using the OWL DL reasoner Pellet.
Our current implementation for referenced ontology resolution uses the following heuristics: the referenced ontologies are (i) ontologies linked by the namespace of classes and properties instantiated in the instance data, and (ii) ontologies recursively imported by the referenced ontologies.
PmlValidator also checks style issues beyond the reach of OWL DL reasoners. Figure2 provides several examples to illustrate the following three issues checked by our general style evaluation.
(i) Issues related to min cardinality restrictions. OWL DL reasoners, using the open world assumption, will not report any inconsistencies in g1 based on o1 because they assume the expected RDF triples may be found outside g1; but style issues (e.g. missing expected RDF triples) may be reported when g1 is used by applications using the closed world assumption, e.g. database applications that require non-null fields.
(ii) Issues related to max cardinality restrictions. OWL DL reasoners will not report any inconsistencies in g2 based on o1 because they cannot find different from assertion in g2; but style issues (e.g., existence of unwanted RDF triples) may be reported when g2 is used by applications using the unique names assumption, i.e. any two individuals with different names are different from each other unless they have been explicitly asserted to be the same.
(iii) Issues related to multiple class-memberships. OWL DL reasoners can, for example, infer that ex:docFrag3 is a member of both pmlp:Agent and pmlp:Document in g3 using the rdfs:range statement in o2. They will only confirm inconsistency in g3 but not in g4 because no relation between pmlp:Language and pmlp:Document has been found in o2. While it might be unnecessary to exhaust all pairs of classes without any asserted relations such as rdfs:subClassOf and owl:disjointWith, we have found it a useful precaution to check for relationships, particularly those generated from local property value restrictions and global domain and range restrictions.
Pml Validator also adds the following service for domain-specific style evaluation: (i) existence of PML instance data: whether the instance data contains at least one instantiation of a class or a property defined in the PML ontologies. (ii) anonymous URI: instances of some specific PML classes must not be anonymous.
Semantic web instance data evaluation brings unique and important benefits to semantic web applications: data publishers may detect and thus fix outstanding issues in the instance data; and data consumers may verify their expectations for incoming semantic web instance data and to customize and extend the domain specific evaluation services if necessary. The above style evaluation services have been implemented and tested in PmlValidator.
Future work includes evaluation experiments and performance study of PmlValidator on large scale instance data, implementation extension and improvement to cover more style issues in instance data.
Acknowledgement: This work is supported by NSF #5710001895, DARPA #F30602-00-2-0579, #55-300000680 to-2 R2.
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