Management of nested collections of resources in Spatial ... · tensible to any type of Digital Library collections. 1. Introduction As regards the cataloguing of geographic resources,

Management of nested collections of resources in Spatial Data Infrastructures

J. Nogueras-Iso, F.J. Zarazaga-Soria, P.R. Muro-MedranoComputer Science and Systems Engineering Department,University of Zaragoza

Marıa de Luna, 1. 50018-Zaragoza (Spain)jnog, javy, [email protected]

http://iaaa.cps.unizar.es

Abstract

The management of collections of resources is an impor-tant issue in Digital Libraries. The modelling of collectionsprovides added-value because they facilitate the organiza-tion of resources and special services may be tailored to thecharacteristics of the collection. This paper will provide ametadata solution to manage nested collections in digitallibrary catalogs, which is based on XML technologies andconcepts derived from knowledge bases. The managementof collections is particularly relevant in the context of Ge-olibraries and Spatial Data Infrastructures. Given the highvolumes of geographic information, it is very frequent tofind collections that arise as a result of the fragmentation ofgeographic resources into datasets of manageable size andsimilar scale. However, the concepts presented here are ex-tensible to any type of Digital Library collections.

1. Introduction

As regards the cataloguing of geographic resources, animportant circumstance to take into account is the existenceof collections or aggregation of geographic resources (ordatasets) that can be considered as a unique entity. Most ofthese collections arise as a result of the fragmentation ofgeographic resources into datasets of manageable size andsimilar scale. In this sense, for example, the Spanish Na-tional Geographic Institute (IGN) offers distinct versionsof its products (Cartographic Numeric Base BCN, NationalTopographic Map MTN, Digital Terrain Model MDT,... )according to different scales: BCN200 identifies the BCN at1:200,000 scale; BCN25 identifies BCN at 1:25,000 scaleand so on. Each product-version pair compiles the set offiles into which the Spanish territory was divided so as toprovide, at the scale required, a number of files with rea-sonable size. Those files are usually named ”tiles” and IGNestablishes for each scale the numbering and spatial extentcovered by these tiles. Besides, each aforementioned prod-uct may be in turn composed of several information lay-

ers. For example, each BCN tile is composed of the follow-ing thematic layers: administrative divisions; altimetry; hy-drography and coasts; buildings and constructions; commu-nication networks; utilities; and geodetic vertexes. That isto say, it is also common to organize resources in more thanone level of aggregation, originating nested collections. Bynested collections it is meant that a collection can be in-cluded as a part of another collection. This recursive defini-tion of collections enables the hierarchical organization ofresources in a repository.

When providers or distributors of geographic informa-tion want to publish the content of their holdings, theymust provide standardized descriptions of their datasets(metadata), which are later incorporated into data catalogsand clearinghouses. The creation and maintenance of ge-ographic metadata is a time consuming and thorough pro-cess. This circumstance is especially problematic if a collec-tion of thousands of datasets must be documented. On onehand, the datasets belonging to the same collection sharea high percentage of meta-information that must be repli-cated multiple times. And on the other hand, users of ge-ographic information are accustomed to manage the entirecollection as a unique entity (e.g. the National TopographicMap at scale 1:50,000), which should be returned by datacatalogs as a unique result instead of displaying the com-plete list of thousands of files that conform the collection.

The problem of how to describe collections within meta-data is an important issue in new proposals for geographicinformation metadata standards (e.g., ISO19115 [7] or Re-mote extensions of CSDGM [2]). Thus, most of these meta-data standards define elements to point at related resources,usually by means of a string or number conforming to a for-mal identification system. However, a catalog system cannot manage collections just enabling librarians to manuallyedit the fields concerned with these links. There are sev-eral aspects that justify a more complex implementation ofcollections. Firstly, the resources (and metadata records de-scribing them) must be uniquely identified, at least withinthe local catalog. Thus, all the references among the aggre-

Proceedings of the 15th International Workshop on Database and Expert Systems Applications (DEXA’04) 1529-4188/04 $ 20.00 IEEE

(Draft) Proceedings of the 15th Database and Experts Systems Applications - DEXA'04 (1st International Workshop on Geographic Information Management - GIM'04). 2004, p. 878-882.

gate and the parts must be always up-to-date whenever acomponent of the aggregation is added or removed. Sec-ondly, the components that form part of a collection usuallyshare a high percentage of meta-information (e.g., abstract,topic category, etc.). There are metadata elements whosecontent could be inherited from the metadata record that de-scribes the collection. But if the catalog does not providean automatic mechanism to inherit meta-information, meta-data creators must replicate common descriptions for eachdataset. And thirdly, some values of the metadata elements(e.g. the temporal or spatial extent) in the collection meta-data record are aggregated or averaged over the values ofthe components of the collection.

The objective of this paper will be to provide a meta-data solution to manage nested collections of geographicresources, which is based on XML technologies and con-cepts derived from knowledge bases. The most acceptedway to exchange metadata is by means of XML documents,whose syntax is enforced by control files in the form ofDTDs or XML-Schemas. Thus, a system managing meta-data records as XML documents will be highly indepen-dent of the structure of metadata standards. This paper pro-poses the construction of catalog services over a knowledgebase component, which is able to store the different typesof metadata schemas supported, the aggregation relationsestablished among these schemas, and the inference mech-anisms that these relations will provide.

2. Related work

According to [6], the precedent of the management ofcollections in digital libraries can be found in the world ofonline bibliographic services, which sum up the content ofmaterially significant databases. On the other hand, as tra-ditional libraries gave public access to their catalogs via theInternet, several standardization initiatives appeared to de-scribe the contents of a collection such as: the EncodedArchival Description standard for the encoding archivalfinding aids to collections of materials; the Z39.50 Profilefor access to digital collections; or the Resarch Support Li-braries Programme Collection Description Project [9].

One of the most relevant works to facilitate the access todigital library collections is the STARTS protocol [4]. Thisprotocol for internet search and retrieval facilitates the taskof querying multiple document sources, namely text collec-tions accessed via search engines. The goal of STARTS isthat the search engines implementing the protocol will as-sist a meta-searcher in choosing the best sources to evaluatea query, evaluating the query at these resources, and merg-ing the query results from these sources. The basis for theimplementation of STARTS protocol is the availability ofsource metadata, which describes the contents of the collec-tion. This metadata consists of two pieces: the source meta-data attributes, which includes information that a meta-

searcher can use to rewrite the queries sent to the source aswell as other attributes manually generated (e.g., abstract,contact or access constraints); and the source content sum-mary which contains the information that is automaticallygenerated such as the list of words that appear in the source,the statistics for each word listed, or the total number ofdocuments in the source.

Within the context of geolibraries, a good example ofa system handing collections is the Alexandria Digital Li-brary (ADL) project [6]. Collections of geographically ref-erenced items (maps, aerial photographs, satellite images,etc.) are described by means of: collection level metadata,which is a standardized description about the collection;and item level metadata, which are the individual descrip-tions of the items that form part of the collection. Sim-ilar to STARTS source metadata, collection level meta-data is also divided into: contextual metadata (equivalentto STARTS source metadata attributes) and inherent meta-data (the STARTS source content summary). The main con-tribution of ADL with respect to previous approaches isits geographic-oriented approach. Unlike text-oriented ap-proaches (STARTS, bibliographic databases) it has identi-fied the relevance of presenting graphic characteristics ofthe collections such as the visualizations of the geographicand temporal coverages.

3. The Metadata Knowledge Base

As mentioned in the introduction, the metadata recordsdescribing the components and the own collection as awhole only differ in a few set of metadata elements. Justobserving the features of the most frequent types of collec-tions, one could imagine the metadata elements that willprobably differentiate the description of two componentsin the same collection. That is to say, instead of creatingcomplete descriptions of each component in the collectionmanually, a system could automate this labor just having ahigh-level description of the entire collection and the spe-cific values of just a few elements for each component. Forinstance, the BCN200 product (mentioned in the introduc-tion) is an example of a spatial collection (the componentsare spatially distributed to cover a wide area) that groups thefiles providing real data for each province in Spain. And themetadata describing each component uniquely differ in thespecific title of the component; the reference date; the geo-graphic location identifier (code and name of province); thebounding box; and the coordinates reference system. Be-sides, there are also some elements that are subject to besummarized and stored as common elements at collectionlevel. For instance, for spatial collections, it results interest-ing to calculate the minimum bounding box that covers thebounding boxes of the components.

Therefore, our ideal catalog system should enable aflexible definition of metadata records (probably not con-



strained to a specific metadata standard), provide inferencemechanisms between relations established between meta-data records, and, as mentioned in the introduction, supportrecursive levels of aggregations (i.e. nested collections). Asa possible solution, such a catalog could be developed over aknowledge-base component. A Knowledge Base System isdefined as a system that includes a knowledge base abouta domain and programs that include rules for processingthe knowledge and for solving problems relating to the do-main. And one way to represent this knowledge could bebased on the concept of ontology. An ontology is usuallydefined as an ”explicit formal specification of a shared con-ceptualization” [5]. In the context of information systemsand knowledge representation, the term ontology is usedto denote a knowledge model, which represents a particu-lar domain of interest. And more specifically in the con-text of metadata standards, the own structure of metadatastandards (also called metadata schemas) can be consideredas ontologies, where metadata records are the instances ofthose ontologies. Therefore, ontologies may be used to pro-file the metadata needs of a specific resource and its rela-tionship with the metadata of other related resources. Forinstance, the ISO19115 geographic metadata standard [7]has been modelled as an ontology using the Protege ontol-ogy editor (http://protege.stanford.edu/).

Fig. 1 shows the ontology describing the metadata needsfor the collection and components of the BCN200 using aframe-slot-facet representation [8]. There, each frame rep-resents a different type of metadata schema. Although ametadata schema is usually structured in sections and sub-sections, for the sake of clarity, it is assumed that theseschemas can be simplified into a flattened list of elementsabstracting us from their complexity. The slots displayedinside frames correspond to some metadata elements ofISO19115. Besides, it can be observed that there are threetypes of relations between frames: the is-a hierarchy for cre-ating more specific metadata schemas with more slots ormodifying the slots of the parent frame; the whole-part hier-archy for establishing the relation between the metadata de-scribing a collection and the metadata describing the com-ponents of that collection; and the instance hierarchy, whichis used to relate instances of a metadata schema to the frameestablishing its syntax.

Another question that may arise from the model infig. 1 is why we should create two different schemas,MD Collection and MD Component, for the descrip-tion of IGN products and components. In principle, allmetadata instances should follow the syntax imposed byMD ISO19115, which represents the ISO19115 standard.The answer to this question can be found in the different in-ference behavior of MD Collection and MD Componentwith respect to the whole-part relation. On one hand, theframe acting as part in a whole-part relation will ob-

Figure 1. A frame-slot-facet representation

tain the value of a slot using one of the following prior-itized ways: by means of the part-if-needed facet dae-mon, by using the own value of the slot, or by inheritingthis value from the whole. The part-if-needed facet dae-mon returns a value obtained as the combination of slotvalues of the part and slot values of the whole. For in-stance, the part-if-needed of datasetTitle in fig. 1 concate-nates the datasetTitle of the whole and the geographicLoca-tionIdentifier of the part. On the other hand, a frame actingas whole will obtain the value of a slot using one of the fol-lowing prioritized ways: by using the own value of the slot,or by means of the whole-if-needed facet daemon. This dae-mon is usually implemented as an aggregated functionapplied over the components of the aggregation. For in-stance, the whole-if-needed daemon of geographicLo-cationBoundingBox in fig. 1 computes the minimumbounding box covering the geographicLocationBounding-Box of the parts.

Although this frame-based solution seems to solve theproblem of metadata duplication, the direct implementationby means of a frame-based language (understood in generalterms as a knowledge-based approach) introduces importantdisadvantages. Firstly, the experience says that knowledgeengineering specific tools have not been exploited enoughin industrial applications [3]. A widely used ontology man-agement tool like Protege has not been tested with a realsystem containing more than 150,000 frames (classes & in-stances). However, a catalog managing collections couldmanage millions of metadata records. And secondly, us-ing this frame-based solution, we need to define new framesnot only for each metadata standard but also for each spe-cial behavior. The most accepted way to exchange meta-data is by means of XML documents, whose syntax of thisXML is enforced by control files in the form of DTDsor XML-Schemas. Given that standardization organizationsusually publish these control files, the question is clear:



”Why must we rewrite this syntax in the form of framesor other concept-based representations?”. And thirdly, oneof the desirable functionalities of the digital library catalogwould be to provide collection statistics, which include his-tograms of spatial coverage or temporal coverage. However,frame-based languages do not usually provide many facili-ties for the work with complex data types.

Given these disadvantages, we have opted for our ownimplementation of knowledge bases that reinforces the roleof relations and makes profit of XML technologies. Onone hand, works like [1] encourage the improvement ofsemantics and inference mechanisms of whole-part rela-tions in object-centered systems. In this case, our knowl-edge base enables the definition of whole-part relationswhere we have transferred the inference mechanisms pre-viously found in the frames (if-needed facets). This way,frames are only focused in representing metadata, not in thebehavior involved in whole-part relations. And on the otherhand, the use of XML technologies increments the flexi-bility of the knowledge base. A knowledge base managingmetadata records (instances) as XML documents and thesyntax (frames) of those documents as XML-Schemas willbe highly scalable and independent of the particular struc-ture of each metadata standard.

Figure 2. The Knowledge Base

Fig. 2 shows the object-oriented model for the imple-mentation of this knowledge base, which uses a relationaldatabase (e.g., Oracle) as storage device and has been pro-grammed in Java. As it can be observed, there are two dif-ferentiate parts in the model: on the left side, the classesthat represent the metadata types and the relation types (theknowledge); and on the right side, the classes that repre-sent the instances of these types.

Regarding the knowledege part of fig 2, theKB MetadataType class represents the syntax of a meta-data schema or standard. It has a syntax attribute which

stores the XML-Schema that defines the syntax of a particu-lar type of metadata. And the KB AggregationRelationTypeclass represents the types of relations established be-tween two metadata types. The inference knowledgeprovided by the relation is specified in the attributes whole-InferredValuesSpecification and partDerivedValuesSpec-ification, which correspond to the whole-if-needed andpart-if-needed daemons of the frame model (fig. 1) respec-tively. The domain type of these attributes is an XSL (eX-tensible Stylesheet Language) document. XSL integratesa transformation language (XSLT) which enables the def-inition of rules to transform an XML-document intoanother XML-document. Thus, it results ideal to spec-ify the inference that will combine or obtain values fromthe XML metadata of whole and part metadata records. Be-sides, this class includes a constraints attribute whichstores the specification of the constraints (if applica-ble) that the components of the collection must observe.

And as concerns the instance part of the model in fig.2, the KB Metadata class represents instances of meta-data which conform to a particular KB MetadataType. Thespecific meta-information of a metadata record is storedin the specificValues, whose domain type is an XMLDoc-ument that should conform to the XML-Schema storedin the syntax attribute of KB MetadataType. Last, theKB AggregationRelation class is used to describe the in-stances of the aggregation relations that are establishedbetween metadata records. This class includes a pat-tern attribute to identify (if it is applicable) the defaultspatial/temporal pattern that follow the components. An ex-ample where these patterns appear would be the case of ge-ographic information collections that have arisen as a resultof the fragmentation of geographic resources into datasetsof manageable size and similar scale. Usually, the spa-tial area covered by the components of these collectionsfollow some type of prefixed division (e.g. the grid estab-lishing the division of tiles at a specific scale or the provinceboundaries) of the space. Knowing this pattern will facili-tate the documentation and organization of the componentsin a particular collection.

With respect to the dynamic behavior of this model,the most important feature is the ability to infer com-plete metadata descriptions, ascending or descendingthrough the aggregation relations. The methods pre-sented in KB Metadata and KB AggregationRelation pro-vide the behavior already sketched in fig. 1 for framefacet daemons. Firstly, the method getCompleteVal-ues act as the if-needed facets but providing the completevalues for all the elements making use of the methods get-ValuesBeingPart and getValuesBeingWhole. Secondly,the method getValuesBeingPart uses, in turn, the meth-ods getPartDerivedValues and getPartInheritedValues to in-fer meta-information for a metadata record acting as part.



On one hand, the getPartInheritedValues method en-ables parts to inherit meta-information contained inmetadata records through the ascending whole-part hier-archy. And on the other hand, the getPartDerivedValuesmethod enables a part to merge its metadata element val-ues with the values obtained from getPartInheritedValuesand according to the functions specified in the partDerived-ValuesSpecification of KB AggregationRelationType. Andthirdly, the method getValuesBeingWhole makes use ofthe method getWholeInferredValues, which obtains inher-ent metadata (metadata derived through the analysis of thecomponents of the aggregation) according to the aggre-gated functions specified in the wholeInferredValuesSpeci-fication of KB AggregationRelationType.

Finally, it must be mentioned that KB AggregationRelationalso offers special statistics of the elements in the collec-tion: the methods getItemCount generates statistics by typeor format; and the methods getSpatialCoverage and get-TemporalCoverage generate the spatial and temporal cov-erage of the components.

4. Conclusions

This paper has presented a solution for the managementof nested collection of resources in Digital Libraries, whichmakes use of XML technologies and knowledge base con-cepts. It proposes the construction of catalog services overthe base of a Metadata Knowledge component. Some of theconcepts already existent in ADL and STARTS approacheshave contributed to the design of this Metadata KnowledgeBase. Similar to ADL and STARTS, the knowledge basemakes a distinction between contextual and inherent meta-data: the goal of metadata records (KB Metadata) describ-ing collections is to store uniquely specific contextual meta-data; and the rest of meta-information, the inherent meta-data, is automatically generated by the methods providedin KB AggregationRelation. But our knowledge base com-ponent makes two additional contributions to previous ap-proaches. Firstly, it introduces the automatic inference forthe records describing the components of the collection,which may inherit meta-information already filled for thecollection. And secondly, it gives support for nested collec-tions. In contrast to ADL where it is made a strict separa-tion between collection level metadata and item level meta-data, our solution enables the description of collections andcomponents according to the same schema.

Although the concepts presented in this paper are exten-sible to any type of Digital Library collections, the contextof Geographic Information has been used to illustrate theproposals. The management of collections and series of re-sources is an important need and the knowledge represen-tation model presented here may provide great benefits forthe construction of metadata cataloguing systems integratedwithin Geolibraries or Spatial Data Infrastructures. First of

all, this system will avoid the redundancy in metadata cre-ation, metadata is only maintained in one place and inher-ited whenever is needed. Secondly, it will facilitate the su-pervision of the metadata creation process by comparing thealready catalogued components with respect to the patternfollowed by these components. For instance, in the case ofa spatial collection, this system will be able to overlap thespatial pattern grid (the division of tiles for a specific scale)and the layer formed by the bounding boxes of the com-ponents already catalogued. Another benefit of this systemwill be the possibility of providing discovery and presenta-tion of metadata records at an aggregated or disaggregatedlevel on user demand. Although not explained here, theknowledge base could deduce whether a initial set of meta-data results are describing components of the same collec-tion, i.e. the knowledge base could find the metadata recordthat subsumes the initial results in the ascending whole-parthierarchy. Finally, the unified description of collections andcomponents can also help to generalize software for accessand visualization of aggregated resources.

Acknowledgments

The basic technology of this work has been partially sup-ported by the Spanish Ministry of Science and Technologythrough the project TIC2003-09365-C02-01 from the Na-tional Plan for Scientific Research, Development and Tech-nology Innovation.

References

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[2] Federal Geographic Data Committee (FGDC). Content Stan-dard for Digital Geospatial Metadata: Extensions for RemoteSensing Metadata. Public review draft, 2002.

[3] K. D. Forbus and J. de Kleer. Building Problem Solvers. MITPress, 1993.

[4] L. Gravano, C.-C. K. Chang, H. Garcıa-Molina, andA. Paepcke. STARTS: Stanford Proposal for Internet Meta-Searching. In Proc. of 1997 ACM SIGMOD Conf., 1997.

[5] T. Gruber. A translation approach to portable ontology spec-ifications. Technical Report KSL 92-71, Knowledge SystemsLaboratory, Standford University, Stanford, CA, 1992.

[6] L. L. Hill, G. Janee, R. Dolin, J. Frew, and M. Larsgaard.Collection metadata solutions for digital library applications.Journal of the American society for Information Science,50(13):1169–1181, 1999.

[7] International Organization for Standardization (ISO). Geo-graphic information - Metadata. ISO 19115:2003, 2003.

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Management of nested collections of resources in Spatial ... · tensible to any type of Digital Library collections. 1. Introduction As regards the cataloguing of geographic resources,