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2006
The Semantic and Syntactic Model of Metadata
Jian Qin Syracuse University
Javier Calzada Prado Universidad Carlos III de Madrid
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Recommended Citation Qin, J. & Prado, J.C. (2006). The semantic and syntactic model of metadata. In: Alfabetiza??o Digital e Accesso ao Conhecimento (S?rie Comunica??o da Informacao Digital 2006), Vol. 4 (pp. 143-156). Brasilia: Departmento de Ci?ncia da Informa??o e Documenta??o, Universidade de Brasilia.
This Article is brought to you for free and open access by the School of Information Studies (iSchool) at SURFACE. It has been accepted for inclusion in School of Information Studies - Faculty Scholarship by an authorized administrator of SURFACE. For more information, please contact [email protected]. The Semantic and Syntactic Model of Metadata
Jian Qin Javier Calzada Prado School of Information Studies Departamento de Biblioteconomía y Documentación Syracuse University Universidad Carlos III de Madrid 4-187 Center for Sci & Tech Calle Madrid, 126 Syracuse, NY 13244 28903, Getafe (Madrid) USA Spain [email protected] [email protected]
Abstract: The first question is related to the current model of metadata. Early metadata experiments, including the As more information becomes “born digital”, metadata one initiated at OCLC and contributed by librarians creation is increasingly becoming part of the (Jul 1995), used the MAchine Readable Cataloging information creation process. Current metadata (MARC) format to encode the description data for web schemes inherit much of the library cataloging tradition, sites and pages, which is the data-binding format of the which has shown limitations on representing “born 2nd edition of Anglo-American Cataloging Rules digital” type of resources. Through analysis of issues of (AACR2). The metadata schemes developed from metadata schemes and review of metadata research and these experiments are greatly influenced by library projects, the authors propose an ontology-based cataloging practice. Elements in metadata schemes approach to building a modular metadata model in have similar linear structures as those defined in which semantics and syntax may be integrated to suit AACR2. Syntactic structures for these elements are the needs for representing “born digital” resources. The provided in separate specifications, which may deviate authors use an learning object ontology as an example slightly from the element definition due to the need to to demonstrate how the semantics and syntax may be adapt to the syntactic language. Gaps between built into a modular model for metadata. metadata semantic and syntactic structures resulted in
duplicate efforts in binding the same data elements Keywords: with various languages and application programs, Metadata standards; Digital libraries; Ontologies; which leads to widely varied data binding models and Semantic modeling. implementations. If MARC format has successfully
converted the card catalog into machine-readable form 1 Introduction three decades ago, it is very unlikely that metadata Three areas of work are essential for metadata to standards will repeat the history again simply by perform its functions: semantics to define the meaning following the footprint of MARC in the “born-digital” of data, syntax to specify the data binding structure, information environment. and vocabulary to control the language (Duval et al The second question is the amount of semantics 2002). Duval et al maintain that because syntax offered in current metadata standards. Due to the language such as XML is still under development, it is traditional cataloging influence, metadata standards necessary to keep metadata semantics separate from generally contain limited semantics for machine syntax, which has been witnessed during the first processing. On one hand, common semantic elements decade of metadata development. As more information in metadata schemas such as title, author, subject index is “born digital,” metadata creation is increasingly terms, and description are often far from enough when becoming part of the information creation process. This finer metadata representation is needed. This forces fundamental change has a significant implication for developers to expand the metadata semantics with metadata development. The “born-digital” trend has methods and technology suitable in their context, caught the attention of metadata and digital information which results in widely varied practices and duplicate developers. One of the strategies in addressing the efforts. On the other hand, the fast growth of digital challenge is expanding metadata standards by adding information is difficult enough for human catalogers to structural and/or content elements (Becker et al 2003; keep up with even for such limited metadata semantics. Dushay 2002; Kostur 2002). This raises questions in Much of the information about an object has to be left the paradigm of separating semantics and syntax in out of the metadata record. To enrich the semantics in representing information that is created digital. metadata schemes while increase the amount of machine-processable data, a promising solution lies in
1 a new metadata model that will standardize the among elements can only be established at data binding metadata development and provides extensible and (either in form of database tables or XML schemas). powerful semantics and syntax for utilizing the fullest Ontological modeling of metadata takes an object- potential of the “born-digital” information. oriented view of all elements in a metadata scheme and The limitations and future prospective of reorganizes them as concepts, concept properties, metadata standards call for a formal metadata model to instances, and relations. General ontology modeling address the issues related to metadata semantics and related to metadata includes the
2 and categorizing digital objects. Khan et al (2004) Instructional Authoring and Distribution Networks for created a domain-dependent ontology to represent the Europe). context and meaning of audio objects’ content. The A relational database in Microsoft Access was most specific concepts in this ontology were then created to collect data on schemas, elements and considered as metadata. By using automatic context subelements. The following data fields were included extraction techniques, the more general concepts in the in the database: name and URL of digital libraries, ontology were used to categorize audio objects. Khan schemas used in digital libraries and their version, and et al demonstrate how metadata may be generated and elements and subelements that belonged to each of audio selection customized using the ontology model. them. Detailed information was collected about To summarize, element-based expansion is elements and subelements: name given to each tag, common in metadata creation and an easier way to type of element or subelement, description available adopt a metadata standard. One disadvantage, among and semantics embedded. Types of elements or other things, is the limitations in offering finer-grained subelements were defined according to the reference semantics at conceptual level and in establishing schema or standard chosen and declared as such by relationships between related concepts, which can only each digital library or, if necessary, defined as an be established at the implementation stage. Ontological expansion or locally developed element or subelement, modeling as a promising methodology is still being as indicated by the following categories: “DC” (Dublin explored. Experiments with domain-dependent Core), “DC element expansion”, “LOM” (IEEE ontologies have been conducted in metadata extraction Learning Object Metadata), “LOM element expansion”, and information retrieval. However, questions remain “IMS”, “IMS element expansion” and “local element.” on how to construct the metadata model to maximize In addition to element coding, we also followed the the potential of born-digital information objects and to data entry rules below: bring semantics and syntax together to minimize the When tag names were not identical to the ones in implementation efforts. the adopted metadata standard, they were considered as element expansions, even if they 3 Methodology referred to the same concept. Some subelements recurred in several elements or To address the questions raised from literature subelements during database binding. We entered review, we chose to study a number of representative these recurring subelements as “expansions.” metadata schemas to examine their structures and An element or a subelement was considered as vocabularies, rather than conducting a formal survey local when it was developed by the adopting with a scientific sampling method. Our main purpose is schema. to gain insights into the extent to which metadata If a subelement is identical to one of those in DC standards were adopted, where the expansions to these or LOM, it was marked as “DC” or “LOM” (even standards occurred in the adopting schemas, and what if they were found in a locally developed element), semantic and syntactic characteristics existed in the stating the fact that it had not been altered from the schemas and expansions. standard considered.
3.1 Data Collection 3.2 Data Processing We realize that it is impossible to examine all After all data had been collected (resulting in 95 metadata schemas used by all digital libraries. The elements and 311 subelements), we ran several queries selection criteria were based on two considerations: to merge data fields for elements and subelements as whether the digital library has a strong presence of well as categorized both elements and subelements for metadata development and a metadata team, and descriptive statistical processing. whether the metadata schema has its own controlled One important step in data processing is vocabulary and expansions. The six metadata schemas categorizing the elements and subelements. included in our study were chosen from six digital Researchers have categorized metadata with more or libraries that met the two considerations and had less similar groups (Greenberg 2001; Gilliland- separate sites for metadata information: the Digital Swetland, 2000; Lagoze et al 1996). Common Library of Theses and Dissertations (NDLTD), the groupings include administrative data, Digital Library for Earth System Education (DLESE), descriptive/discovery data, intellectual content data, the Alexandria Geospatial Digital Library (ADL), the technical data, and rights data. Based on previous Gateway to Educational Materials (GEM), MERLOT research on metadata groupings, we categorized all (Multimedia Educational Resource for Learning and elements in our data set into four groups: Online Teaching) and ARIADNE (Alliance of Remote
3 (1) Administration: This group includes elements shows the Relation elements counted for 42% of all that are mainly used for managing and tracking descriptive subelements; Title for 11%; URL or web metadata. It includes time and cataloging agency location of the resource 11%; Description of the related data. resource 8%; Version of the resource 7%; and the rest (2) Description: Any element describing a digital counted for only small fractions. object’s discovery or access characteristics is categorized into this group. Included in this group Table 1. Number of elements by element type and are content, descriptive, time coverage, scientific schema data, and rights. They mainly perform user- oriented tasks. Number of elements Metadata (3) Education: Most schemas included in this study by element type scheme contain a number of educational elements, but DC IMS LOM Local Total little research has been done in examining the ADL 5 56 61 elements in this group. It is also a user-oriented group. ADN 71 19 90 (4) Technical data: This group contains the elements ARIADNE 29 21 50 dealing with the physical characteristics and ETDMS 18 4 22 system requirements for using the objects. GEM 60 9 69 Elements in this group have the greatest potential MERLOT 8 11 19 to be extracted automatically by computer Total 78 76 37 120 311 programs. Data categorization was conducted by both researchers in parallel. The categorization results were 180 Time_Cont compared and differences were discussed and cleaned Time_Desc 160 in order to ensure the accuracy and consistency. Rights 140 Creator 3.3 Analysis 120 Scidata 100 Content Once the data set was ready, we ran frequency and Time_Aud cross-tabulation analysis: elements and subelements by 80 schema, type of element (DC, LOM, or local elements), 60 Descriptive Admin and category. The findings are presented in Section 4 40 Education and discussion of results and modelling in Section 5. 20 Audience 0
4 Findings N C C H S U C MI E D D The six metadata schemes we studied had 95 first D E TE A level elements. The frequency analysis of these elements reveals that only a handful of elements were Figure 1. Subelements by category identical: title, description, rights, and technical. The rest of elements had one occurrence each, though many Type Other of them were semantically similar or identical. We 4% 8% further analyzed their corresponding 311 subelements Language 4% by dividing them into four types as shown in Table 1. Identifier Relation 5% While each of the metadata schemes adopted at least 42% one standard, they all created a large number of local Version elements, counting slightly over one-third of the total. 7%
Description 4.1 Categories of Metadata Elements 8% To find out the distribution of metadata element Title URL categories, we divided all subelements into four main 11% groups: administration, description, educational, and 11% technical (Figure 1). Figure 2. Details of description subelements. Within each group, elements were further categorized based on their semantics. As we can see in The description group contained a large number of Figure 1, the largest group was Description. Figure 2 elements for content, creator and scientific data.
4 Content elements had to do primarily with four aspects, Table 2. Content elements by semantic category by frequency order: the subject of the resource, the abstract of its content, its discipline and keywords Semantic category Number of elements (Table 2). Subelements related to the creator of the digital object are straightforward—they deal with their Subject 8 names and roles. Those subelements that dealt with Abstract 4 Discipline 4 scientific data belonged to ADL and DLESE, Keywords 3 containing specific data to attend the specific needs for Other (1 occurrence each) 6 representing coordinates, elevation, projection, etc. in the digital library collections. Total 25 Table 3. Subelement types by semantic category
Semantic Number of Subelements by Subelement type No Total category DC DC exp IMS IMS exp LOM LOM exp Local subelement ADMIN 0 1 0 4 16 0 2 20 43 AUDIENCE 1 0 3 2 5 0 0 0 12 CONTENT 3 2 7 1 3 0 0 15 31 CREATOR 1 3 3 1 2 0 2 5 17 DESC 10 18 21 2 10 3 8 17 89 EDUCATION 0 0 0 3 3 7 5 3 20 RIGHTS 2 1 0 1 2 0 1 3 10 SCIDATA 0 0 4 3 1 3 0 22 33 TECHNICAL 2 2 5 6 8 1 3 1 28 TIME 0 2 6 1 2 2 4 11 28 Total 19 29 49 24 52 16 25 97 311
Table 3 shows the distribution of subelements by administrative subelements beginning with a Type subelement type and semantic category. An obvious element include following: pattern is that expanding on standard metadata elements is common practice and local elements were Type-Email-Address used in almost all categories. Most of the expansions in Type-Larger Organization DC concentrated on descriptive elements, while spread Type-Notes across categories relatively evenly in IMS and LOM. It Type-Operators is worth mentioning that local elements counted for Type-Metadata-Mapping almost one-third in the total, among which Type-Postal-Address administration and scientific data categories were at the Type-Postal-Address-PO Box top. It is surprising that only 41 LOM elements and Type-Postal-Address-City subelements were adopted by the six metadata schemes while the standard has over 80 elements and Element names in the content category incurred subelements in total. wide variations in terms of both semantics and Category wise, expansions by way of local linguistic forms (Table 5). The content subelements fell elements concentrated mainly in administration, into two categories: those for topical terms, which used content, description, and scientific data. the thesaurus construction approach, and those for time covered by the digital object content, which were 4.2 Vocabulary Use in Local Elements ambiguous in their meaning and use. Local subelements in the description category A further examination was conducted to analyze added more details to the common ones. For example, the vocabulary use in subelements in the four largest “hierarchy” was used in two elements to describe the categories. We found an interesting phenomenon item type and “Event name tied to coordinates” to link across all the local expansions, i.e., an element “Type” events and geographic areas. Scientific data categories used in XML data binding was mixed among the contained many subelements particular to geospatial semantic elements, which has completely different data. semantics from the Type element in Dublin Core. This semantic- and syntactic-neutral element often hints a Table 5. Local subelements in the content category user-defined data type in the XML schema. The Body or Planet Simple Time Period
5 Discipline Spatial-Coverage One implication from the data analysis is that, as Level Subdiscipline technology evolves and digital information grows in Main Concept Temporal-Coverage both volume and complexity, we need to reexamine the principles and methods for metadata development. Main Concept Synonyms Time AD
Name Time BC 5 Discussion Named Time and Period Time Relative In previous sections, we discussed the reasons why Other Concepts Type we need to reexamine the metadata principles and what issues need to be addressed. The focal point of 4.3 Educational Elements discussion falls onto what metadata models would be Since most of the metadata schemes included in more extensible, scalable, and effective and more this study are created for educational digital resources, fundamentally, what underlying philosophy supports we conducted an analysis of the details in educational such metadata models. Based on the literature review elements. Figure 3 shows the distribution of elements and the analysis of data as well as previous research in both educational and audience categories. In 32 (Qin 2003; Qin 2004b), we propose an ontology-based subelements, the audience elements counted for the metadata model that specifies concepts, properties, and largest number. “Type” here belongs to data binding as relationships involved in metadata schemas by using an discussed before, hence is not a real semantic element. object-oriented approach. Compared to other categories that had large number of local expansions, the educational elements were 5.1 Underlying Philosophy of Modeling relatively poorly developed. The vocabulary used to Metadata is used for three main purposes: reuse, label some of the elements was not immediately clear retrieval, and tracking (Rockley 2003). Reuse has two (e.g., Didactical context, Semantic density, meanings—the element definition reuse (e.g., address Granularity), while others (Interactivity level, elements can be defined once and reused in publisher, Interactivity type, Grouping) require intensive human creator, and contributor elements) and the data reuse judgment. (e.g., address for the same author who created several digital objects). Metadata modeling must facilitate
Content reuse in both senses. Objective 1( 3%) Assessme Retrieval metadata is perhaps the oldest arena in 1( 3%) nt 1(3%) metadata in its broadest sense (thus including Duration bibliographic data in the traditional sense). 2( 6%) Conventional retrieval elements such as author, title, Context and keywords still play a vital role in resource 3( 9%) Audience discovery, but the way they are constructed should 9(28%) enable local expansions in a consistent manner to avoid Interactivity wild variations in semantics and syntax. This is the 3(9%) basis for enabling multiple-database searching and Type 4(13%) reducing duplicate implementation efforts. Compliance Digital objects often need information for tracking 4(13%) Pedagogy who created or submitted the object and/or metadata 4(13%) and when it was created or submitted. The large number of time-related elements in our survey Figure 3. Detail of educational subelements demonstrates the importance of such metadata elements. Tracking digital objects in large repositories 4.4 Summary of Findings may require use of tracking elements combined with The findings from our survey data reveal at least other types of elements to narrow the search. three important facts: One thing that becomes clearer in the past decade 1) Metadata standards provide limited semantics of metadata activities is that developing access to and have to be expanded to meet local needs; digital objects can not simply copy the model from 2) Problems exist in local expansions in both AACR2 and MARC. A more flexible, powerful model semantic consistency and explicitness; must be developed to accommodate the characteristics 3) Metadata binding with XML brings in of digital objects and the needs for using these digital semantic and syntactic neutral elements as a resources in non-traditional ways. As the World Wide method for bridging reusable or user-defined Web Consortium (W3C) phases out the metadata data types. activities into Web Ontology, the metadata modeling
6 discussion can not come at a more appropriate time. properties of term, synonym, and related term, which The ontological, object-oriented approach to modeling are used as the data model to capture instances for the metadata would also be in line with the Web Ontology classes. The concept Learning object, for example, has development at W3C. direct instances as shown in the second column on the left. The instance Figure uses the word “Figure” as the 5.2 The Model preferred term, which has synonyms such as Illustration, interactive illustration, diagram illustration, Metadata elements need to have an abstract model to photo illustration, chart, etc. and related terms such as consistently represent the semantics and syntax. figure title, figure type, and figure content. Their Following the paradigm of RDF, we propose an relationships may be expressed as: abstract model that is simple and conforms with the
RDF formal model while maintains scalability and Learning object is Concept extensibility for metadata schemes. The diagram in Which has property of { Figure 4 suggests that elements in a metadata scheme Structure Term, are concepts and have properties and instances Structure Synonym, (properties also have instances). Concepts (or classes) Structure Term Related}; form the structure of a domain in which semantics, Figure is Instance of Learning object syntax, and properties are specified. While semantics Which has { refers to the meaning of an element and syntax to the Structure Term {Figure}, encoding format, properties serve as a data model to Structure Synonym { capture instances and define constraints of concepts. Illustration, Instances contain vocabulary, both controlled and free- Interactive illustration, text, for elements and element values with a consistent …}, syntax. Structure Term Related { Figure title, …};
5.3 Modules One of the main drawbacks in most metadata schemas is a lack of modular structure for the elements. It is common that dozens of elements are stuffed in a metadata schema as a very long list. Such a single list style of metadata elements makes metadata schema maintenance and implementation inconvenient and
Figure 4. An abstract model for semantic and complicated. syntactic metadata A modular data model is usually considered as more extensible and flexible because it can be managed The main advantage of this model is that no matter separately and tested independently or combined as an how the domain concepts are structured, they will integrated whole (Luna 1992). The abstract model we always be represented by a tuple of concept, property, proposed allows metadata elements to be built in a and instances. This model may be used for any modular style while still maintains structural and metadata scheme to define metadata structure and syntactic consistency. In this model, a concept or vocabulary. We will use a learning object ontology several concepts can be created as a simple module. (Qin 2004a) as an example to demonstrate what this Several modules may also be combined to form a new, model means. complex module while the properties remain the same The learning object ontology created by Qin for elements in these modules. In the implementation (2004a) contains a number of main concepts: learning stage, an adopter may choose to maintain a shallow objective, learning object, learning content, learning metadata model in which individual modules are context, learning model, learning practice, and jointed together by an overarching schema at run time, assessment. These concepts form the knowledge or the adopter may choose to joint the modules before structure for the learning related content in learning applications are developed. objects. Figure 5 presents a portion of the concept classes in the ontology and direct instances for the Learning object concept. Each class in the ontology has
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Figure 5. Concept classes and the direct instances for the Learning object concept in the learning object ontology (http://web.syr.edu/~jqin/LO/LOV2/) Metadata modules include reusable modules and metadata schemas. As more and digital information functional modules. objects are created with structural elements, metadata Reusable modules: The findings from our survey schemes will need to be extended to include such show that reusable elements occurred mostly in role elements in addition to metadata. With this vision of and syntactic elements, such as name, address, email, future metadata development, it becomes critical to and the elements in data binding. Another category that have a simple abstract model for dealing with the can be defined as reusable module is content elements. complexity, scalability, and interoperability of Regardless of element names, all elements in the metadata schemes. content category may use a model of preferred name, Based on the ontology we created, our future synonym, and related terms. Reusable modules are research will continue the work on metadata modeling. similar to “user-defined” data types in object-oriented This will include developing the modular semantic data modeling. model with various functions and reusable data types, Functional modules: These modules will perform as well as the syntactic model that will provide retrieval, tracking, and administration functions. They effective and standard data binding formats. may overlap with reusable modules. References 6 Conclusion Aslan, G. and D. McLeod. (1999). Semantic In this paper, we analyzed 311 metadata elements heterogeneity resolution in federated databases by in six metadata schemas based on their type and metadata implantation and stepwise evolution. The category. Our findings show that large numbers of VLDB Journal, 8: 120-132. local expansions were made based on metadata Becker, J., C. Brelage, K. Klose, and M. Thygs. (2003). standards but semantic inconsistencies and ambiguities Conceptual modeling of semantic navigation existed across local expansions among the schemas. As structures: the MoSeNa-approach. In: WIDM’03, we pointed out in the Introduction section, much of 118-125. New York: ACM Press. these problems are related to the underlying philosophy Duval, E., W. Hodgins, S. Sutton, and S. L. (2002). of metadata development, which is influenced Weibel. Metadata principles and practicalities. 8(4): primarily by traditional library cataloging. We http://www.dlib.org/dlib/april02/weibel/04weibel. proposed an abstract model of the concept, property, html and instance tuple and explained the underlying Dushay, N. (2002). Localizing experience of digital philosophy of the model. Using the learning object content via structural metadata. In: JCDL’02, 244- ontology as an example, we also demonstrated what the 252. New York: ACM Press. model means and how it works for building a modular, Gilliland-Swetland, A. J. (2000). Defining metadata. In: extensible, and ontology-based metadata model. M. Baca (Ed.), Introduction to metadata: Pathways The main contribution of this paper is that it raises to digital information. Los Angeles, CA: Gettry questions on the metadata development direction and Information Institute. Also available: proposes an ontology-based approach that is simple yet http://www.getty.edut/gri/standard/intrometadata/2 allows for extensibility and consistency in developing _articles/index.htm.
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