The main objective of the project on hand has been to map two specialized data models to a data model that conforms to the CIDOC Conceptual Reference Model. This data can be shared and processed within multiple contexts. However, this does not answer for the existence of tools that can process the data in a way that is meaningful and contributes to solving a specific scientific problem. One fundamental step towards this objective has been visualizing the data as soon as possible to get a better impression of how Semantic Web tools could deal with the data to be ingested.
1SVG is maintained at the W3C (http:// www.w3.org/ TR/ SVG/). The X3D standard is
defined athttp:// www.web3d.org/ x3d/ specifications/ x3d specification.html.
2A representation of “Berlin” in Disco as HTML can be retrieved athttp:// dbpedia.org/
page/ Berlin, the same resource as RDF/XML at http:// dbpedia.org/ data/ Berlin. The Tabulator browser is available athttp:// www.w3.org/ 2005/ ajar/ tab.
Longwell is a Semantic Web browser using the faceted classification paradigm that was first introduced by the Flamenco Project at Berkeley University [46, 19]. This paradigm assigns a couple of category terms to each term from one or more facets. A facet is a set of categories, for example, archaeological artefacts could be classified under a facet “material” with categories such as “marble” and “bronze.” Unfortunately, Flamenco has its own proprietary data model and mark-up format. Therfore, it will not be able to ingest RDF metadata that is published on the World Wide Web. Longwell is a web-based Semantic Web browser and runs on a standalone basis or within the context of a Java servlet container like Apache Tomcat [22].
Longwell is highly configurable in how it presents data to the user. The Fres- nel3 display vocabulary can be used to change the appearance of items that are
displayed within the browser [61]. Currently most RDF browsers rely on their individual methods to approach two issues: selecting what information of an RDF graph will be displayed and how the data will be formatted. Fresnel can be used to facilitate concept-oriented browsing by explicitly displaying links to related ob- jects. Listing 6.1 shows an abbreviated example of how the Fresnel language was used to tailor the output.
Listing 6.1: Fresnel configuration code in Notation3 (N3).
1 @ p r e f i x f r e s n e l : <h t t p : / /www. w3 . o r g / 2 0 0 4 / 0 9 / f r e s n e l#> . 2 @ p r e f i x r d f : <h t t p : / /www. w3 . o r g /1999/02/22 − r d f −s y n t a x −n s#> . 3 @ p r e f i x f a c e t s : <h t t p : / / s i m i l e . m i t . edu / 2 0 0 6 / 0 1 / o n t o l o g i e s / f r e s n e l −f a c e t s#> . 4 @ p r e f i x crm : <h t t p : / / c i d o c . i c s . f o r t h . g r / r d f s / c i d o c v 4 . 2 . r d f s#> . 5 6 @ p r e f i x : <#> . 7 8 : f a c e t s a f a c e t s : F a c e t S e t ; 9 f a c e t s : t y p e s f a c e t s : a l l T y p e s ; 10 f a c e t s : f a c e t s ( r d f : type ) . 11 12 : c i d o c F a c t e t s r d f : type f a c e t s : F a c e t S e t ; 13 f a c e t s : t y p e s ( crm : E 2 4 P h y s i c a l M a n −Made Thing ) ; 14 f a c e t s : f a c e t s ( 15 crm : P 6 7 B i s r e f e r r e d t o b y 16 crm : P 5 3 F h a s f o r m e r o r c u r r e n t l o c a t i o n 17 crm : P 4 4 F h a s c o n d i t i o n 18 crm : P 4 6 B f o r m s p a r t o f 19 crm : P 1 0 3 F w a s i n t e n d e d f o r 20 crm : P 4 5 F c o n s i s t s o f 21 ) . 22 23 : c i d o c O b j e c t L e n s r d f : type f r e s n e l : L e n s ; 24 f r e s n e l : p u r p o s e f r e s n e l : d e f a u l t L e n s ; 25 f r e s n e l : c l a s s L e n s D o m a i n crm : E 2 4 P h y s i c a l M a n −Made Thing ; 26 f r e s n e l : s h o w P r o p e r t i e s ( 27 crm : P 3 F h a s n o t e 28 crm : P 1 0 3 F w a s i n t e n d e d f o r 29 crm : P 5 3 F h a s f o r m e r o r c u r r e n t l o c a t i o n 30 crm : P 4 4 F h a s c o n d i t i o n 31 crm : P 4 5 F c o n s i s t s o f 32 crm : P 6 7 B i s r e f e r r e d t o b y 33 crm : P 4 6 B f o r m s p a r t o f 34 crm : P 1 3 8 B h a s r e p r e s e n t a t i o n 35 ) ; 36 f r e s n e l : g r o u p : g r . 37 38 : c i d o c O j e c t I m a g e F o r m a t r d f : type f r e s n e l : Format ;
3The term “Fresnel” refers to the French physicist Augustin-Jean Fresnel who constructed a
39 f r e s n e l : p r o p e r t y F o r m a t D o m a i n crm : P 1 3 8 B h a s r e p r e s e n t a t i o n ; 40 f r e s n e l : v a l u e f r e s n e l : i m a g e ; 41 f r e s n e l : l a b e l " I m a g e s " ; 42 f r e s n e l : g r o u p : g r . 43 44 : g r r d f : type f r e s n e l : Group ; 45 f r e s n e l : l a b e l " C I D O C C R M s t a n d a r d g r o u p " ; 46 f r e s n e l : s t y l e s h e e t L i n k <h t t p : / / p e n t h e u s . p e r s e u s . t u f t s . edu / crm . c s s > .
Figures 6.1 and 6.2 exemplify how the Fresnel language can be used to change the appearance of data objects within the browser, including the display of images.
Figure 6.1: The Longwell Semantic Web browser, unconfigured.
Longwell is an example for using the underlying data model to control the user interface component of an application. In this example the CIDOC CRM is used both for internal information representation and for external user interface generation. If the underlying data model will be changed or extended, the graph- ical user interface component will automatically reflect these changes without any additional efforts.
Khurso and Tjoa found out that Longwell, compared to other browsing and visualization tools, is one of the more scalable tools [38]. According to their ex- periments, Longwell is able to handle more than 500,000 triples. Currently about 40 fields and one link to the picture database of Perseus’ 6,000 database records are mapped to RDF/XML. This results in about 401,000 RDF triples, an amount of data that has been indexed within a couple of hours. The data-set could be browsed at good performance. However, for Arachne, ten fields of the main object table with links to geographic entities and bibliographic information have been mapped resulting in about 2,402,000 triples for about 60,000 archaeological ob- jects, 6,000 bibliographic entries, and 5,000 records with place information. This amount of data could not be ingested into the Longwell browser, the ingesting process was stopped after 109 hours of computing time on a Mac Pro (3,0 GHz
Figure 6.2: The Longwell Semantic Web browser, configured with Fresnel. Quad-Core Intel Xeon 5300, 2GB main memory). Performance experiments with a native in-memory store turned out to be promising.
But there are other alternatives that should be explored as well. A larger integration project for archaeological data would easily reach a magnitude of more than 30 million RDF triples. Portwin and Parvatikar state that the Jena API scaled up to 200 million triples during their project [51].4 This amount of triples is enough for a small cultural domain but not enough for huge amounts of data worldwide.
Unfortunately, Longwell does not support any inferencing on the underlying ontology. Even if the CIDOC CRM definitions were ingested together with Perseus’ and Arachne’s metadata, no links from data objects to their defining classes were discovered and indexed. This leads to the fact that Longwell completely ignores the concepts of generalization and inheritance. For example, Longwell does not allow for displaying all persistent physical items and non-material products of human activity by selecting the E71 Man-Made Stuff, the class under which they are subsumed. The user rather has to formulate a concatenated query that includes both classes. However, this prevents from exploiting some of the most fundamental advantages of ontologies and thesauri.
4At “http:// www.mkbergman.com/ ?p=227” M. K. Bergmann states that 250 million
Chapter 7
Conclusion
After evaluating functional requirements of digital scholarship, some building blocks of a future Cyberinfrastructure have been introduced. A distinction has been made that conceptually separates instances from entities. To conduct serious research, scientists need to refer to instances within primary sources to give evidence for their argumentation. They also need to make unambiguous assertions about, for example, historical places and persons. Thus, there is a need for a system that enables scientists referring to specific entities. A complex software architecture including authority-naming services and institutional repositories that build upon Semantic Web concepts could provide the functionality needed. Additionally, stan- dards that facilitate networked knowledge organization systems have been looked at. To better understand different conceptual and physical elements of the overall architecture, a basic mapping workflow has been established, reaching from data extraction, over cleaning and mapping, to visual presentation in the Longwell Se- mantic Web browser. Most current data models cannot instantly deliver the data in a way that can be processed for Semantic Web purposes. Common problems comprise dirty and unstructured data that could not be easily extracted. Addition- ally, many Semantic Web concepts are still not well understood and complicated to implement using state of the art web-server technology.
The Perseus art and archaeology database contains approximately 6,000 data objects. Each object is described as a subset of altogether 102 database fields. Some of these fields are administrative and only used internally so that 94 fields qualify for mapping. Since the database hosts a high diversity of objects rang- ing from coins to buildings, only 34 fields were found to be relevant for all data objects. Therefore, the mapping experiment started with mapping those fields to the CIDOC CRM. Three fields contained structured bibliographic entities that could be easily extracted by trivial pre-processing. Four fields, however, contained mostly unstructured text with valuable information about places and people that have not been extracted. The mapping workflow certainly needs better automation
and options for plugging in data quality and cleaning tools. The compilation of further and better mapping rules as well as pre-processing components will be an iterative and ongoing endeavor. The experience we gained with mapping Perseus’ data will help to better map the more than 100,000 data objects of Arachne. As a test-case the most important fields of three central Arachne tables (objekt, literatur, ort) have also been mapped to the CIDOC CRM.
Some problems with extracting data from both databases originate from fields with an implicit internal structure. For bibliographic information, the structure could be automatically discovered and items extracted. Because of poor data qual- ity, some information had to be dropped and could not be mapped to the CIDOC CRM. The application of tools that can fix common data quality problems would result in a more comprehensive mapping result. A couple of tools are freely avail- able in the public domain and commercial solutions also exist. But most problems require domain specific knowledge and could probably be handled better by spe- cialized software. However, investing in internal data cleaning and re-organization of data models would help with mapping cultural heritage databases to the CIDOC CRM.
Introduction of multilingual record-linkage tools could assist with automatically linking data objects that belong together. Perseus and Arachne have slightly overlapping collections. In this context, digital surrogates that refer to the same entity should be linked. This would result in accumulating multilingual metadata for these objects. Even if cross language information retrieval tools are introduced, all metadata internally should to be available in a certain language, for example, English.
Record-linkage is dependent on entity identification. If two bundles of metadata can be identified as referring to the same entity, the records can be linked as belonging together. The objective has to be not only to identify that a specific string refers to a person or a place, but also to what specific place or person. This will be carried out by assigning a common global identifier. The overall aim will be to automatically linking data objects that conceptually belong together.
Entity-identification will become more powerful if it is done with the assistance of authority naming-services. Hooking text-parsers up to these services could extract information about people and places from full-text descriptions. However, in the course of the project, record-linkage only could be established on a very low level, future research should concentrate on this area. Advanced record-linkage applications seem to be promising for contributing linked Semantic Web data.
For the time being, Perseus’ data has been published for harvesting at http: // athena.perseus.tufts.edu/ collection in three different representations (RDF/XML, HTML and Collection service XML). This data-set should be ingested into institu- tional repository software that is able to handle huge amounts of data. Each data
object should be equipped with a persistent identifier. This could be the Fedora institutional repository software or a simple triple store with another publishing component. Fedora bears the advantage of delivering many data management tools and facilitating long term preservation. For publishing the data to a large audience, Fedora implements the OAI Protocol for Metadata Harvesting. Large repositories that facilitate discovery of RDF data are emerging.1
By the development of new and flexible ways of knowledge representation, historical cultural scientists will be enabled to refer to, access, and manage vast amounts of densely linked data objects as surrogates for existing cultural heritage objects. The most obvious benefit of putting granular data online is providing rapid and economic access not only to documents but to granular metadata and large knowledge organization systems. To action this vision, scientists will need to encode their documentation in a way that can be processed by machines and reused by other scientists. Moreover, the vast amount of material that has been published traditionally, in print or even hand written, should be digitized in a way that contributes to the linked data idea. Named entity identification systems in addition to full-text parsers could be adapted to this task.
Although the Semantic Web is obviously an emerging field, current frameworks and browsers leave many issues unaddressed. Each tool is targeted to a certain display paradigm and provides only limited scalability, being suitable for research in the lab but not for a large production environment. Because Longwell separates data and display it provides a promising paradigm for future research, provided that it will be able to overcome current scalability issues. Frameworks like the Jena API also show promise concerning scalability issues. However, certain communities need to chose a suitable display paradigm that provides useful access to contribute to their research objectives.
All tools that have been described so far could also be applied to publications that exist in digital form either to link archaeological objects and ancient texts to secondary sources or to automatically create data objects in bulk. A fruitful area of research surely will be the development of tools that provide “Intelligent Information Access” for digital libraries. These are “technologies that make use of human knowledge or human-like intelligence to provide effective and efficient access to large, distributed, heterogeneous and multilingual (and at this time mainly [but not only] text-based) information resources and to satisfy users’ information needs [6].”
1http:// pingthesemanticweb.com/ is a service which acts as a concentrator for multiple
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