Revisit: Reuse of Structured Data: Semantics, Linkage, and Realization (2)

(continue from part I) / Library and Information Science, 43.1 (2017): 7-46. / [[中文]]

RESEARCH HIGHLIGHTS: 

# An old record is not a data but now defined as a new semantic dataset. 
  i.e. its triples, graphs, links, file formats ...
  i.e. its revised, vocabulary encoded versions ...
  ex. data:d2148340 a dcat:dataset. #files:json-ld, ttl, XML

# A new method to curate, publish & visualize LOD graphs via CKAN portal. 
  i.e. two models for one dataset published in two views.
  ex. data:d2148340 a dcat:dataset.   # Dublin Core @schema1
  ex. data:d2148340 a data:Refined. # more semantics@schema2

# Validation & Reproducibility: Provenance and Contexts are in details. 

Practices

Example: data:d2148340 (click to enlarge)

We then make use of structured records (XML files) from a digital archive catalogue, and convert the records into semantically rich and interlinked resources on the Web. This is realized as a unified Linked Data catalogue to several digital archive collections. Our work results in a LOD catalogue (data.odw.tw) available to the public at the website . The following five parts are involved in realizing this website. 

A catalogue record, about a species of Pleione Formosana (data:d2148340), is used throughout in the paper as an example to demonstrate the way we model, convert, and represent the semantics of a structured record.

R4R Ontology (click to enlarge)

Part 1: Exploring data reuse relations in a shared context -- We review our previous research about the Relation for Reuse Ontology (R4R). In particular, we provide mechanisms for reusing article, data, and code with some flexibility of encoding provenance and license information.

Part 2: Comparing two different data conversion approaches to providing LOD for an archive catalogue -- We show two different scenarios: (1) The LOD catalogue is converted directly from a relational database, and (2) the LOD catalogue is generated from a series of format conversions --- from XML to CSV, and then to RDF. 

KB links Example (click to enlarge)

Part 3: Data profiling, cleaning and mapping -- We demonstrate format conversion processes, and we discuss the pros and cons of various ways in handling broken links in source datasets. In addition, we mapped and linked catalogue records to three external knowledge bases: GeoNames, Wikidata, and Encyclopedia of Life.  

Part 4: Using CKAN as a Linked Data platform -- We briefly introduce CKAN, an open source web-based data portal software package for curating and publishing datasets. CKAN provides data preview, search, and discovery, especially with regard to geospatial datasets. We built several extensions to CKAN in order to deposit, publish, browse, and search Linked Data. Various Linked Data representations of a catalogue record --- Turtle, RDF/XML, and JSON-LD --- can all be downloaded and reused.

Part 5: Designing an ontology for data representation and reuse -- We design an ontology voc4odw which includes the following 3 modules:

(1) The Core Model. It is comprise of a data model and a conceptual model. 

The data model represents key data structure and relation. It is a framework to illustrate data source,derivation, and provenance.

The voc4odw Data Model (click)

The conceptual model incorporates Simple Knowledge Organization System (SKOS); it also connects to key event concepts. The conceptual model allows for data contextualization using common and domain knowledge vocabularies.

(2) The Curation Model. It is responsible for disclosing the identification, classification, and publication of structured records at a curation platform, such as the classification of themes, the assignment of data identifiers, and the publication of datasets.

(3) A vocabulary voaf:Vocabulary. It is defined as "A vocabulary used in the Linked Data cloud", from the Vocabulary of a Friend . This module is to relate the Core Model to external common vocabularies. Some hierarchy relations between different external vocabularies can be traced with this vocabulary.

voc4odw ontology
Common Knowledge
Prefix	Namespace	Description
cc	http://creativecommons.org/ns#	1.       Creative Commons Rights Expression Language
csvw	http://www.w3.org/ns/csvw#	2.       W3C CSVW Namespace Vocabulary Terms
dc	http://purl.org/dc/elements/1.1/	3.       DC 15 (Dublin Core Metadata Element Set)
dcat	http://www.w3.org/ns/dcat#	4.       W3C Data Catalog Vocabulary
dct	http://purl.org/dc/terms/	5.       DCMI Metadata Terms
dctype	http://purl.org/dc/dcmitype/	6.       DCMI Type Vocabulary
event	http://purl.org/NET/c4dm/event.owl#	7.       Event Ontology
foaf	http://xmlns.com/foaf/0.1/	8.       FOAF Vocabulary Specification
geo	http://www.w3.org/2003/01/geo/wgs84_pos#	9.       W3C  WGS84 Geo Positioning: an RDF vocabulary
gn	http://www.geonames.org/ontology#	10.     GeoNames Ontology
gns	http://sws.geonames.org/	11.     GeoNames Entity
lcsh	http://id.loc.gov/authorities/subjects	12.     Library of Congress Subject Headings
org	http://www.w3.org/ns/org#	13.     W3C Organization Ontology
prov	http://www.w3.org/ns/prov#	14.     W3C Provenance Ontology (PROV)
r4r	http://guava.iis.sinica.edu.tw/r4r/	15.     Relations for Reusing Ontology (r4r)
schema	http://schema.org/	16.     Schema.org
skos	http://www.w3.org/2004/02/skos/core#	17.     W3C Simple Knowledge Organization System (SKOS)
time	http://www.w3.org/2006/time#	18.     W3C  Time Ontology
voaf	http://purl.org/vocommons/voaf#	19.     Vocabulary of a Friend (VOAF)
wde	http://www.wikidata.org/entity/	20.     Wikidata Entity
Domain Knowledge
aat	http://vocab.getty.edu/aat/	1.       Art and Architecture Thesaurus
dwc	http://rs.tdwg.org/dwc/terms/	2.       Darwin Core Terms
dwciri	http://rs.tdwg.org/dwc/iri/	3.       Darwin Core terms
eol	http://eol.org/pages/	4.       The Encyclopaedia of Life (EOL)
txn	http://lod.taxonconcept.org/ontology/txn.owl#	5.       Taxon Concept OWL Ontology
Local Namespace
voc	http://voc.odw.tw/ontology#	1.      Ontology for ODWeb (voc4odw)
agent	http://data.odw.tw/agent/	2.      Organization/Agent Entity in ODW
article	http://data.odw.tw/article/	3.      Textual Description with rdf:type r4r:Article in ODW
code	http://data.odw.tw/code/	4.      Code Description with rdf:type r4r:Code in ODW
data	http://data.odw.tw/record/	5.      Linked Data for ODWeb
evt84	http://data.odw.tw/event/	6.      Event Entity in ODW
project	http://data.odw.tw/project/	7.      Project Entity in ODW
r1 (n)	http://data.odw.tw/r1/   (r2, r3…)	8.      Refined Version(s) of ODW Entity
refined	http://data.odw.tw/refined/	9.      Directory of the Refined Versions
catdat	http://catalog.digitalarchives.tw/	10.    Union Catalog of Digital Archives Taiwan

Introduction: Metadata as Linked Data for Research Data Repositories

中文版

 “Every man has his own cosmology and who can say that his own is right.” said by Einstein. This is also true when we come to understand data semantics that one data may be differently interpreted by different data creators, curators and re-users. Then, how do we build a better research data repository?

We start with the point made by Willis, C., Greenberg, J., & White, H. (2012) that the metadata of research data increases the access to and reuse of the data. At the same time , Stanford, Harvard, and Cornell also believe the use of linked data technologies is a promising method to gather contextual information about research resources.

As a result, we look for for inspiration tools that can meet the urgent needs of innovative solutions providing feature-rich services for helping data publishing such as visualization, validation & reuse in different applications by research repositories (Assante, et.al, 2016). The CKAN (Comprehensive Knowledge Archive Network) as a major solution that makes linked metadata available, citable, and validated becomes our first choice. 

PDF 

For a general overview, our current results at data.odw.tw include:

1. An Use Case for Curation, Publication & Reuse of Metadata as Linked Data.

• 843,309 CC licensed metadata records of 14 domains reused from the Union Catalog of Digital Archives Taiwan.
• 44,806,400 triples (Linked Data) encoded with Dublin Core 15 Elements and Provenance Information.
• 25,913,304 triples from 832,803 records semantically refined with spatial & temporal normalization, mapping, and linking with domain knowledges (external vocabularies, ontologies. and knowledges bases).
• 14 domains include Archaeology,  Architecture, Archives, Artifacts, Biology, Geology, Manuscript, Multimedia, NewsMedia, PaintCal , RareBook, ResearchReuse, StoneRub.
• 80 projects and 74 agents associated with metadata records are curated by their linked data formats and Wikidata ID: they have roles in NGO (2), Museum (5), Library (2), Government (9), Archive (1) and Academia (55).

2. A New Method to Manage Data for General-use & Discipline-specific Repositories.

• For Open Science: using the CKAN (Comprehensive Knowledge Archive Network) as a major solution that makes linked metadata available, citable, and validated.
• Availability: data shared with multiple formats, CSV, XML, Turtle, RDF/XML, JSON-LD, consumed both by human & machine.
• Validation and Reproducibility: each data encoded with provenance in details while at the same time a complete mechanism for publishing article, data and code is designed and implemented.
• A flexible and adaptable ontology for describing different data context (common knowledge or domain knowledge), event concepts (people, place, time) and objects collected by meaningful groups of different vocabularies is provided .
• Data Visualization is enhanced and integrated through spatial and temporal mapping, filtering and linking system design.   

3. Data Semantically Enriched with Vocabularies and Knowledge Bases via Adaptable Mechanisms.

• 18 international vocabularies used  for modeling common knowledge, and 5 domain specific vocabularies for place, time, art and humanity, or biology are applied.  3 knowledge bases like GeoNames, Wikidata, and Encyclopedia of Life are mapped and linked. 
• The use of SPARQL language and endpoints  provide data analytic semantic queries both in local and external. In addition, data from the RDF triplestore can be easily used in 3rd-party applications.
• Multiple DataClean Versions Mechanism:  we treat data cleaning as a kind of interpretation. Refined Versions (R Versions i.e. r1, r2, r3… ) provide different contexts to different needs of users. 
• Multiple LinkedKnowledge Bases Mechanism:  more knowledge bases like DBpedia, WordCat, or LinkedGeoData can be linked in future via different R Versions without sacrificing  the  integrality of the original Version, encoded with DC 15 .
• Multiple SemanticStructure Versions Mechanism:  different  interpretations results from the use of different vocabularies .  Co-exists of multiple R Versions  with different vocabularies or transforming vocabularies via SPARQL are  solutions. 

Reference:

• Douglas, A. Vibert. "Forty minutes with Einstein." Journal of the Royal Astronomical Society of Canada 50 (1956): 99. P.100
• Assante, M., Candela, L., Castelli, D., & Tani, A. (2016). Are scientific data repositories coping with research data publishing?. Data Science Journal, 15.. DOI: http://doi.org/10.5334/dsj-2016-006
• Willis, C., Greenberg, J., & White, H. (2012). Analysis and synthesis of metadata goals for scientific data. Journal of the American Society for Information Science and Technology, 63(8), 1505-1520.
• Cheng-Jen Lee, Andrea Wei-Ching Huang, Tyng-Ruey Chuang (2017) Metadata as linked data for research data repositories, International Symposium on Grids & Clouds (ISGC) 2017
• 黃韋菁， 李承錱， 莊庭瑞 (2017) 結構資料的再次使用：語意、連結與實作, 圖書館學與資訊科學, 第 43 卷 第 1 期 2017 年 4月

Citation Information: Andrea Wei-Ching Huang (2017) Introduction: Metadata as Linked Data for Research Data Repositories. URL: https://andrea-index.blogspot.com/2017/03/metadata-as-linked-data-for-research.html

ArticleRead (19):GEON: Making sense of the myriad resources, researchers and concepts that comprise a geoscience cyberinfrastructure

GEON: Making sense of the myriad resources, researchers and concepts that comprise a geoscience cyberinfrastructure, By Mark Gahegan, Junyan Luo, Stephen D. Weaver, William Pike, Tawan Banchuen, in Computers & Geosciences, Vol. 35, Issue 4, 836-854, (Apr. 2009)

There are two main considerations for exploring the geoscientific meaning of e-resources: the top-down defined domain ontology and conventional metadata, and the bottom-up emergent meaning carried in how the resource are used by users (epistemology: which can be captured in workflows, in provenance meta-data and even in the interactions between people.).

The primary argument of this article is that whether ontological or epistemological, no single one of these web threads is sufficient to carry the essence of meaning. A review of ontology base on this article is made in a SWOT analysis in the figure below, where current engineering methods are considered along with those of the data integration (the Strength table).

Using a case study of a knowledge portal GEON to illustrate their main arguments, problems of accessing to e-resources are:

(1) large resource
(2) dynamic nature of catalogs
(3) varieties of search strategies of user needs
(4) meanings of resource

Suggested solutions to these difficulties include:

(1)adopting a visualize-on-demand strategy, and visualizing multiple perspectives which highlight connections or overlaps among the e-resources,
(2) classifying resources into 4 categories and 18 subcategories, and translating resource descriptions into RDF triples.

Problems (3) and (4) are tackled together by augmenting ontologies. The authors augment ontologies by adding situational knowledge initiated from:

(i) meaning resides in a nexus of interactions (Whitehead, 1929-1997), thus a knowledge nexus can support multiple strands of meaning.

(ii) semiotics that according to different subject of interest, nodes can change their semiotic role in the nexus (Sowa, 1999-2002).

In short, the authors add use-cases, provenance data, social networks and workflows, to ontology, through the use of Perspectives(global and local), as a pragmatic aspect to understand meaning and definition of e-resource. In particular, perspective filters, defined against an OWL model, facilitate examination of a subset of connections within a complex concept space in a manner that suits thematic exploration.

p.s. GEON is an open collaborative project started in 2002 funded under the NSF Information Technology Research (ITR) program . The aim is to develop CyberInfrastructure, a vision in the U.S. while e-Science is in the Europe, that the need of a comprehensive infrastructure to capitalize on dramatic advances in information technology in support of data sharing and integration.

ArticleRead (16): Towards a semantics-based approach in the development of geographic portals

Towards a semantics-based approach in the development of geographic portals, By Nikolaos Athanasis, Kostas Kalabokidis, Michail Vaitis, Nikolaos Soulakellis,In Computers & Geosciences, 35(2009)301–308

Athanasis et al. (2009) have proposed and implemented a geoportal with the aim of providing a methodology for geospatial information discovery with a new approach responsive to what we understand to be significant issues of the Semantic Web on RDF and ontology-based metadata.

By taking advantages of multiple ontology design, they advocate the ontology-based metadata organization into three special context (three schemas: content type, application domain, and the ISO 19115 metadata standard) to enhance users’ navigation techniques in the geoportal interface as a solution to response to the keyword search problem.

In addition to the difficulty of appropriate search criteria they have identified, problems such as semantic heterogeneity (cognitive and naming heterogeneity in particular), the limitation of expressiveness of queries, or the lack/ discrepancy of geospatial metadata standard at the implementation stage are discussed.

Their solutions to these difficulties by focusing on the relationship among geoportals, users, and information providers in a geoportal infrastructure are described as follow:


(1) propose a RDF-based metadata organisation method using multiple ontologies.

(2) design a interface for information providers to describe characters of new resources, characterise their possible relationships between related resources, and classify them based on the build-in RDF classes of metadata schemas.

(3) auto-translate the submitted data into RDF metadata descriptions stored in PostgreSQL, and maintained by the ICS-FORTH RDFSuite (Alexaki et al.,2001) which enables the validation, storage, and querying of the RDF metadata (both schemas and data descriptions).

(4) use RDF Query Language (RQL) based on a formal graph model, and which are executed in a SQL-like “select-from-where” pattern.

Final comments: the implementation is still a prototype supported by the EU RTD project and the website is : http://incendi.geo.aegean.gr/en/incendi_aggl.html . The future work of this team is to develop OWL.