Ceri Binding

Semantic Interoperability in Archaeological Datasets: Data Mapping and Extraction Via the CIDOC CRM

Findings from a data mapping and extraction exercise undertaken as part of the STAR project are described and related to recent work in the area. The exercise was undertaken in conjunction with English Heritage and encompassed five differently structured relational databases containing various results of archaeological excavations. The aim of the exercise was to demonstrate the potential benefits in cross searching data expressed as RDF and conforming to a common overarching conceptual data structure schema - the English Heritage Centre for Archaeology ontological model (CRM-EH), an extension of the CIDOC Conceptual Reference Model (CRM). A semi-automatic mapping/extraction tool proved an essential component. The viability of the approach is demonstrated by web services and a client application on an integrated data and concept network.

Query expansion via conceptual distance in thesaurus indexed collections

Purpose – The purpose of this paper is to explore query expansion via conceptual distance in thesaurus indexed collectionsDesign/methodology/approach – An extract of the National Museum of Science and Industrys collections database, indexed with the Getty Art and Architecture Thesaurus (AAT), was the dataset for the research. The system architecture and algorithms for semantic closeness and the matching function are outlined. Standalone and web interfaces are described and formative qualitative user studies are discussed. One user session is discussed in detail, together with a scenario based on a related public inquiry. Findings are set in context of the literature on thesaurus‐based query expansion. This paper discusses the potential of query expansion techniques using the semantic relationships in a faceted thesaurus.Findings – Thesaurus‐assisted retrieval systems have potential for multi‐concept descriptors, permitting very precise queries and indexing. However, indexer and searcher may differ in ter...

Implementing archaeological time periods using CIDOC CRM and SKOS

Within the archaeology domain, datasets frequently refer to time periods using a variety of textual or numeric formats. Traditionally controlled vocabularies of time periods have used classification notation and the collocation of terms in the printed form to represent and convey tacit information about the relative order of concepts. The emergence of the semantic web entails encoding this knowledge into machine readable forms, and so the meaning of this informal ordering arrangement can be lost. Conversion of controlled vocabularies to Simple Knowledge Organisation System (SKOS) format provides a formal basis for semantic web indexing but does not facilitate chronological inference - as thesaurus relationship types are an inappropriate mechanism to fully describe temporal relationships. This becomes an issue in archaeological data where periods are often described in terms of (e.g.) named monarchs or emperors, without additional information concerning relative chronological context. An exercise in supplementing existing controlled vocabularies of time period concepts with dates and temporal relationships was undertaken as part of the Semantic Technologies for Archaeological Resources (STAR) project. The general aim of the STAR project is to demonstrate the potential benefits in cross searching archaeological data conforming to a common overarching conceptual data structure schema - the CIDOC Conceptual Reference Model (CRM). This paper gives an overview of STAR applications and services and goes on to particularly focus on issues concerning the extraction and representation of time period information.

Compound descriptors in context: a matching function for classifications and thesauri

There are many advantages for Digital Libraries in indexing with classifications or thesauri, but some current disincentive in the lack of flexible retrieval tools that deal with compound descriptors. This paper discusses a matching function for compound descriptors, or multi-concept subject headings, that does not rely on exact matching but incorporates term expansion via thesaurus semantic relationships to produce ranked results that take account of missing and partially matching terms. The matching function is based on a measure of semantic closeness between terms, which has the potential to help with recall problems. The work reported is part of the ongoing FACET project in collaboration with the National Museum of Science and Industry and its collections database. The architecture of the prototype system and its interface are outlined. The matching problem for compound descriptors is reviewed and the FACET implementation described. Results are discussed from scenarios using the faceted Getty Art and Architecture Thesaurus. We argue that automatic traversal of thesaurus relationships can augment the users browsing possibilities. The techniques can be applied both to unstructured multi-concept subject headings and potentially to more syntactically structured strings. The notion of a focus term is used by the matching function to model AAT modified descriptors (noun phrases). The relevance of the approach to precoordinated indexing and matching faceted strings is discussed.

Improving interoperability using vocabulary linked data

The concept of Linked Data has been an emerging theme within the computing and digital heritage areas in recent years. The growth and scale of Linked Data has underlined the need for greater commonality in concept referencing, to avoid local redefinition and duplication of reference resources. Achieving domain-wide agreement on common vocabularies would be an unreasonable expectation; however, datasets often already have local vocabulary resources defined, and so the prospects for large-scale interoperability can be substantially improved by creating alignment links from these local vocabularies out to common external reference resources. The ARIADNE project is undertaking large-scale integration of archaeology dataset metadata records, to create a cross-searchable research repository resource. Key to enabling this cross search will be the ‘subject’ metadata originating from multiple data providers, containing terms from multiple multilingual controlled vocabularies. This paper discusses various aspects of vocabulary mapping. Experience from the previous SENESCHAL project in the publication of controlled vocabularies as Linked Open Data is discussed, emphasizing the importance of unique URI identifiers for vocabulary concepts. There is a need to align legacy indexing data to the uniquely defined concepts and examples are discussed of SENESCHAL data alignment work. A case study for the ARIADNE project presents work on mapping between vocabularies, based on the Getty Art and Architecture Thesaurus as a central hub and employing an interactive vocabulary mapping tool developed for the project, which generates SKOS mapping relationships in JSON and other formats. The potential use of such vocabulary mappings to assist cross search over archaeological datasets from different countries is illustrated in a pilot experiment. The results demonstrate the enhanced opportunities for interoperability and cross searching that the approach offers.

Automatic Metadata Generation in an Archaeological Digital Library: Semantic Annotation of Grey Literature

This paper discusses the automatic generation of rich metadata from excavation reports from the Archaeological Data Service library of grey literature (OASIS). The work is part of the STAR project, in collaboration with English Heritage. An extension of the CIDOC CRM ontology for the archaeological domain acts as a core ontology. Rich metadata is automatically extracted from grey literature, directed by the CRM, via a three phase process of semantic enrichment employing the GATE toolkit augmented with bespoke rules and knowledge resources. The paper demonstrates the potential of combining knowledge based resources (ontologies and thesauri) in information extraction, and techniques for delivering the automatically extracted metadata as XML annotations coupled with the grey literature reports and as RDF graphs decoupled from content. Examples from two consuming applications are discussed, the Andronikos web portal which serves the annotated XML files for visual inspection and the STAR project, research demonstrator which offers unified search across of archaeological excavation data and grey literature via the core ontology CRM-EH.

Excavating grey literature: A case study on the rich indexing of archaeological documents via natural language‐processing techniques and knowledge‐based resources

Purpose – This paper sets out to discuss the use of information extraction (IE), a natural language‐processing (NLP) technique to assist “rich” semantic indexing of diverse archaeological text resources. The focus of the research is to direct a semantic‐aware “rich” indexing of diverse natural language resources with properties capable of satisfying information retrieval from online publications and datasets associated with the Semantic Technologies for Archaeological Resources (STAR) project.Design/methodology/approach – The paper proposes use of the English Heritage extension (CRM‐EH) of the standard core ontology in cultural heritage, CIDOC CRM, and exploitation of domain thesauri resources for driving and enhancing an Ontology‐Oriented Information Extraction process. The process of semantic indexing is based on a rule‐based Information Extraction technique, which is facilitated by the General Architecture of Text Engineering (GATE) toolkit and expressed by Java Annotation Pattern Engine (JAPE) rules.F...

Template Based Semantic Integration: From Legacy Archaeological Datasets to Linked Data

The online dissemination of datasets is becoming common practice within the archaeology domain. Since the legacy database schemas involved are often created on a per-site basis, cross searching or reusing this data remains difficult. Employing an integrating ontology, such as the CIDOC CRM, is one step towards resolving these issues. However, this has tended to require computing specialists with detailed knowledge of the ontologies involved. Results are presented from a collaborative project between computer scientists and archaeologists that created lightweight tools to make it easier for non-specialists to publish Linked Data. Archaeologists used the STELLAR project tools to publish major excavation datasets as Linked Data, conforming to the CIDOC CRM ontology. The template-based Extract Transform Load method is described. Reflections on the experience of using the template-based tools are discussed, together with practical issues including the need for terminology alignment and licensing considerations.

Using Terminology Web Services for the Archaeological Domain

The AHRC funded STAR project (Semantic Technologies for Archaeological Resources) has developed web services for knowledge organisation systems (KOS) represented in SKOS RDF format, building on previous work by the University of Glamorgan Hypermedia Research Unit on terminology web services. The current service operates on a repository of multiple (English Heritage) thesauri converted to SKOS format, containing terms and concepts that would be familiar to those working within the archaeological domain. It provides facilities for search, concept browsing and semantic expansion across these specialist terminologies.

Augmenting Dublin Core digital library metadata with Dewey Decimal Classification

Purpose – The purpose of this paper is to describe a new approach to a well-known problem for digital libraries, how to search across multiple unrelated libraries with a single query. Design/methodology/approach – The approach involves creating new Dewey Decimal Classification terms and numbers from existing Dublin Core records. In total, 263,550 records were harvested from three digital libraries. Weighted key terms were extracted from the title, description and subject fields of each record. Ranked DDC classes were automatically generated from these key terms by considering DDC hierarchies via a series of filtering and aggregation stages. A mean reciprocal ranking evaluation compared a sample of 49 generated classes against DDC classes created by a trained librarian for the same records. Findings – The best results combined weighted key terms from the title, description and subject fields. Performance declines with increased specificity of DDC level. The results compare favorably with similar studies. R...