124,086 research outputs found
An Infrastructure for acquiring high quality semantic metadata
Because metadata that underlies semantic web applications is gathered from distributed and heterogeneous data sources, it is important to ensure its quality (i.e., reduce duplicates, spelling errors, ambiguities). However, current infrastructures that acquire and integrate semantic data have only marginally addressed the issue of metadata quality. In this paper we present our metadata acquisition infrastructure, ASDI, which pays special attention to ensuring that high quality metadata is derived. Central to the architecture of ASDI is a erification engine that relies on several semantic web tools to check the quality of the derived data. We tested our prototype in the context of building a semantic web portal for our lab, KMi. An experimental evaluation omparing the automatically extracted data against manual annotations indicates that the verification engine enhances the quality of the extracted semantic metadata
Bridging the Gap Between Traditional Metadata and the Requirements of an Academic SDI for Interdisciplinary Research
Metadata has long been understood as a fundamental component of any Spatial Data Infrastructure, providing information relating to discovery, evaluation and use of
datasets and describing their quality. Having good metadata about a dataset is fundamental to using it correctly and to understanding the implications of issues such as missing data or incorrect attribution on the results obtained for any analysis carried out.
Traditionally, spatial data was created by expert users (e.g. national mapping agencies), who created metadata for the data. Increasingly, however, data used in spatial analysis comes from multiple sources and could be captured or used by nonexpert users – for example academic researchers ‐ many of whom are from non‐GIS disciplinary backgrounds, not familiar with metadata and perhaps working in geographically dispersed teams. This paper examines the applicability of metadata in this academic context, using a multi‐national coastal/environmental project as a case study. The work to date highlights a number of suggestions for good practice, issues and research questions relevant to Academic SDI, particularly given the increased levels of research data sharing and reuse required by UK and EU funders
Soft peer review: social software and distributed scientific evaluation
The debate on the prospects of peer-review in the Internet age and the
increasing criticism leveled against the dominant role of impact factor
indicators are calling for new measurable criteria to assess scientific quality.
Usage-based metrics offer a new avenue to scientific quality assessment but
face the same risks as first generation search engines that used unreliable
metrics (such as raw traffic data) to estimate content quality. In this article I
analyze the contribution that social bookmarking systems can provide to the
problem of usage-based metrics for scientific evaluation. I suggest that
collaboratively aggregated metadata may help fill the gap between traditional
citation-based criteria and raw usage factors. I submit that bottom-up,
distributed evaluation models such as those afforded by social bookmarking
will challenge more traditional quality assessment models in terms of coverage,
efficiency and scalability. Services aggregating user-related quality indicators
for online scientific content will come to occupy a key function in the scholarly
communication system
Having Our Say: Engaging ILL and Liaison Librarians in Metadata Curation in EDS
Deciding whether to support discovery of metadata for unsubscribed content raises a lot of questions for technical and public services librarians, from the philosophical to the pragmatic. Doing so requires careful curation and monitoring of metadata sources, and benefits from library-wide input. During this session, presenters describe their process for vetting metadata collections with ILL and liaison librarians for inclusion in EDS. This involves a three-step evaluation of collections for overall metadata quality, likelihood of ILL fulfillment, and value to the collection. Over the course of this evaluation, technical services, ILL, and liaison librarians each “have their say” on whether metadata collections merit inclusion in the discovery layer. Subsequently, ILL and liaison play a critical role monitoring these sources, and supporting decisions to de-commission sources when needed
An Integrated Content and Metadata based Retrieval System for Art
In this paper we describe aspects of the Artiste project to develop a distributed content and metadata based analysis, retrieval and navigation system for a number of major European Museums. In particular, after a brief overview of the complete system, we describe the design and evaluation of some of the image analysis algorithms developed to meet the specific requirements of the users from the museums. These include a method for retrievals based on sub images, retrievals based on very low quality images and retrieval using craquelure type
Accurator: Nichesourcing for Cultural Heritage
With more and more cultural heritage data being published online, their
usefulness in this open context depends on the quality and diversity of
descriptive metadata for collection objects. In many cases, existing metadata
is not adequate for a variety of retrieval and research tasks and more specific
annotations are necessary. However, eliciting such annotations is a challenge
since it often requires domain-specific knowledge. Where crowdsourcing can be
successfully used for eliciting simple annotations, identifying people with the
required expertise might prove troublesome for tasks requiring more complex or
domain-specific knowledge. Nichesourcing addresses this problem, by tapping
into the expert knowledge available in niche communities. This paper presents
Accurator, a methodology for conducting nichesourcing campaigns for cultural
heritage institutions, by addressing communities, organizing events and
tailoring a web-based annotation tool to a domain of choice. The contribution
of this paper is threefold: 1) a nichesourcing methodology, 2) an annotation
tool for experts and 3) validation of the methodology and tool in three case
studies. The three domains of the case studies are birds on art, bible prints
and fashion images. We compare the quality and quantity of obtained annotations
in the three case studies, showing that the nichesourcing methodology in
combination with the image annotation tool can be used to collect high quality
annotations in a variety of domains and annotation tasks. A user evaluation
indicates the tool is suited and usable for domain specific annotation tasks
MetaEnhance: Metadata Quality Improvement for Electronic Theses and Dissertations of University Libraries
Metadata quality is crucial for digital objects to be discovered through
digital library interfaces. However, due to various reasons, the metadata of
digital objects often exhibits incomplete, inconsistent, and incorrect values.
We investigate methods to automatically detect, correct, and canonicalize
scholarly metadata, using seven key fields of electronic theses and
dissertations (ETDs) as a case study. We propose MetaEnhance, a framework that
utilizes state-of-the-art artificial intelligence methods to improve the
quality of these fields. To evaluate MetaEnhance, we compiled a metadata
quality evaluation benchmark containing 500 ETDs, by combining subsets sampled
using multiple criteria. We tested MetaEnhance on this benchmark and found that
the proposed methods achieved nearly perfect F1-scores in detecting errors and
F1-scores in correcting errors ranging from 0.85 to 1.00 for five of seven
fields.Comment: 7 pages, 3 tables, and 1 figure. Accepted by 2023 ACM/IEEE Joint
Conference on Digital Libraries (JCDL '23) as a short pape
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