9,518 research outputs found
Clinical data wrangling using Ontological Realism and Referent Tracking
Ontological realism aims at the development of high quality ontologies that faithfully represent what is general in reality and to use these ontologies to render heterogeneous data collections comparable. To achieve this second goal for clinical research datasets presupposes not merely (1) that the requisite ontologies already exist, but also (2) that the datasets in question are faithful to reality in the dual sense that (a) they denote only particulars and relationships between particulars that do in fact exist and (b) they do this in terms of the types and type-level relationships described in these ontologies. While much attention has been devoted to (1), work on (2), which is the topic of this paper, is comparatively rare. Using Referent Tracking as basis, we describe a technical data wrangling strategy which consists in creating for each dataset a template that, when applied to each particular record in the
dataset, leads to the generation of a collection of Referent Tracking Tuples (RTT) built out of unique identifiers for the entities described by means of the data items in the record. The proposed strategy is based on (i) the distinction between data and what data are about, and (ii) the explicit descriptions of portions of reality which RTTs provide and which range not only over the particulars described by data items in a dataset, but also over these data items themselves. This last feature allows us to describe particulars that are only implicitly referred to by the dataset; to provide information about correspondences between data items in a dataset; and to assert
which data items are unjustifiably or redundantly present in or absent from the dataset. The approach has been tested on a dataset collected from patients seeking treatment for orofacial pain at two German universities and made available for the
NIDCR-funded OPMQoL project
Good Applications for Crummy Entity Linkers? The Case of Corpus Selection in Digital Humanities
Over the last decade we have made great progress in entity linking (EL)
systems, but performance may vary depending on the context and, arguably, there
are even principled limitations preventing a "perfect" EL system. This also
suggests that there may be applications for which current "imperfect" EL is
already very useful, and makes finding the "right" application as important as
building the "right" EL system. We investigate the Digital Humanities use case,
where scholars spend a considerable amount of time selecting relevant source
texts. We developed WideNet; a semantically-enhanced search tool which
leverages the strengths of (imperfect) EL without getting in the way of its
expert users. We evaluate this tool in two historical case-studies aiming to
collect a set of references to historical periods in parliamentary debates from
the last two decades; the first targeted the Dutch Golden Age, and the second
World War II. The case-studies conclude with a critical reflection on the
utility of WideNet for this kind of research, after which we outline how such a
real-world application can help to improve EL technology in general.Comment: Accepted for presentation at SEMANTiCS '1
Achieving interoperability between the CARARE schema for monuments and sites and the Europeana Data Model
Mapping between different data models in a data aggregation context always
presents significant interoperability challenges. In this paper, we describe
the challenges faced and solutions developed when mapping the CARARE schema
designed for archaeological and architectural monuments and sites to the
Europeana Data Model (EDM), a model based on Linked Data principles, for the
purpose of integrating more than two million metadata records from national
monument collections and databases across Europe into the Europeana digital
library.Comment: The final version of this paper is openly published in the
proceedings of the Dublin Core 2013 conference, see
http://dcevents.dublincore.org/IntConf/dc-2013/paper/view/17
On-Demand Big Data Integration: A Hybrid ETL Approach for Reproducible Scientific Research
Scientific research requires access, analysis, and sharing of data that is
distributed across various heterogeneous data sources at the scale of the
Internet. An eager ETL process constructs an integrated data repository as its
first step, integrating and loading data in its entirety from the data sources.
The bootstrapping of this process is not efficient for scientific research that
requires access to data from very large and typically numerous distributed data
sources. a lazy ETL process loads only the metadata, but still eagerly. Lazy
ETL is faster in bootstrapping. However, queries on the integrated data
repository of eager ETL perform faster, due to the availability of the entire
data beforehand.
In this paper, we propose a novel ETL approach for scientific data
integration, as a hybrid of eager and lazy ETL approaches, and applied both to
data as well as metadata. This way, Hybrid ETL supports incremental integration
and loading of metadata and data from the data sources. We incorporate a
human-in-the-loop approach, to enhance the hybrid ETL, with selective data
integration driven by the user queries and sharing of integrated data between
users. We implement our hybrid ETL approach in a prototype platform, Obidos,
and evaluate it in the context of data sharing for medical research. Obidos
outperforms both the eager ETL and lazy ETL approaches, for scientific research
data integration and sharing, through its selective loading of data and
metadata, while storing the integrated data in a scalable integrated data
repository.Comment: Pre-print Submitted to the DMAH Special Issue of the Springer DAPD
Journa
GEORDi: Supporting lightweight end-user authoring and exploration of Linked Data
The US and UK governments have recently made much of the data created by their various departments available as data sets (often as csv files) available on the web. Known as ”open data” while these are valuable assets, much of this data remains useless because it is effectively inaccessible for citizens to access for the following reasons: (1) it is often a tedious, many step process for citizens simply to find data relevant to a query. Once the data candidate is located, it often must be downloaded and opened in a separate application simply to see if the data that may satisfy the query is contained in it. (2) It is difficult to join related data sets to create richer integrated information (3) it is particularly difficult to query either a single data set, and even harder to query across related data sets. (4) To date, one has had to be well versed in semantic web protocols like SPARQL, RDF and URI formation to integrate and query such sources as reusable linked data. Our goal has been to develop tools that will let regular, non-programmer web citizens make use of this Web of Data. To this end, we present GEORDi, a set of integrated tools and services that lets citizen users identify, explore, query and represent these open data sources over the web via Linked Data mechanisms. In this paper we describe the GEORDi process of authoring new and translating existing open data in a linkable format, GEORDi’s lens mechanism for rendering rich, plain language descriptions and views of resources, and the GEORDI link-sliding paradigm for data exploration. With these tools we demonstrate that it is possible to make the Web of open (and linked) data accessible for ordinary web citizen users
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