14,842 research outputs found
Bridging the gap between social tagging and semantic annotation: E.D. the Entity Describer
Semantic annotation enables the development of efficient computational methods for analyzing and interacting with information, thus maximizing its value. With the already substantial and constantly expanding data generation capacity of the life sciences as well as the concomitant increase in the knowledge distributed in scientific articles, new ways to produce semantic annotations of this information are crucial. While automated techniques certainly facilitate the process, manual annotation remains the gold standard in most domains. In this manuscript, we describe a prototype mass-collaborative semantic annotation system that, by distributing the annotation workload across the broad community of biomedical researchers, may help to produce the volume of meaningful annotations needed by modern biomedical science. We present E.D., the Entity Describer, a mashup of the Connotea social tagging system, an index of semantic web-accessible controlled vocabularies, and a new public RDF database for storing social semantic annotations
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From open content to open thinking
So far Open Educational Resources (OER) research has focused on the objective to 'open' education by making accessible free educational resources to the world. In the latest years the movement has matured, and a growing amount of OER have been made available by universities, researchers and scholars through several portals. Nonetheless, the level of adoption of OERs into common teaching practices remains quite low. In this paper we suggest that one of the main barriers to OER's adoption is the lack of 'opening up' to people's thinking around OERs and we propose Cohere, a tool which aims at making this thinking visible and exportable in ways that support the emergence of 'collective intelligence' around OERs research. Accessing Collective Intelligence (CI) around OERs is presented as a medium to know and understand what people think, how people design and use OERs thus increasing the easy of re-use of OER in learning and research practices
Reason Maintenance - Conceptual Framework
This paper describes the conceptual framework for reason maintenance developed as part of
WP2
Just an Update on PMING Distance for Web-based Semantic Similarity in Artificial Intelligence and Data Mining
One of the main problems that emerges in the classic approach to semantics is
the difficulty in acquisition and maintenance of ontologies and semantic
annotations. On the other hand, the Internet explosion and the massive
diffusion of mobile smart devices lead to the creation of a worldwide system,
which information is daily checked and fueled by the contribution of millions
of users who interacts in a collaborative way. Search engines, continually
exploring the Web, are a natural source of information on which to base a
modern approach to semantic annotation. A promising idea is that it is possible
to generalize the semantic similarity, under the assumption that semantically
similar terms behave similarly, and define collaborative proximity measures
based on the indexing information returned by search engines. The PMING
Distance is a proximity measure used in data mining and information retrieval,
which collaborative information express the degree of relationship between two
terms, using only the number of documents returned as result for a query on a
search engine. In this work, the PMINIG Distance is updated, providing a novel
formal algebraic definition, which corrects previous works. The novel point of
view underlines the features of the PMING to be a locally normalized linear
combination of the Pointwise Mutual Information and Normalized Google Distance.
The analyzed measure dynamically reflects the collaborative change made on the
web resources
NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps
Molecular biology knowledge can be systematically represented in a
computer-readable form as a comprehensive map of molecular interactions. There
exist a number of maps of molecular interactions containing detailed
description of various cell mechanisms. It is difficult to explore these large
maps, to comment their content and to maintain them. Though there exist several
tools addressing these problems individually, the scientific community still
lacks an environment that combines these three capabilities together. NaviCell
is a web-based environment for exploiting large maps of molecular interactions,
created in CellDesigner, allowing their easy exploration, curation and
maintenance. NaviCell combines three features: (1) efficient map browsing based
on Google Maps engine; (2) semantic zooming for viewing different levels of
details or of abstraction of the map and (3) integrated web-based blog for
collecting the community feedback. NaviCell can be easily used by experts in
the field of molecular biology for studying molecular entities of their
interest in the context of signaling pathways and cross-talks between pathways
within a global signaling network. NaviCell allows both exploration of detailed
molecular mechanisms represented on the map and a more abstract view of the map
up to a top-level modular representation. NaviCell facilitates curation,
maintenance and updating the comprehensive maps of molecular interactions in an
interactive fashion due to an imbedded blogging system. NaviCell provides an
easy way to explore large-scale maps of molecular interactions, thanks to the
Google Maps and WordPress interfaces, already familiar to many users. Semantic
zooming used for navigating geographical maps is adopted for molecular maps in
NaviCell, making any level of visualization meaningful to the user. In
addition, NaviCell provides a framework for community-based map curation.Comment: 20 pages, 5 figures, submitte
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
Do peers see more in a paper than its authors?
Recent years have shown a gradual shift in the content of biomedical publications that is freely accessible, from titles and abstracts to full text. This has enabled new forms of automatic text analysis and has given rise to some interesting questions: How informative is the abstract compared to the full-text? What important information in the full-text is not present in the abstract? What should a good summary contain that is not already in the abstract? Do authors and peers see an article differently? We answer these questions by comparing the information content of the abstract to that in citances-sentences containing citations to that article. We contrast the important points of an article as judged by its authors versus as seen by peers. Focusing on the area of molecular interactions, we perform manual and automatic analysis, and we find that the set of all citances to a target article not only covers most information (entities, functions, experimental methods, and other biological concepts) found in its abstract, but also contains 20% more concepts. We further present a detailed summary of the differences across information types, and we examine the effects other citations and time have on the content of citances
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