Unstructured Information Management applications are software systems that analyze large volumes of unstructured information in order to discover knowledge that is relevant to an end user. An example UIM application might ingest plain text and identify entities, such as persons, places, organizations; or relations, such as works-for or located-at.
Basically, its an RDF-based web annotations system.
Three JISC-funded projects have a requirement to allow people to annotate events and other things. The projects are:
* Collaborative Research on the Web (CREW) - University of Bristol and University of Manchester
* Semantic Tools for Screen Arts Research (STARS) - University of Bristol
* Integration Project (CIP) - University of Bristol
The Caboto project was setup to create a collaborative effort to fulfill the requirements of CREW, STARS and CIP.
The requirements from the JISC projects:
* CREW Events Requirements
* CIP Requirements
* STARS Requirements
The project is in the early stages but its is possible to obtain and run the project:
Hibernate Annotations is my preferred way to map my entity classes, since they don't require any external file (thus keeping mapping info in your Java files), is fully integrated with all Hibernate mapping capabilities and Hibernate documentation encourages us to use this kind of configuration because it's more efficient.
Annotation driven mapping in Hibernate uses the standard JPA API annotations and introduce some specific extensions to deal with some Hibernate features. You can find a full reference in the official documentation.
About
AutoDAO is a Generic DAO on steroids implementation for Java.
This project was inspired by Don't repeat the DAO! article by Per Mellqvist.
Main features
* Ready to use CRUD operations
* Zero persistence code for common DAO queries
* Annotation-driven auto-configuration
* Spring Framework custom namespace for easy to use configuration
* Hibernate/JPA support
Tinderbox stores and organizes your notes, plans, and ideas. It can help you analyze and understand them. And Tinderbox helps you share ideas through Web journals and web logs.
CWIS (pronounced see-wis) is software to assemble, organize, and share collections of data about resources, like Yahoo! or Google Directory but conforming to international and academic standards for metadata. CWIS was specifically created to help build collections of Science, Technology, Engineering, and Math (STEM) resources and connect them into NSF's National Science Digital Library, but can be (and is being) used for a wide variety of other purposes.
Some of the features of CWIS include:
* resource annotations and ratings (a la Amazon)
* keyword searching (with phrase and exclusion support a la Google)
* fielded searching
* recommender system (a la Amazon)
* OAI 2.0 export (with oai_dc and nsdl_dc schemas)
* RSS feed support
* integrated metadata editing tool
* user-definable schema (comes with full qualified Dublin Core)
* prepackaged taxonomies (includes GEM Subject taxonomy)
* user interface themes
* turnkey installation
CWIS also has functionality (PHP) separated from appearance (HTML), making it relatively easy to customize for your own site.
An approach focussed on resolving identity of
subjects in a photo using mobile device connectivity,
Web services and social network ontologies is
presented in this paper. A framework is described in
which mobile device sensors, Web services and
ontologies are combined to provide meaningful photo
annotation metadata that can be used to recall photos
from the Web. Useful metadata can be gleaned from
the environment at the time of capture and further
information inferred from available Web services.
This paper presents an approach to semi-automate photo annotation. Instead of using content-recognition techniques this approach leverages context information available at the scene of the photo such as time and location in combination with existing photo annotations to provide suggestions to the user. An algorithm exploits a number of technologies including Global Positioning System (GPS), Semantic Web, Web services and Online Social Networks, considering all information and making a best-e ort attempt to suggest both people and places depicted in the photo. The user then selects which of the suggestions are correct to annotate the photo. This process accelerates the photo annotation process dramatically which in turn aids photo search for a wide range of query tools that currently trawl the millions of photos on the Web.