Finding the right answer: an information retrieval approach supporting knowledge sharing

Knowledge Management can be defined as the effective strategies to get the right piece of knowledge to the right person in the right time. Having the main purpose of providing users with information items of their interest, recommender systems seem to be quite valuable for organizational knowledge management environments. Here we present KARe (Knowledgeable Agent for Recommendations), a multiagent recommender system that supports users sharing knowledge in a peer-to-peer environment. Central to this work is the assumption that social interaction is essential for the creation and dissemination of new knowledge. Supporting social interaction, KARe allows users to share knowledge through questions and answers. This paper describes KARe�s agent-oriented architecture and presents its recommendation algorithm

Strategies for Improving Semi-automated Topic Classification of Media and Parliamentary documents

Since 1995 the techniques and capacities to store new electronic data and to make it available to many persons have become a common good. As of then, different organizations, such as research institutes, universities, libraries, and private companies (Google) started to scan older documents and make them electronically available as well. This has generated a lot of new research opportunities for all kinds of academic disciplines. The use of software to analyze large datasets has become an important part of doing research in the social sciences. Most academics rely on human coded datasets, both in qualitative and quantitative research. However, with the increasing amount of datasets and the complexity of the questions scholars pose to the datasets, the quest for more efficient and effective methods is now on the agenda. One of the most common techniques of content analysis is the Boolean key-word search method. To find certain topics in a dataset, the researcher creates first a list of keywords, added with certain parameters (AND, OR etc.). All keys are usually grouped in families and the entire list of keys and groups is called the ontology. Then the keywords are searched in the dataset, retrieving all documents containing the specified keywords. The online newspaper dataset, LexisNexis, provides the user with such a Boolean search method. However, the Boolean key-word search is not always satisfying in terms of reliability and validity. For that reason social scientists rely on hand-coding. Two projects that do so are the congressional bills project (www.congressionalbills.org ) and the policy agenda-setting project (see www.policyagendas.org ). They developed a topic code book and coded various different sources, such as, the state of the union speeches, bills, newspaper articles etcetera. The continuous improving automated coding techniques, and the increasing number of agenda setting projects (in especially European countries), however, has made the use of automated coding software a feasible option and also a necessity

Evaluation of MIRACLE approach results for CLEF 2003

This paper describes MIRACLE (Multilingual Information RetrievAl for the CLEf campaign) approach and results for the mono, bi and multilingual Cross Language Evaluation Forum tasks. The approach is based on the combination of linguistic and statistic techniques to perform indexing and retrieval tasks

Unshackle the Internet: Independent Voices and the Role of Foreign Internet Companies Operating in China

Advances in information technology have the potential to empower individuals globally and to serve as a force for democratization. The number of Internet users in mainland China continues to increase at a phenomenal rate. From 1998 to 2005, China's online population grew from 1.17 million to 103 million, with the most recent official count in January 2006 at approximately 110 million.In China, the Internet has become an increasingly important tool for empowering Chinese activists, journalists, rights defenders, intellectuals and grassroots groups by providing increased access to information as well as a virtual commons for the exchange of ideas between groups and individuals.However, technology and control of the Internet have also been utilized by the Chinese government to implement censorship, surveillance and social and political control.In the last several months, as foreign IT companies have come under media and U.S. government scrutiny, HRIC has been actively monitoring the human rights impact of their activities and developing suggestions for implementing the human rights responsibilities of foreign-based IT companies operating in China

Enriching ontological user profiles with tagging history for multi-domain recommendations

Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites

Distinctive-attribute Extraction for Image Captioning

Image captioning, an open research issue, has been evolved with the progress of deep neural networks. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are employed to compute image features and generate natural language descriptions in the research. In previous works, a caption involving semantic description can be generated by applying additional information into the RNNs. In this approach, we propose a distinctive-attribute extraction (DaE) which explicitly encourages significant meanings to generate an accurate caption describing the overall meaning of the image with their unique situation. Specifically, the captions of training images are analyzed by term frequency-inverse document frequency (TF-IDF), and the analyzed semantic information is trained to extract distinctive-attributes for inferring captions. The proposed scheme is evaluated on a challenge data, and it improves an objective performance while describing images in more detail.Comment: 14 main pages, 4 supplementary page