ClaiMaker: Weaving a Semantic Web of Research Papers

Li, Gangmin; Uren, Victoria; Motta, Enrico; Shum, Simon Buckingham; Domingue, John

doi:10.1007/3-540-48005-6_37

Cited by 23 publications

(13 citation statements)

References 6 publications

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“…ClaiMaker (Li et al, 2002) sits at a different point in the knowledge construction process; it is designed to express and contest arguments that exist in published material. Our intent with Codex is to capture knowledge in situ, that is, during the course of its construction.…”

Section: Related Workmentioning

confidence: 99%

“…These ontologies describe the kind of entities that a CSCW system is capable of expressing (van Bruggen et al, 2003) and are typically considered generic containers for scientific work (e.g., Suthers, 1999), not as choices of perspective that are themselves contestable. Some CSCW methods, like ScholOnto (Buckingham Shum et al, 2000), develop discourse ontologies to express connections between researchers and published topics; ClaiMaker (Li et al, 2002) provides a structured vocabulary for articulating scientific claims and a mechanism to annotate documents so as to encode relations between text documents and the concepts expressed therein. However, the public record of science tells only part of the story, and not always faithfully; it does not reveal all of the analysis procedures, decisions, wrong turns, and intermediate results that underlie published work.…”

Section: Building Knowledge From the Bottom-upmentioning

confidence: 99%

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Beyond ontologies: Toward situated representations of scientific knowledge

Pike

Gahegan

2007

International Journal of Human-Computer Studies

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Section: Related Workmentioning

confidence: 99%

Section: Building Knowledge From the Bottom-upmentioning

confidence: 99%

Beyond ontologies: Toward situated representations of scientific knowledge

Pike

Gahegan

2007

International Journal of Human-Computer Studies

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“…Argumentation systems have been used predominantly in physically co-located meetings where participants engage in a free-form discussion while a single facilitator captures these deliberations in the form of an commonly-viewable argumentation map [9] [10] [11] [12]. Argumentation systems have also been used, to a lesser extent, to enable nonfacilitated deliberations, over the Internet, with physically distributed participants [13] [19]. With only one exception 1 , the scale of use has however been small, with on the order of 10 participants or so working together on any given task, far less than what is implied by the vision of large-scale collective intelligence introduced in this paper.…”

Section: Generate Ideasmentioning

confidence: 99%

Achieving Collective Intelligence via Large-Scale On-line Argumentation

Klein

2007

Second International Conference on Internet and Web Applications and Services (ICIW'07)

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“…al. propose a system that analyzes the claims made in a paper (Li et al, 2002). ClaiMaker utilizes ontologies to improve the accuracy of the analysis.…”

Section: Recommender Systemsmentioning

confidence: 99%

A Subspace Clustering Framework for Research Group Collaboration

Agarwal

Haque

Liu

et al. 2006

International Journal of Information Technology and Web Engineering

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Researchers spend considerable time searching for relevant papers on the topic in which they are currently interested. Often, despite having similar interests, researchers in the same lab do not find it convenient to share results of bibliographic searches and thus conduct independent time-consuming searches. Research paper recommender systems can help the researcher avoid such time-consuming searches by allowing each researcher to automatically take advantage of previous searches performed by others in the lab. Existing recommender systems were developed for commercial domains to assist users by focussing towards products of their interests. Unlike those domains, the research paper domain has relatively few users when compared with the huge number of research papers. In this paper we present a novel system to recommend relevant research papers to a user based on the user's recent querying and browsing habits. The core of the system is a scalable subspace clustering algorithm (SCuBA 1 ) that performs well on the sparse, high-dimensional data collected in this domain. Both synthetic and benchmark datasets are used to evaluate the recommendation system and to demonstrate that it performs better than the traditional collaborative filtering approaches when recommending research papers. Background and MotivationThe explosive growth of the world-wide web and the emerging popularity of e-commerce has caused the collection of data to outpace the analysis necessary to extract useful information. Recommender systems were developed to help close the gap between information collection and analysis by filtering all of the available information to present what is most valuable to the user (Resnick and Varian, 1997).One area of the web that has seen continued growth is the online publication of research papers. The number of research papers published continues to increase, and new technology has allowed many older papers to be rapidly digitized. A typical researcher must sift through a large quantity of articles manually, relying on keyword-based searches or paper citations to guide them. The search results of researchers with similar interests can help direct a more effective search, but the process of sharing search results is often too We assume that the proposed system is localized for a typical lab setting which augments an existing search engine. Rather than performing search in some digital library, SCuBA tries to leverage the existing search results. It tries to find similar interest groups of users based on their browsing patterns and recommends research papers which might be interesting to them.There are two main branches of recommender systems; content based filtering and collaborative filtering.Content based filtering (CBF) approaches create relationships between items by analyzing inherent charac-

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ClaiMaker: Weaving a Semantic Web of Research Papers

Cited by 23 publications

References 6 publications

Beyond ontologies: Toward situated representations of scientific knowledge

Beyond ontologies: Toward situated representations of scientific knowledge

Achieving Collective Intelligence via Large-Scale On-line Argumentation

A Subspace Clustering Framework for Research Group Collaboration

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