2,196 research outputs found
Building Interoperable Vocabulary and Structures for Learning Objects
The structural, functional, and production views on learning objects influence metadata structure and vocabulary. We drew on these views and conducted a literature review and in-depth analysis of 14 learning objects and over 500 components in these learning objects to model the knowledge framework for a learning object ontology. The learning object ontology reported in this paper consists of 8 top-level classes, 28 classes at the second level, and 34 at the third level. Except class Learning object, all other classes have the three properties of preferred term, related term, and synonym. To validate the ontology, we conducted a query log analysis that focused on discovering what terms users have used at both conceptual and word levels. The findings show that the main classes in the ontology are either conceptually or linguistically similar to the top terms in the query log data. We built an Exercise Editor as an informal experiment to test its ability to be adopted in authoring tools. The main contribution of this project is in the framework for the learning object domain and methodology used to develop and validate an ontology
Reasoning & Querying – State of the Art
Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF
Syntactic Topic Models
The syntactic topic model (STM) is a Bayesian nonparametric model of language
that discovers latent distributions of words (topics) that are both
semantically and syntactically coherent. The STM models dependency parsed
corpora where sentences are grouped into documents. It assumes that each word
is drawn from a latent topic chosen by combining document-level features and
the local syntactic context. Each document has a distribution over latent
topics, as in topic models, which provides the semantic consistency. Each
element in the dependency parse tree also has a distribution over the topics of
its children, as in latent-state syntax models, which provides the syntactic
consistency. These distributions are convolved so that the topic of each word
is likely under both its document and syntactic context. We derive a fast
posterior inference algorithm based on variational methods. We report
qualitative and quantitative studies on both synthetic data and hand-parsed
documents. We show that the STM is a more predictive model of language than
current models based only on syntax or only on topics
Bibliographic Analysis on Research Publications using Authors, Categorical Labels and the Citation Network
Bibliographic analysis considers the author's research areas, the citation
network and the paper content among other things. In this paper, we combine
these three in a topic model that produces a bibliographic model of authors,
topics and documents, using a nonparametric extension of a combination of the
Poisson mixed-topic link model and the author-topic model. This gives rise to
the Citation Network Topic Model (CNTM). We propose a novel and efficient
inference algorithm for the CNTM to explore subsets of research publications
from CiteSeerX. The publication datasets are organised into three corpora,
totalling to about 168k publications with about 62k authors. The queried
datasets are made available online. In three publicly available corpora in
addition to the queried datasets, our proposed model demonstrates an improved
performance in both model fitting and document clustering, compared to several
baselines. Moreover, our model allows extraction of additional useful knowledge
from the corpora, such as the visualisation of the author-topics network.
Additionally, we propose a simple method to incorporate supervision into topic
modelling to achieve further improvement on the clustering task.Comment: Preprint for Journal Machine Learnin
Development of a controlled vocabulary for learning objects' functional description in an educational repository
Proceeding of: Metadata for Knowledge and Learning (DC 2006). October 3-6, 2006. Colima, MexicoThis paper presents the development of a controlled vocabulary for functional description in an educational repository project which has adopted a DC application profile. The vocabulary, organized according to the identified functions of educational documents and learning objects' components, permits their retrieval and reuse to be improved.The DOTEINE Project has been financed by the Spanish Interministerial Commission of
Science & Technology (CICYT, ref. BSO2003-04895). The IACORIE Project has been
financed by the Regional Government of Madrid (Comunidad de Madrid, ref.
06/HSE/0165/2004).Publicad
The Semantic and Syntactic Model of Metadata
As more information becomes “born digital”, metadata creation is increasingly becoming part of the information creation process. Current metadata schemes inherit much of the library cataloging tradition, which has shown limitations on representing “born digital” type of resources. Through analysis of issues of metadata schemes and review of metadata research and projects, the authors propose an ontology-based approach to building a modular metadata model in which semantics and syntax may be integrated to suit the needs for representing “born digital” resources. The authors use an learning object ontology as an example to demonstrate how the semantics and syntax may be built into a modular model for metadata
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