16,129 research outputs found
Forecasting the Spreading of Technologies in Research Communities
Technologies such as algorithms, applications and formats are an important part of the knowledge produced and reused in the research process. Typically, a technology is expected to originate in the context of a research area and then spread and contribute to several other fields. For example, Semantic Web technologies have been successfully adopted by a variety of fields, e.g., Information Retrieval, Human Computer Interaction, Biology, and many others. Unfortunately, the spreading of technologies across research areas may be a slow and inefficient process, since it is easy for researchers to be unaware of potentially relevant solutions produced by other research communities. In this paper, we hypothesise that it is possible to learn typical technology propagation patterns from historical data and to exploit this knowledge i) to anticipate where a technology may be adopted next and ii) to alert relevant stakeholders about emerging and relevant technologies in other fields. To do so, we propose the Technology-Topic Framework, a novel approach which uses a semantically enhanced technology-topic model to forecast the propagation of technologies to research areas. A formal evaluation of the approach on a set of technologies in the Semantic Web and Artificial Intelligence areas has produced excellent results, confirming the validity of our solution
Multi-Instance Multi-Label Learning
In this paper, we propose the MIML (Multi-Instance Multi-Label learning)
framework where an example is described by multiple instances and associated
with multiple class labels. Compared to traditional learning frameworks, the
MIML framework is more convenient and natural for representing complicated
objects which have multiple semantic meanings. To learn from MIML examples, we
propose the MimlBoost and MimlSvm algorithms based on a simple degeneration
strategy, and experiments show that solving problems involving complicated
objects with multiple semantic meanings in the MIML framework can lead to good
performance. Considering that the degeneration process may lose information, we
propose the D-MimlSvm algorithm which tackles MIML problems directly in a
regularization framework. Moreover, we show that even when we do not have
access to the real objects and thus cannot capture more information from real
objects by using the MIML representation, MIML is still useful. We propose the
InsDif and SubCod algorithms. InsDif works by transforming single-instances
into the MIML representation for learning, while SubCod works by transforming
single-label examples into the MIML representation for learning. Experiments
show that in some tasks they are able to achieve better performance than
learning the single-instances or single-label examples directly.Comment: 64 pages, 10 figures; Artificial Intelligence, 201
Exploiting Synergy Between Ontologies and Recommender Systems
Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured
Exploiting synergy between ontologies and recommender systems
Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations.Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain.
This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured
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