28,918 research outputs found
Improving Ontology Recommendation and Reuse in WebCORE by Collaborative Assessments
In this work, we present an extension of CORE [8], a tool for Collaborative Ontology Reuse and Evaluation. The system receives an informal description of a specific semantic domain and determines which ontologies from a repository are the most appropriate to describe the given domain. For this task, the environment is divided into three modules. The first component receives the problem description as a set of terms, and allows the user to refine and enlarge it using WordNet. The second module applies multiple automatic criteria to evaluate the ontologies of the repository, and determines which ones fit best the problem description. A ranked list of ontologies is returned for each criterion, and the lists are combined by means of rank fusion techniques. Finally, the third component uses manual user evaluations in order to incorporate a human, collaborative assessment of the ontologies. The new version of the system incorporates several novelties, such as its implementation as a web application; the incorporation of a NLP module to manage the problem definitions; modifications on the automatic ontology retrieval strategies; and a collaborative framework to find potential relevant terms according to previous user queries. Finally, we present some early experiments on ontology retrieval and evaluation, showing the benefits of our system
Porqpine: a peer-to-peer search engine
In this paper, we present a fully distributed and collaborative search
engine for web pages: Porqpine. This system uses a novel query-based model
and collaborative filtering techniques in order to obtain user-customized
results. All knowledge about users and profiles is stored in each user
node?s application. Overall the system is a multi-agent system that runs on
the computers of the user community. The nodes interact in a peer-to-peer
fashion in order to create a real distributed search engine where
information is completely distributed among all the nodes in the network.
Moreover, the system preserves the privacy of user queries and results by
maintaining the anonymity of the queries? consumers and results? producers.
The knowledge required by the system to work is implicitly caught through
the monitoring of users actions, not only within the system?s interface but
also within one of the most popular web browsers. Thus, users are not
required to explicitly feed knowledge about their interests into the system
since this process is done automatically. In this manner, users obtain the
benefits of a personalized search engine just by installing the application
on their computer. Porqpine does not intend to shun completely conventional
centralized search engines but to complement them by issuing more accurate
and personalized results.Postprint (published version
Context Models For Web Search Personalization
We present our solution to the Yandex Personalized Web Search Challenge. The
aim of this challenge was to use the historical search logs to personalize
top-N document rankings for a set of test users. We used over 100 features
extracted from user- and query-depended contexts to train neural net and
tree-based learning-to-rank and regression models. Our final submission, which
was a blend of several different models, achieved an NDCG@10 of 0.80476 and
placed 4'th amongst the 194 teams winning 3'rd prize
Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models
Many complex multi-target prediction problems that concern large target
spaces are characterised by a need for efficient prediction strategies that
avoid the computation of predictions for all targets explicitly. Examples of
such problems emerge in several subfields of machine learning, such as
collaborative filtering, multi-label classification, dyadic prediction and
biological network inference. In this article we analyse efficient and exact
algorithms for computing the top- predictions in the above problem settings,
using a general class of models that we refer to as separable linear relational
models. We show how to use those inference algorithms, which are modifications
of well-known information retrieval methods, in a variety of machine learning
settings. Furthermore, we study the possibility of scoring items incompletely,
while still retaining an exact top-K retrieval. Experimental results in several
application domains reveal that the so-called threshold algorithm is very
scalable, performing often many orders of magnitude more efficiently than the
naive approach
Web Service Discovery Based on Past User Experience
Web service technology provides a way for simplifying interoperability among different organizations. A piece of functionality available as a web service can be involved in a new business process. Given the steadily growing number of available web services, it is hard for developers to find services appropriate for their needs. The main research efforts in this area are oriented on developing a mechanism for semantic web service description and matching. In this paper, we present an alternative approach for supporting users in web service discovery. Our system implements the implicit culture approach for recommending web services to developers based on the history of decisions made by other developers with similar needs. We explain the main ideas underlying our approach and report on experimental results
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