This research addresses the role of lyrics in the music emotion recognition process. Our approach is based on several state of the art features complemented by novel stylistic, structural and semantic features. To evaluate our approach, we created a ground truth dataset containing 180 song lyrics, according to Russell's emotion model. We conduct four types of experiments: regression and classification by quadrant, arousal and valence categories. Comparing to the state of the art features (ngrams - baseline), adding other features, including novel features, improved the F-measure from 69.9, 82.7 and 85.6 percent to 80.1, 88.3 and 90 percent, respectively for the three classification experiments. To study the relation between features and emotions (quadrants) we performed experiments to identify the best features that allow to describe and discriminate each quadrant. To further validate these experiments, we built a validation set comprising 771 lyrics extracted from the AllMusic platform, having achieved 73.6 percent F-measure in the classification by quadrants. We also conducted experiments to identify interpretable rules that show the relation between features and emotions and the relation among features. Regarding regression, results show that, comparing to similar studies for audio, we achieve a similar performance for arousal and a much better performance for valence.

Abstract Ontologising is the task of associating terms, in text, with an ontological representation of their meaning, in an ontology. In this article, we revisit algorithms that have previously been used to ontologise the arguments of semantic relations in a relationless thesaurus, resulting in a wordnet. For increased flexibility, the algorithms do not use the extraction context when selecting the most adequate synsets for each term argument. Instead, they exploit a term-based lexical network which can be established by knowledge extracted automatically, or obtained from the resource the relations are being ontologised to. On the latter idea, we made several experiments to conclude that the algorithms can be used both for wordnet creation and for their enrichment. Besides describing the algorithms with some detail, the aforementioned experiments, which target both English and Portuguese, and their results are reported and discussed.

This document describes the current state of Onto.PT, a new large wordnet for Portuguese, freely available, and created automatically after exploiting and integrating existing lexical resources in a wordnet structure.Besides an overview on Onto.PT, its creation and evaluation, we enumerate the developments of version 0.6.Moreover, we provide a quantitative view on this version, its comparison to other Portuguese wordnets, in terms of contents and size, as well as some details about its global coverage and availability.

Abstract Regarding that information in broad‐coverage knowledge bases, such as thesauri, is usually incomplete, merging information from different sources is an alternative to amplify coverage. We propose a method for the enrichment of a thesaurus with information acquired automatically from dictionaries. First, synonymy pairs are extracted. Then, these pairs are assigned to the most similar candidate synsets. Finally, the remaining pairs are the target of clustering to identify new synsets. After selecting the adequate experimentation settings, this method was applied to enrich a Portuguese thesaurus with synonyms extracted from three dictionaries, which resulted in TRIP, a larger and broader thesaurus with new words and concepts. The steps towards the creation of this new thesaurus and its evaluation are described here.

While working in an IDE, developers typically deal with a large number of different artifacts at the same time. The software development process requires that they repeatedly switch between different artifacts, which often depends on searching for these artifacts in the source code structure. We propose a tool that integrates context-based search and recommendation of source code artifacts in Eclipse. The artifacts are collected from the workspace of the developer and represented using ontologies. A context model of the developer is used to improve search and give recommendations of these artifacts, which are ranked according to their relevance to the developer. The tool was tested by a group of developers and the results show that contextual information has an important role in retrieving relevant information for developers.

A software developer programming in an object-oriented programming language deals with a source code structure that may contain hundreds of source code elements. These elements are commonly related to each other and working on a specific element may require the developer to access other related elements. We propose a recommendation approach that uses the context of the developer to retrieve and rank recommendations of relevant source code elements in the IDE. These recommendations provide a shortcut to reach the desired elements and increase the awareness of the developer in relation to elements that may be of interest in that moment. We have tested our approach with a group of developers and the results show that context has a promising role in predicting and ranking the source code elements needed by a developer at each moment.

In order to deal with ambiguity in natural language, it is common to organise words, according to their senses, in synsets, which are groups of synonymous words that can be seen as concepts. The manual creation of a broad-coverage synset base is a time-consuming task, so we take advantage of dictionary definitions for extracting synonymy pairs and clustering for identifying synsets. Since word senses are not discrete, we create fuzzy synsets, where each word has a membership degree. We report on the results of the creation of a fuzzy synset base for Portuguese, from three electronic dictionaries. The resulting resource is larger than existing hancrafted Portuguese thesauri.

When a semantic network is based on triples relating terms, ambiguity arises as a problem, so we have used these triples to identify clusters, which can be seen as synsets. We report the results of this approach on a synonymy network extracted from a dictionary and additional tests involving manually created thesaurus. Part of the resulting synsets were also evaluated by human subjects.

Many organizations have to manage an increasingly large number of software projects. In many cases, these projects are outsourced to different companies or developed across several departments. This creates a problem because it is increasingly difficult for a project manager, responsible for several projects, to have an accurate view of all activities underway, being able to act proactively before problems occur. In this paper we present an approach and corresponding implementation for effectively tracking and managing multiple projects across an organization, offering an integrated view of the state of projects being implemented. This approach allows to effectively monitoring risks, progress, budget, deliveries, and other critical aspects, allowing responding in real-time. The system was implemented for the European Space Agency, being now successfully used in a number of projects. This paper also presents an accurate view on how software development and tracking takes place in the scope of mission-critical systems.

Most of the approaches for dealing with uncertainty in the Semantic Web rely on the principle that this uncertainty is already asserted. In this paper, we propose a new approach to learn and reason about uncertainty in the Semantic Web. Using instance data, we learn the uncertainty of an OWL ontology, and use that information to perform probabilistic reasoning on it. For this purpose, we use Markov logic, a new representation formalism that combines logic with probabilistic graphical models.

As software systems become bigger and more complex, software developers need to cope with a growing amount of information and knowledge. New supporting toolsfor management of knowledge created during softwaredevelopment and maintenance is a necessity felt in thesoftware industry. The knowledge generated during the software development process can be a valuable asset for a software company. But in order to take advantage of this knowledge, the company must acquire, store andmanage it for reuse. This paper describes SRS (Semantic Reuse System), a system for management and reuse of software development knowledge based on Semantic Web technologies. SRS enables software developers to submit knowledge that the system then analyzes and indexes for later use. We describe how the knowledge is represented and how the reasoning mechanisms support knowledge reuse.