Ontology-based semantic similarity: A new feature-based approach

Estimation of the semantic likeness between words is of great importance in many applications dealing with textual data such as natural language processing, knowledge acquisition and information retrieval. Semantic similarity measures exploit knowledge sources as the base to perform the estimations. In recent years, ontologies have grown in interest thanks to global initiatives such as the Semantic Web, offering an structured knowledge representation. Thanks to the possibilities that ontologies enable regarding semantic interpretation of terms many ontology-based similarity measures have been developed. According to the principle in which those measures base the similarity assessment and the way in which ontologies are exploited or complemented with other sources several families of measures can be identified. In this paper, we survey and classify most of the ontology-based approaches developed in order to evaluate their advantages and limitations and compare their expected performance both from theoretical and practical points of view. We also present a new ontology-based measure relying on the exploitation of taxonomical features. The evaluation and comparison of our approach’s results against those reported by related works under a common framework suggest that our measure provides a high accuracy without some of the limitations observed in other works.     

Evolving the Topology and the Weights of Neural Networks Using a Dual Representation

Evolutionary computation is a class of global search techniques based on the learning process of a population of potential solutions to a given problem, that has been successfully applied to a variety of problems. In this paper a new approach to the construction of neural networks based on evolutionary computation is presented. A linear chromosome combined to a graph representation of the network are used by genetic operators, which allow the evolution of the architecture and the weights simultaneously without the need of local weight optimization. This paper describes the approach, the operators and reports results of the application of this technique to several binary classification problems.     

ECOC-DRF: Discriminative random fields based on error correcting output codes

We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments.     

A quantitative feedback solution to the multivariable tracking error problem

This paper introduces a novel solution for the multi‐input multi‐output (MIMO) quantitative feedback theory control design problem with tracking error specifications. Looking for a minimum controller overdesign, the technique finds new controller quantitative feedback theory bounds based on necessary and sufficient conditions for the existence of suitable associated prefilter matrix elements. It improves previous approaches to the subject and includes (i) the possibility of a free selection of the nominal plant, (ii) a less conservative application of the Schwartz inequality to decisively reduce the potential controller overdesign, (iii) a methodology to design independently the elements of the prefilter matrix, and (iv) a scope of application to both sequential and nonsequential MIMO controller design methods. The benefits of the new control design technique are illustrated by means of two examples. The first one, a standard 2 × 2 MIMO problem, is provided for comparison purposes with previous approaches. The second example, included as a major control challenge, deals with a well‐known demanding distillation column benchmark problem. Copyright © 2013 John Wiley & Sons, Ltd.     

Output feedback of Markov jump linear systems with no mode observation: An automotive throttle application

The note presents an output feedback control strategy for Markov jump linear systems with no mode observation. Based on minimizing a finite‐time quadratic cost, we derive an algorithm that generates output feedback gains that satisfy a necessary optimality condition. These gains can be computed off‐line relying only on the initial condition of the system. This result expands a previous one from the literature that considered state‐feedback only. To illustrate the usefulness of the approach, real‐time laboratory experiments were performed to control an automotive electronic throttle valve subject to Markov‐driven voltage fluctuations. Copyright © 2015 John Wiley & Sons, Ltd.     

Analytical formulation to compute QFT bounds: The envelope method

This paper describes an analytical formulation to compute quantitative feedback theory (QFT) bounds in one‐degree‐of‐freedom feedback control problems. The new approach is based on envelope curves and shows that a QFT control specification can be expressed as a family of circumferences. Then, the controller bound is defined by the envelope curve of this family and can be obtained as an analytical function. This offers the possibility of studying the QFT bounds in an analytical way with several useful properties. Gridding methods are avoided, resulting in a lower computational effort procedure. The new formulation improves the accuracy of previous methods and allows the designer to calculate multivalued bounds. Copyright © 2009 John Wiley & Sons, Ltd.     

Discovering social structures of local influence by using tweetStimuli

Information diffusion in large-scale networks has been studied to identify the users influence. The influence has been targeted as a key feature either to reach large populations or influencing public opinion. Through the use of micro-blogs, such as Twitter, global influencers have been identified and ranked based on message propagation (retweets). In this paper, a new application is presented, which allows to find first and classify then the local influence on Twitter: who have influenced you and who have been influenced by you. Until now, social structures of tweets’ original authors that have been either retweeted or marked as favourites are unobservable. Throughout this application, these structures can be discovered and they reveal the existence of communities formed by users of similar profile (that are connected among them) interrelated with other similar profile users’ communities.     

Ontology-driven web-based semantic similarity

Estimation of the degree of semantic similarity/distance between concepts is a very common problem in research areas such as natural language processing, knowledge acquisition, information retrieval or data mining. In the past, many similarity measures have been proposed, exploiting explicit knowledge—such as the structure of a taxonomy—or implicit knowledge—such as information distribution. In the former case, taxonomies and/or ontologies are used to introduce additional semantics; in the latter case, frequencies of term appearances in a corpus are considered. Classical measures based on those premises suffer from some problems: in the first case, their excessive dependency of the taxonomical/ontological structure; in the second case, the lack of semantics of a pure statistical analysis of occurrences and/or the ambiguity of estimating concept statistical distribution from term appearances. Measures based on Information Content (IC) of taxonomical concepts combine both approaches. However, they heavily depend on a properly pre-tagged and disambiguated corpus according to the ontological entities in order to compute accurate concept appearance probabilities. This limits the applicability of those measures to other ontologies –like specific domain ontologies- and massive corpus –like the Web-. In this paper, several of the presented issues are analyzed. Modifications of classical similarity measures are also proposed. They are based on a contextualized and scalable version of IC computation in the Web by exploiting taxonomical knowledge. The goal is to avoid the measures’ dependency on the corpus pre-processing to achieve reliable results and minimize language ambiguity. Our proposals are able to outperform classical approaches when using the Web for estimating concept probabilities.     

Traffic sign recognition system with β -correction

Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success.     

Analysis of the HLA class I associated peptide repertoire in a hepatocellular carcinoma cell line reveals tumor-specific peptides as putative targets for immunotherapy

HLA class I molecules present peptides on the cell surface to CD8(+) T cells. The repertoire of peptides that associate to class I molecules represents the cellular proteome. Therefore, cells expressing different proteomes could generate different class I-associated peptide repertoires. A large number of peptides have been sequenced from HLA class I alleles, mostly from lymphoid cells. On the other hand, T cell immunotherapy is a goal in the fight against cancer, but the identification of T cell epitopes is a laborious task. Proteomic techniques allow the definition of putative T cell epitopes by the identification of HLA natural ligands in tumor cells. In this study, we have compared the HLA class I-associated peptide repertoire from the hepatocellular carcinoma (HCC) cell line SK-Hep-1 with that previously described from lymphoid cells. The analysis of the peptide pool confirmed that, as expected, the peptides from SK-Hep-1 derive from proteins localized in the same compartments as in lymphoid cells. Within this pool, we have identified 12 HLA class I peptides derived from HCC-related proteins. This confirms that tumor cell lines could be a good source of tumor associated antigens to be used, together with MS, to define putative epitopes for cytotoxic T cells from cancer patients.