Anastasia Krithara

Ontology population and enrichment: state of the art

Ontology learning is the process of acquiring (constructing or integrating) an ontology (semi-) automatically. Being a knowledge acquisition task, it is a complex activity, which becomes even more complex in the context of the BOEMIE project1, due to the management of multimedia resources and the multi-modal semantic interpretation that they require. The purpose of this chapter is to present a survey of the most relevant methods, techniques and tools used for the task of ontology learning. Adopting a practical perspective, an overview of the main activities involved in ontology learning is presented. This breakdown of the learning process is used as a basis for the comparative analysis of existing tools and approaches. The comparison is done along dimensions that emphasize the particular interests of the BOEMIE project. In this context, ontology learning in BOEMIE is treated and compared to the state of the art, explaining how BOEMIE addresses problems observed in existing systems and contributes to issues that are not frequently considered by existing approaches.

Semi-supervised document classification with a mislabeling error model

This paper investigates a new extension of the Probabilistic Latent Semantic Analysis (PLSA) model [6] for text classification where the training set is partially labeled. The proposed approach iteratively labels the unlabeled documents and estimates the probabilities of its labeling errors. These probabilities are then taken into account in the estimation of the new model parameters before the next round. Our approach outperforms an earlier semi-supervised extension of PLSA introduced by [9] which is based on the use of fake labels. However, it maintains its simplicity and ability to solve multiclass problems. In addition, it gives valuable information about the most uncertain and difficult classes to label. We perform experiments over the 20Newsgroups, WebKB and Reuters document collections and show the effectiveness of our approach over two other semi-supervised algorithms applied to these text classification problems.

Results of the 4th edition of BioASQ Challenge

The goal of this task is to push the research frontier towards hybrid information systems. We aim to promote systems and approaches that are able to deal with the whole diversity of the Web, especially for, but not restricted to, the context of biomedicine. This goal is pursued by the organization of challenges. The fourth challenge, as the previous challenges, consisted of two tasks: semantic indexing and question answering. 16 systems participated by 7 different participating teams for the semantic indexing task. The question answering task was tackled by 37 different systems, developed by 11 different teams. 25 of the systems participated in the phase A of the task, while 12 participated in phase B. 3 of the teams participated in both phases of the question answering task. Overall, as in previous years, the best systems were able to outperform the strong baselines. This suggests that advances over the state of the art were achieved through the BIOASQ challenge but also that the benchmark in itself is very challenging. In this paper, we present the data used during the challenge as well as the technologies which were at the core of the participants’ frameworks.

Splice Site Recognition Using Transfer Learning

In this work, we consider a transfer learning approach based on K-means for splice site recognition. We use different representations for the sequences, based on n-gram graphs. In addition, a novel representation based on the secondary structure of the sequences is proposed. We evaluate our approach on genomic sequence data from model organisms of varying evolutionary distance. The first obtained results indicate that the proposed representations are promising for the problem of splice site recognition.

Analysis and Classification of Constrained DNA Elements with N-gram Graphs and Genomic Signatures

Most common methods for inquiring genomic sequence composition, are based on the bag-of-words approach and thus largely ignore the original sequence structure or the relative positioning of its constituent oligonucleotides. We here present a novel methodology that takes into account both word representation and relative positioning at various lengths scales in the form of n-gram graphs (NGG). We implemented the NGG approach on short vertebrate and invertebrate constrained genomic sequences of various origins and predicted functionalities and were able to efficiently distinguish DNA sequences belonging to the same species (intra-species classification). As an alternative method, we also applied the Genomic Signatures (GS) approach to the same sequences. To our knowledge, this is the first time that GS are applied on short sequences, rather than whole genomes. Together, the presented results suggest that NGG is an efficient method for classifying sequences, originating from a given genome, according to their function.

Learning aspect models with partially labeled data

In this paper, we address the problem of learning aspect models with partially labeled data for the task of document categorization. The motivation of this work is to take advantage of the amount of available unlabeled data together with the set of labeled examples to learn latent models whose structure and underlying hypotheses take more accurately into account the document generation process, compared to other mixture-based generative models. We present one semi-supervised variant of the Probabilistic Latent Semantic Analysis (PLSA) model (Hofmann, 2001). In our approach, we try to capture the possible data mislabeling errors which occur during the training of our model. This is done by iteratively assigning class labels to unlabeled examples using the current aspect model and re-estimating the probabilities of the mislabeling errors. We perform experiments over the 20Newsgroups, WebKB and Reuters document collections, as well as over a real world dataset coming from a Business Group of Xerox and show the effectiveness of our approach compared to a semi-supervised version of Naive Bayes, another semi-supervised version of PLSA and to transductive Support Vector Machines.

An extension of the aspect PLSA model to active and semi-supervised learning for text classification

In this paper, we address the problem of learning aspect models with partially labeled examples We propose a method which benefits from both semi-supervised and active learning frameworks In particular, we combine a semi-supervised extension of the PLSA algorithm [11] with two active learning techniques We perform experiments over four different datasets and show the effectiveness of the combination of the two frameworks.