Ontology-Based MEDLINE Document Classification

An increasing and overwhelming amount of biomedical information is available in the research literature mainly in the form of free-text. Biologists need tools that automate their information search and deal with the high volume and ambiguity of free-text. Ontologies can help automatic information processing by providing standard concepts and information about the relationships between concepts. The Medical Subject Headings (MeSH) ontology is already available and used by MEDLINE indexers to annotate the conceptual content of biomedical articles. This paper presents a domain-independent method that uses the MeSH ontology inter-concept relationships to extend the existing MeSH-based representation of MEDLINE documents. The extension method is evaluated within a document triage task organized by the Genomics track of the 2005 Text REtrieval Conference (TREC). Our method for extending the representation of documents leads to an improvement of 17% over a non-extended baseline in terms of normalized utility, the metric defined for the task. The SVMlight software is used to classify documents

Semantics-based selection of everyday concepts in visual lifelogging

Concept-based indexing, based on identifying various semantic concepts appearing in multimedia, is an attractive option for multimedia retrieval and much research tries to bridge the semantic gap between the media’s low-level features and high-level semantics. Research into concept-based multimedia retrieval has generally focused on detecting concepts from high quality media such as broadcast TV or movies, but it is not well addressed in other domains like lifelogging where the original data is captured with poorer quality. We argue that in noisy domains such as lifelogging, the management of data needs to include semantic reasoning in order to deduce a set of concepts to represent lifelog content for applications like searching, browsing or summarisation. Using semantic concepts to manage lifelog data relies on the fusion of automatically-detected concepts to provide a better understanding of the lifelog data. In this paper, we investigate the selection of semantic concepts for lifelogging which includes reasoning on semantic networks using a density-based approach. In a series of experiments we compare different semantic reasoning approaches and the experimental evaluations we report on lifelog data show the efficacy of our approach

Proceedings of the Workshop Semantic Content Acquisition and Representation (SCAR) 2007

This is the proceedings of the Workshop on Semantic Content Acquisition and Representation, held in conjunction with NODALIDA 2007, on May 24 2007 in Tartu, Estonia.</p

Text Summarization Techniques: A Brief Survey

In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.Comment: Some of references format have update

Improving approximation of domain-focused, corpus-based, lexical semantic relatedness

Semantic relatedness is a measure that quantifies the strength of a semantic link between two concepts. Often, it can be efficiently approximated with methods that operate on words, which represent these concepts. Approximating semantic relatedness between texts and concepts represented by these texts is an important part of many text and knowledge processing tasks of crucial importance in many domain-specific scenarios. The problem of most state-of-the-art methods for calculating domain-specific semantic relatedness is their dependence on highly specialized, structured knowledge resources, which makes these methods poorly adaptable for many usage scenarios. On the other hand, the domain knowledge in the fields such as Life Sciences has become more and more accessible, but mostly in its unstructured form - as texts in large document collections, which makes its use more challenging for automated processing. In this dissertation, three new corpus-based methods for approximating domain-specific textual semantic relatedness are presented and evaluated with a set of standard benchmarks focused on the field of biomedicine. Nonetheless, the proposed measures are general enough to be adapted to other domain-focused scenarios. The evaluation involves comparisons with other relevant state-of-the-art measures for calculating semantic relatedness and the results suggest that the methods presented here perform comparably or better than other approaches. Additionally, the dissertation also presents an experiment, in which one of the proposed methods is applied within an ontology matching system, DisMatch. The performance of the system was evaluated externally on a biomedically themed ‘Phenotype’ track of the Ontology Alignment Evaluation Initiative 2016 campaign. The results of the track indicate, that the use distributional semantic relatedness for ontology matching is promising, as the system presented in this thesis did stand out in detecting correct mappings that were not detected by any other systems participating in the track. The work presented in the dissertation indicates an improvement achieved w.r.t. the stat-of-the-art through the domain adapted use of the distributional principle (i.e. the presented methods are corpus-based and do not require additional resources). The ontology matching experiment showcases practical implications of the presented theoretical body of work

Content-based ontology ranking

Techniques to rank ontologies are crucial to aid and encourage the re-use of publicly available ontologies. This paper presents a system that obtains a list of ontologies from a search engine that contain the terms provided by a knowledge engineer and ranks them. The ranking of these ontologies will be done according to how many of the concept labels in those ontologies match a set of terms extracted from a corpus of documents related to the domain of knowledge identified by the knowledge engineer's original search terms