Information Retrieval by Semantic Similarity

Hliaoutakis, Angelos; Varelas, Giannis; Voutsakis, Epimenidis; Petrakis, Euripides G. M.; Milios, Evangelos

doi:10.4018/jswis.2006070104

Cited by 171 publications

(72 citation statements)

References 36 publications

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“…A better, more semantically aware similarity measure, such as the one described in [13], is likely to have a significant impact on the overall accuracy. To accommodate such a metric, the extracted semantic properties need to be accurately linked to the Semantic Web.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Automatic Discovery of High-Level Provenance Using Semantic Similarity

Nies

Coppens

Deursen

et al. 2012

Lecture Notes in Computer Science

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Abstract. As interest in provenance grows among the Semantic Web community, it is recognized as a useful tool across many domains. However, existing automatic provenance collection techniques are not universally applicable. Most existing methods either rely on (low-level) observed provenance, or require that the user discloses formal workflows. In this paper, we propose a new approach for automatic discovery of provenance, at multiple levels of granularity. To accomplish this, we detect entity derivations, relying on clustering algorithms, linked data and semantic similarity. The resulting derivations are structured in compliance with the Provenance Data Model (PROV-DM). While the proposed approach is purposely kept general, allowing adaptation in many use cases, we provide an implementation for one of these use cases, namely discovering the sources of news articles. With this implementation, we were able to detect 73% of the original sources of 410 news stories, at 68% precision. Lastly, we discuss possible improvements and future work.

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Section: Discussion and Future Workmentioning

confidence: 99%

Automatic Discovery of High-Level Provenance Using Semantic Similarity

Nies

Coppens

Deursen

et al. 2012

Lecture Notes in Computer Science

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“…The evaluation of the semantic similarity methods indicated that this method is particularly effective, achieving up to 73% correlation with results obtained by humans [34]. An important observation and a desirable property of this method is that it tends to assign higher similarity to terms which are close together (in terms of path length) and lower in the hierarchy (more specific terms), than to terms which are equally close together but higher in the hierarchy (more general terms).…”

Section: The Amtex Methodsmentioning

confidence: 99%

“…Documents are represented by term vectors produced by AMTEx (v2.0) and MMTx respectively. Document matching is performed by Vector Space Model (VSM, [34]). Both methods (i.e., retrieval by AMTEx and MMTx vectors) are compared against retrieval using vectors of MEDLINE provided index term sets, i.e.…”

Section: Abstract-based Retrieval Experimentsmentioning

confidence: 99%

The AMTEx approach in the medical document indexing and retrieval application

Hliaoutakis

Zervanou

Petrakis

2009

Data & Knowledge Engineering

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AMTEx is a medical document indexing method, specifically designed for the automatic indexing of documents in large medical collections, such as MEDLINE, the premier bibliographic database of the U.S. National Library of Medicine (NLM).AMTEx combines MeSH, the terminological thesaurus resource of NLM, with a wellestablished method for extraction of terminology, the C/NC-value method. The performance evaluation of two AMTEx configurations is measured against the current state-of-the-art, the MetaMap Transfer (MMTx) method in four experiments, using two types of corpora: a subset of MEDLINE (PMC) full document corpus and a subset of MEDLINE (OHSUMED) abstracts, for each of the indexing and retrieval tasks respectively. The experimental results demonstrate that AMTEx performs better in indexing in 20-50% of the processing time compared to MMTx, while for the retrieval task, AMTEx performs better in the full text (PMC) corpus.

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“…Applications to provide inexact search capabilities over ontologies or to improve classical information retrieval techniques have also been proposed, e.g., (Hliaoutakis, 2005;Varelas et al, 2005;Hliaoutakis et al, 2006;Kiefer et al, 2007;Sy et al, 2012;Pirró, 2012). In this context, semantic measures have also been successfully applied to learning tasks using Semantic Web technologies (D'Amato, 2007).…”

Section: Knowledge Engineering Semantic Web and Linked Datamentioning

confidence: 99%

Semantic Similarity from Natural Language and Ontology Analysis

Harispe

Ranwez

Janaqi

et al. 2015

Synthesis Lectures on Human Language Technologies

157

102

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Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments -most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli.In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning -intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-theart approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesaurus or ontologies.Semantic measures are widely used today to compare units of language, concepts, instances, or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains towards a better understanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterisation of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity towards the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented.keywords: Semantic similarity, semantic relatedness, semantic measures, distributional measures, domain ontology, knowledge-based semantic measure.iii iv

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Information Retrieval by Semantic Similarity

Cited by 171 publications

References 36 publications

Automatic Discovery of High-Level Provenance Using Semantic Similarity

Automatic Discovery of High-Level Provenance Using Semantic Similarity

The AMTEx approach in the medical document indexing and retrieval application

Semantic Similarity from Natural Language and Ontology Analysis

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