Semantic Similarity of Ontology Instances Tailored on the Application Context

Albertoni, Riccardo; Martino, Monica De

doi:10.1007/11914853_66

Cited by 28 publications

(13 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach can be implemented to take into account the indirect properties of compared elements, e.g., properties induced by the elements associated to the element that we want to characterise. Albertoni and De Martino (2006) extended the formal framework proposed in Ehrig et al (2004) to allow for the consideration of some indirect properties. This framework is dedicated to instance comparison.…”

Section: Consideration Of Indirect Properties Of Elementsmentioning

confidence: 99%

Semantic Similarity from Natural Language and Ontology Analysis

Harispe

Ranwez

Janaqi

et al. 2015

Synthesis Lectures on Human Language Technologies

166

102

View full text Add to dashboard Cite

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

show abstract

Section: Consideration Of Indirect Properties Of Elementsmentioning

confidence: 99%

Semantic Similarity from Natural Language and Ontology Analysis

Harispe

Ranwez

Janaqi

et al. 2015

Synthesis Lectures on Human Language Technologies

166

102

View full text Add to dashboard Cite

show abstract

“…Cruz, Sunna et al [4] describe AgreementMaker, a visual tool that provides a user with the ability to perform mappings between ontologies using a multifaceted strategy involving automated techniques as well as manual specifications. Albertoni et al [25] devised an instance based similarity measure that matches instances of ontological concepts based on two contextual layers: an ontology context, which is based on a comparison of the concepts" depth in a structured hierarchy as well as the number of attributes and relations they share, and an application context, which uses instance paths and set of predefined comparison operations between concepts to perform a match based on the specific needs of the user. Janowicz and Wilkes [24] describe SIM-DL A , a DL based instance similarity measure that matches instances from a source concept, specified as a user query, with the instances from all target concepts that can satisfy the query.…”

Section: Related Workmentioning

confidence: 99%

“…Semantic similarity in the geospatial domain has been successfully applied to numerous information retrieval and ranking problems, including geolocation [29], text classification [30], geospatial tagging, land cover similarity [34], ontology alignment [4,24,25,31,38,44], recreational tasks like route planning for mountain climbing [20], more serious tasks like emergency response decision making [22] and much more. Furthermore, the success of the geospatial Semantic Web depends very much on semantic similarity algorithms being able to determine commonalities and differences between geospatial data and their data models [28].…”

Section: Introductionmentioning

confidence: 99%

“…Other similarity methods, such as those that use description logic (DL) [31], NLP [35] or network based matching [25], require a strictly defined set of relationships between concepts and attributes. For example, calculating the difference in depths between two concepts (as one would do in network-based approaches), or determining a common parent between two concepts (as one might do in DL approaches) is only possible if the concepts are represented in a hierarchy, such as an ontology.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced geographically typed semantic schema matching

Partyka¹,

Parveen²,

Khan³

et al. 2011

Journal of Web Semantics

View full text Add to dashboard Cite

“…Besides representation, context is a major challenge for similarity. In most cases, meaningful measures cannot be defined without specifying a context in which similarity is measured [6,17,18,19,20].…”

Section: Semantic Similarity Measurementmentioning

confidence: 99%

SIM-DLA: A Novel Semantic Similarity Measure for Description Logics Reducing Inter-concept to Inter-instance Similarity

Janowicz

Wilkes

2009

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. While semantic similarity plays a crucial role for human categorization and reasoning, computational similarity measures have also been applied to fields such as semantics-based information retrieval or ontology engineering. Several measures have been developed to compare concepts specified in various description logics. In most cases, these measures are either structural or require a populated ontology. Structural measures fail with an increasing expressivity of the used description logic, while several ontologies, e.g., geographic feature type ontologies, are not populated at all. In this paper, we present an approach to reduce interconcept to inter-instance similarity and thereby avoid the canonization problem of structural measures. The novel approach, called SIM-DLA, reuses existing similarity functions such as co-occurrence or network measures from our previous SIM-DL measure. The required instances for comparison are derived from the completion tree of a slightly modified DL-tableau algorithm as used for satisfiability checking. Instead of trying to find one (clash-free) model, the new algorithm generates a set of proxy individuals used for comparison. The paper presents the algorithm, alignment matrix, and similarity functions as well as a detailed example.

show abstract

Semantic Similarity of Ontology Instances Tailored on the Application Context

Cited by 28 publications

References 8 publications

Semantic Similarity from Natural Language and Ontology Analysis

Semantic Similarity from Natural Language and Ontology Analysis

Enhanced geographically typed semantic schema matching

SIM-DLA: A Novel Semantic Similarity Measure for Description Logics Reducing Inter-concept to Inter-instance Similarity

Contact Info

Product

Resources

About