Thesaurus enrichment for query expansion in audiovisual archives

Hollink, Laura; Malaisé, Veronique; Schreiber, Guus

doi:10.1007/s11042-009-0400-y

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…We say that this is undoubtedly one of the major reasons for a system's poor performance, especially as the outcome of subject searching [3,4]. Agreeing with [5] we also suggest that the Subject facet is the only facet with semantic relations between its terms, making it the most suitable facet for our method and experimental setup.…”

Section: Related Workmentioning

confidence: 80%

Query Expansion of Zero-Hit Subject Searches: Using a Thesaurus in Conjunction with NLP Techniques

Kapidakis

Mastora

Peponakis

2012

Theory and Practice of Digital Libraries

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Abstract. The focus of our study is zero-hit queries in keyword subject searches and the effort of increasing recall in these cases by reformulating and, then, expanding the initial queries using an external source of knowledge, namely a thesaurus. To this end, the objectives of this study are twofold. First, we perform the mapping of query terms to the thesaurus terms. Second, we use the matched terms to expand the user's initial query by taking advantage of the thesaurus relations and implementing natural language processing (NLP) techniques. We report on the overall procedure and elaborate on key points and considerations of each step of the process.Keywords: Query expansion, Thesaurus, Zero-hit queries, NLP techniques IntroductionThe focus of our study is zero-hit queries in keyword subject searches in an effort to increase recall by reformulating and, then, expanding the initial queries using an external source of knowledge, namely a thesaurus, and taking advantage of natural language processing (NLP) techniques. In case of zero-hit queries query expansion methods based on sets of retrieved results (implicit relevance feedback) cannot be implemented. Building on this fact, we chose to use a handmade thesaurus to expand the initial queries taking advantage of the relations identified within a thesaurus' structure without letting the users interfere in the process. In order to proceed to query expansion we first allocate an entry point within the knowledge base, i.e. match the initial queries to a term from the thesaurus. Exact string matching is unlikely to be successful in highly inflectional languages, like Greek, because of the various forms a word can take. Additionally, research has shown that typing errors are also responsible of delivering zero-hit queries [1]. To overcome the identified obstacles we used techniques for natural language processing, namely spelling, lemmatizing, removal of stop words, accent and case processing. The database and the thesaurus underwent the same processing where needed. Finally, we derived candidate expansion terms by considering the related, parallel, narrower and broader terms of the allocated entry point in the thesaurus moving one level towards each direction.The remaining sections elaborate on the overall procedure and report on key points and considerations of each step of the process.

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Section: Related Workmentioning

confidence: 80%

Query Expansion of Zero-Hit Subject Searches: Using a Thesaurus in Conjunction with NLP Techniques

Kapidakis

Mastora

Peponakis

2012

Theory and Practice of Digital Libraries

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“…In [18], the enrichment consists of adding terms to a source thesaurus by exploiting parallel corpora. In [30], WordNet is used to establish hierarchical relations between the source terms. Oliveira and Gomes [47] propose a method to automatically enrich a Portuguese thesaurus with synonyms extracted from dictionaries.…”

Section: Terminology-related Technologymentioning

confidence: 99%

“…This module allows to obtain a list of the most representative terms from a given corpus. After analysing and testing several open source automatic term extraction (ATE) tools, and also proprietary software, as mentioned in Section 2, we chose to implement the TBXTools service 30 [48]. TBXTools is a fast and flexible tool that offers statistical and linguistic approaches to term extraction.…”

Section: Module 1: Terminology Extractionmentioning

confidence: 99%

TermitUp: Generation and enrichment of linked terminologies

Martín-Chozas

Vázquez-Flores

Calleja

et al. 2022

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Domain-specific terminologies play a central role in many language technology solutions. Substantial manual effort is still involved in the creation of such resources, and many of them are published in proprietary formats that cannot be easily reused in other applications. Automatic term extraction tools help alleviate this cumbersome task. However, their results are usually in the form of plain lists of terms or as unstructured data with limited linguistic information. Initiatives such as the Linguistic Linked Open Data cloud (LLOD) foster the publication of language resources in open structured formats, specifically RDF, and their linking to other resources on the Web of Data. In order to leverage the wealth of linguistic data in the LLOD and speed up the creation of linked terminological resources, we propose TermitUp, a service that generates enriched domain specific terminologies directly from corpora, and publishes them in open and structured formats. TermitUp is composed of five modules performing terminology extraction, terminology post-processing, terminology enrichment, term relation validation and RDF publication. As part of the pipeline implemented by this service, existing resources in the LLOD are linked with the resulting terminologies, contributing in this way to the population of the LLOD cloud. TermitUp has been used in the framework of European projects tackling different fields, such as the legal domain, with promising results. Different alternatives on how to model enriched terminologies are considered and good practices illustrated with examples are proposed.

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“…Hierarchical mesh segmentation method [4] which summarizes the conventional way of achieving semantic annotation is adopted by a system that collects personnel and company information via natural language processing, as well as many other applications that focus on ontological annotation of various kinds of documents. Hollink [12] annotates paintings based on features such as artist name, painting style, etc. However, because this system annotates directly to the image document, it lacks the ability to query semantic features from the paintings.…”

Section: Annotation Based On Semantic Relationshipmentioning

confidence: 99%

Semantic annotation for web3D scene based on three-layer ontology

Cai

Shi

et al. 2015

ICA

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Semantic annotation provides a fundamental technology for web-based visualization application. However, the lack of automatic reasoning ability makes annotation task both boring and time-consuming when mass 3D models occurs. By means of three-layer ontology based on X3D, a comprehensive approach is proposed to implement semi-automatic semantic annotation in web3D scene. Firstly, three-layer ontology including general ontology, domain ontology and scene ontology is constructed by combining X3D instances with general knowledge, which forms a comprehensive knowledge representation for semantic annotation. Then, based on X3D, features extraction, node reduction and node matching is carried out to implement part-annotation of a certain scene. Next, based on the rules defined, annotation spread mechanism is carried out to implement automatic annotation. Lastly, a case study based on implemented prototype system is also provided to demonstrate the process of semantic annotation. The result shows that the proposed approach can help to implement semantic annotation effectively.

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Thesaurus enrichment for query expansion in audiovisual archives

Cited by 5 publications

References 15 publications

Query Expansion of Zero-Hit Subject Searches: Using a Thesaurus in Conjunction with NLP Techniques

Query Expansion of Zero-Hit Subject Searches: Using a Thesaurus in Conjunction with NLP Techniques

TermitUp: Generation and enrichment of linked terminologies

Semantic annotation for web3D scene based on three-layer ontology

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