Measuring Semantic Similarity between Named Entities by Searching the Web Directory

Ligozat, Anne-Laure; Grau, Brigitte; Vilnat, Anne; Robba, Isabelle; Grappy, Arnaud

doi:10.1109/wi.2007.70

Cited by 20 publications

(12 citation statements)

References 8 publications

(14 reference statements)

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“…In [19], an unsupervised procedure for discovering, disambiguating, and linking named entities with the aid of WordNet was introduced. A method was proposed in [20] that exploits the semantic knowledge embedded in the ODP to compute the semantic similarity between named entities to be used for disambiguation. Another system in [21] proposed another measure for computing the semantic similarity between named entities by comparing the semantic similarity of their contexts after relying on WordNet.…”

Section: Related Work and Discussionmentioning

confidence: 99%

Matching named entities with the aid of Wikipedia

Bawakid¹,

Oussalah²,

Afzal³

et al. 2015

Turk J Elec Eng & Comp Sci

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Abstract:In this paper we propose a novel framework using features extracted from Wikipedia for the task of matching named entities. We present how the employed features are extracted from Wikipedia and how its structure is utilized. We describe how a term-concepts table constructed from Wikipedia and the redirect links are integrated in the framework. In addition, the internal links within Wikipedia along with the categories structure are also used to compute the relatedness between concepts. We evaluate the built framework and report its performance in the applications of word sense disambiguation and named entities matching. The system performance is compared against other learning-based stateof-the-art systems and its reported results are found to be competitive. We also present a method in this paper for named entities matching that is context-independent and compare its results with those of other systems.

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Section: Related Work and Discussionmentioning

confidence: 99%

Matching named entities with the aid of Wikipedia

Bawakid¹,

Oussalah²,

Afzal³

et al. 2015

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…www.ijacsa.thesai.org Definition: Let ne be a named entity in Wikipedia (WP) belonging to any of the three types, Person (P), Location (L) and Organization (O). If XITA denotes infobox template attributes 4 of type X and IA(ne) is the infobox attributes extracted from WP article associated with ne, then the classifier identifies ne type according to quantification (1).…”

Section: B Named Entities In Wikipediamentioning

confidence: 99%

“…However, for the actual named entity extraction, a local access is made to a downloaded Wikipedia xml dump of 4 These are the core attributes used for matching February 2014. In implementing the query access method, this study partially adapts the Wikipedia Automated Interface [24] while the local access to the Wikipedia Dump is built on a MediaWiki dump Files Processing Tool [25].…”

Section: B Accessing Wikipedia Databasementioning

confidence: 99%

“…An improvement of named entity coverage are now being made in lexical semantic networks such as ConceptNet 5 [2]. More importantly, constantly updated live online repositories like Wikipedia [3] and Open Directory Project [4] do possess high named entity coverage than the aforementioned resources holding almost all object names. Therefore, in order to automatically handle NER or NEC tasks, the use of such repositories is inevitable.…”

Section: Introductionmentioning

confidence: 99%

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Identifying and Extracting Named Entities from Wikipedia Database Using Entity Infoboxes

Mohamed¹,

Oussalah²

2014

ijacsa

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Abstract-An approach for named entity classification based on Wikipedia article infoboxes is described in this paper. It identifies the three fundamental named entity types, namely; Person, Location and Organization. An entity classification is accomplished by matching entity attributes extracted from the relevant entity article infobox against core entity attributes built from Wikipedia Infobox Templates. Experimental results showed that the classifier can achieve a high accuracy and F-measure scores of 97%. Based on this approach, a database of around 1.6 million 3-typed named entities is created from 20140203 Wikipedia dump. Experiments on CoNLL2003 shared task named entity recognition (NER) dataset disclosed the system's outstanding performance in comparison to three different stateof-the-art systems.

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“…Works as [1] in the medical field, use thesaurus in semantic recovery strategies. Broader approaches, not specific to a particular area of knowledge, often require the implementation of semantic similarity analysis techniques [2] the combination of the use of thesauri to ontology [3]. [4] show, for example, how ontologies can be used as a basis for semantic retrieval of objects based on specific learner's characteristics, leading to adaptive strategies for teaching and learning.…”

Section: Introductionmentioning

confidence: 99%

Semantic Method of Recovering of Medical Articles

Oliveira¹,

Marques²

2015

IJCA

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The amount of information produced today reached an unprecedented contingent in history, which makes it difficult to locate relevant documents in a search. This situation leads to the need to use tools that facilitate the search, making information retrieval area increasingly important in the development of this context. Based on the organization of documents and ontology, information retrieval area has the challenge of getting smarter ways to recovery than just select syntactically documents, exploiting to the full the semantic context when selecting the information. This paper presents a semantic enrichment method that seeks to improve the quality of results when querying a database of medical articles. The proposed method performs a search on the repository of articles that is submitted to latent semantic analysis together with the National Cancer Institute (NCI) ontology and the lexical WordNet ontology database. After this joint treatment, the semantic relationship of those new terms to the survey conducted in the context is performed in order to improve the accuracy of recovery and enabling the retrieve of more relevant articles regarding the search.

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Measuring Semantic Similarity between Named Entities by Searching the Web Directory

Cited by 20 publications

References 8 publications

Matching named entities with the aid of Wikipedia

Matching named entities with the aid of Wikipedia

Identifying and Extracting Named Entities from Wikipedia Database Using Entity Infoboxes

Semantic Method of Recovering of Medical Articles

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