A Rule-Based Morphosemantic Analyzer for French for a Fine-Grained Semantic Annotation of Texts

2013 ◽  
pp. 92-114 ◽  
Author(s):  
Fiammetta Namer
Keyword(s):  
Semantic Annotation ◽  
Rule Based ◽  
Fine Grained

scholarly journals PLATFORMIZING KNOWLEDGE: MESS AND MEANING IN WEB 3.0 INFRASTRUCTURES

2020 ◽  
Author(s):  
Andrew Iliadis ◽  
Wesley Stevens ◽  
Jean-Christophe Plantin ◽  
Amelia Acker ◽  
Huw Davies ◽  
...  
Keyword(s):  
Semantic Web ◽  
Information Exchange ◽  
Semantic Annotation ◽  
Data Access ◽  
Web 3.0 ◽  
Fine Grained ◽  
The Status ◽  
Nature Of Information ◽  
Data Portability ◽  
Machine Readable

This panel focuses on the way that platforms have become key players in the representation of knowledge. Recently, there have been calls to combine infrastructure and platform-based frameworks to understand the nature of information exchange on the web through digital tools for knowledge sharing. The present panel builds and extends work on platform and infrastructure studies in what has been referred to as “knowledge as programmable object” (Plantin, et al., 2018), specifically focusing on how metadata and semantic information are shaped and exchanged in specific web contexts. As Bucher (2012; 2013) and Helmond (2015) show, data portability in the context of web platforms requires a certain level of semantic annotation. Semantic interoperability is the defining feature of so-called "Web 3.0"—traditionally referred to as the semantic web (Antoniou et al, 2012; Szeredi et al, 2014). Since its inception, the semantic web has privileged the status of metadata for providing the fine-grained levels of contextual expressivity needed for machine-readable web data, and can be found in products as diverse as Google's Knowledge Graph, online research repositories like Figshare, and other sources that engage in platformizing knowledge. The first paper in this panel examines the international Schema.org collaboration. The second paper investigates the epistemological implications when platforms organize data sharing. The third paper argues for the use of patents to inform research methodologies for understanding knowledge graphs. The fourth paper discusses private platforms’ extraction and collection of user metadata and the enclosure of data access.

scholarly journals Hybrid Semantic Annotation: Rule-based and Manual Annotation of the Open American National Corpus with Top-Level Ontology

Abakós ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 64-78
Author(s):  
Guidson Coelho de Andrade ◽  
Alcione de Paiva Oliveira ◽  
Alexandra Moreira
Keyword(s):  
Semantic Annotation ◽  
Manual Annotation ◽  
Rule Based ◽  
National Corpus

O processamento de linguagem natural ainda enfrenta o desafio de fazer com que as máquinas compreendam o significado contido nas palavras que ocorrem em uma frase. A anotação semântica ajuda nesse processo adicionando metadados que atribuem significado aos lexemas. Existem diversos aspectos semânticos que podem ser anotados, tais como função, papel semântico e categorias ontológicas. As categorias ontológicas de nível superior adicionam informações sobre a natureza do conceito denotado pelo lexema e permitem eliminar ambiguidades. A proposta de trabalho é uma abordagem híbrida de anotação semântica baseada em ontologias de nível topo aplicadas a um corpus em inglês americano. A pesquisa é dividida em duas etapas de anotação, ambas usando as categorias de alto nível topo do Schema.org como rótulos de anotação. Na primeira etapa é criado um anotador baseado em regras, e na segunda etapa é feita uma anotação manual para correção e adição de rótulos no corpus anotado na etapa anterior. A contribuição deste trabalho é a geração de um corpus anotado que pode ser usado no treinamento de anotadores automáticos. 

Representation and Display of Digital Images of Cultural Heritage: A Semantic Enrichment Approach

2021 ◽  
Vol 48 (3) ◽  
pp. 231-247
Author(s):  
Xu Tan ◽  
Xiaoxi Luo ◽  
Xiaoguang Wang ◽  
Hongyu Wang ◽  
Xilong Hou
Keyword(s):  
Cultural Heritage ◽  
Digital Images ◽  
Semantic Network ◽  
Semantic Representation ◽  
Semantic Annotation ◽  
Visual Display ◽  
Semantic Content ◽  
Entity Recognition ◽  
Semantic Enrichment ◽  
Fine Grained

Digital images of cultural heritage (CH) contain rich semantic information. However, today’s semantic representations of CH images fail to fully reveal the content entities and context within these vital surrogates. This paper draws on the fields of image research and digital humanities to propose a systematic methodology and a technical route for semantic enrichment of CH digital images. This new methodology systematically applies a series of procedures including: semantic annotation, entity-based enrichment, establishing internal relations, event-centric enrichment, defining hierarchy relations between properties text annotation, and finally, named entity recognition in order to ultimately provide fine-grained contextual semantic content disclosure. The feasibility and advantages of the proposed semantic enrichment methods for semantic representation are demonstrated via a visual display platform for digital images of CH built to represent the Wutai Mountain Map, a typical Dunhuang mural. This study proves that semantic enrichment offers a promising new model for exposing content at a fine-grained level, and establishing a rich semantic network centered on the content of digital images of CH.

A RULE-BASED APPROACH FOR SEMANTIC ANNOTATION EVOLUTION

2007 ◽  
Vol 23 (3) ◽  
pp. 320-338 ◽  
Author(s):  
P.-H. Luong ◽  
R. Dieng-Kuntz
Keyword(s):  
Semantic Annotation ◽  
Rule Based ◽  
Rule Based Approach

scholarly journals SBML Level 3 package: Multistate, Multicomponent and Multicompartment Species, Version 1, Release 1

2018 ◽  
Vol 15 (1) ◽  
Author(s):  
Fengkai Zhang ◽  
Martin Meier-Schellersheim
Keyword(s):  
Plasma Membrane ◽  
Spatial Information ◽  
Reaction Networks ◽  
Rule Based ◽  
Fine Grained ◽  
Multiple Components ◽  
Level 3 ◽  
Type Concept ◽  
Definition Of ◽  
Molecular Components

AbstractRule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains (components) and the states such as phosphorylation and binding status of the involved components. Fine-grained spatial information such as the locations of the molecular components relative to a membrane (e.g. whether a modeled molecular domain is embedded into the inner leaflet of the cellular plasma membrane) can also be provided. Through wildcards representing component states entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states and multiple compartments. The SBML Level 3 Multi Package (Multistate, Multicomponent and Multicompartment Species Package for SBML Level 3) extends the SBML Level 3 core with the “type” concept in the Species and Compartment classes and therefore reaction rules may contain species that can be patterns and be in multiple locations in reaction rules. Multiple software tools such as Simmune and BioNetGen support the SBML Level 3 Multi package that thus also becomes a medium for exchanging rule-based models.

Data modeling and evaluation of deep semantic annotation for cultural heritage images

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaoguang Wang ◽  
Ningyuan Song ◽  
Xuemei Liu ◽  
Lei Xu
Keyword(s):  
Cultural Heritage ◽  
Organization Theory ◽  
Semantic Annotation ◽  
Information Organization ◽  
New Approach ◽  
Content Type ◽  
Semantic Enrichment ◽  
Fine Grained ◽  
Semantic Data ◽  
Image Metadata

PurposeTo meet the emerging demand for fine-grained annotation and semantic enrichment of cultural heritage images, this paper proposes a new approach that can transcend the boundary of information organization theory and Panofsky's iconography theory.Design/methodology/approachAfter a systematic review of semantic data models for organizing cultural heritage images and a comparative analysis of the concept and characteristics of deep semantic annotation (DSA) and indexing, an integrated DSA framework for cultural heritage images as well as its principles and process was designed. Two experiments were conducted on two mural images from the Mogao Caves to evaluate the DSA framework's validity based on four criteria: depth, breadth, granularity and relation.FindingsResults showed the proposed DSA framework included not only image metadata but also represented the storyline contained in the images by integrating domain terminology, ontology, thesaurus, taxonomy and natural language description into a multilevel structure.Originality/valueDSA can reveal the aboutness, ofness and isness information contained within images, which can thus meet the demand for semantic enrichment and retrieval of cultural heritage images at a fine-grained level. This method can also help contribute to building a novel infrastructure for the increasing scholarship of digital humanities.

A Web Replay Method for Highlights in Soccer Videos

2013 ◽  
Vol 321-324 ◽  
pp. 1209-1212
Author(s):  
Chong Wang ◽  
Yi Xin Ding ◽  
Zheng Yang Ding
Keyword(s):  
Web Service ◽  
Semantic Annotation ◽  
Experimental Results ◽  
Color Model ◽  
Heuristic Rules ◽  
Fine Grained ◽  
Speed Accuracy

A web replay method is proposed in this paper for highlights in soccer videos. Firstly, automatically detection and fine-grained semantic annotation are introduced for soccer highlights. Secondly, an adaptive grass color model and several heuristic rules are used to improve the performance of the method. Finally, a database for highlights is established for web service. Experimental results show that the proposed method has excellent analyzing speed, accuracy and practicability.

scholarly journals A structural approach to the automatic adjudication of word sense disagreements

2008 ◽  
Vol 14 (4) ◽  
pp. 547-573 ◽  
Author(s):  
ROBERTO NAVIGLI
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Semantic Annotation ◽  
Research Community ◽  
Word Sense ◽  
Lexical Database ◽  
Specific Kind ◽  
Short Term ◽  
Fine Grained ◽  
Lexical Chains

AbstractThe semantic annotation of texts with senses from a computational lexicon is a complex and often subjective task. As a matter of fact, the fine granularity of the WordNet sense inventory [Fellbaum, Christiane (ed.). 1998.WordNet: An Electronic Lexical DatabaseMIT Press], ade factostandard within the research community, is one of the main causes of a low inter-tagger agreement ranging between 70% and 80% and the disappointing performance of automated fine-grained disambiguation systems (around 65% state of the art in the Senseval-3 English all-words task). In order to improve the performance of both manual and automated sense taggers, either we change the sense inventory (e.g. adopting a new dictionary or clustering WordNet senses) or we aim at resolving the disagreements between annotators by dealing with the fineness of sense distinctions. The former approach is not viable in the short term, as wide-coverage resources are not publicly available and no large-scale reliable clustering of WordNet senses has been released to date. The latter approach requires the ability to distinguish between subtle or misleading sense distinctions. In this paper, we propose the use of structural semantic interconnections – a specific kind of lexical chains – for the adjudication of disagreed sense assignments to words in context. The approach relies on the exploitation of the lexicon structure as a support to smooth possible divergencies between sense annotators and foster coherent choices. We perform a twofold experimental evaluation of the approach applied to manual annotations from the SemCor corpus, and automatic annotations from the Senseval-3 English all-words competition. Both sets of experiments and results are entirely novel: structural adjudication allows to improve the state-of-the-art performance in all-words disambiguation by 3.3 points (achieving a 68.5% F1-score) and attains figures around 80% precision and 60% recall in the adjudication of disagreements from human annotators.

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