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.

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.

Representing and Linking Dunhuang Cultural Heritage Information Resources Using Knowledge Graph

2020 ◽  
Vol 47 (7) ◽  
pp. 604-615
Author(s):  
Xiaoguang Wang ◽  
Wanli Chang ◽  
Xu Tan
Keyword(s):  
Cultural Heritage ◽  
Semantic Representation ◽  
Semantic Annotation ◽  
Visual Display ◽  
Information Resources ◽  
Knowledge Graph ◽  
Semantic Retrieval ◽  
Semantic Relationship ◽  
Mogao Grottoes ◽  
User Friendly

This study employs a knowledge graph approach to realize the representation and association of information resources, promote the research, teaching, and dissemination of Dunhuang cultural heritage (CH). The Dunhuang Mogao Grottoes is a UNESCO world CH site, and digitization of Dunhuang CH has produced a large amount of information resources. However, these digitized resources continue to lack the systematic granular semantic representation required to correlate Dunhuang cultural heritage information (CHI) in order to facilitate efficient research and appreciation. To respond to this need, new approaches for representing CHI are being developed. This study identifies five facets and their semantic relationship to Dunhuang CH, constructs an ontology model to regulate the entities, attributes, and relationships of Dunhuang CH knowledge, and subsequently processes the resulting data using various techniques (such as semantic annotation and entity association) to facilitate rendering the data in a knowledge graph construction. Finally, we constructed a DH-oriented knowledge graph service platform in order to provide a user friendly visual display and semantic retrieval service.

scholarly journals Managing knowledge to enhance learning

2009 ◽  
pp. 103-119
Keyword(s):  
Distance Education ◽  
Semantic Network ◽  
Collaborative Work ◽  
Semantic Networks ◽  
Semantic Content ◽  
Open Learning ◽  
Fine Grained

The article first summarizes reasons why current approaches supporting Open Learning and Distance Education need to be complemented by tools permitting lecturers, researchers and students to cooperatively organize the semantic content of Learning related materials (courses, discussions, etc.) into a fine-grained shared semantic network. This first part of the article also quickly describes the approach adopted to permit such a collaborative work. Then, examples of such semantic networks are presented. Finally, an evaluation of the approach by students is provided and analyzed.

Examining the episodic-semantic interplay during future thinking – A reanalysis of external details

2020 ◽  
Author(s):  
Cherie Strikwerda-Brown ◽  
John Hodges ◽  
Olivier Piguet ◽  
Muireann Irish
Keyword(s):  
Life Story ◽  
Semantic Content ◽  
Structural Mri ◽  
Future Thinking ◽  
Fine Grained ◽  
Differential Vulnerability ◽  
Clinical Disorders ◽  
External Component ◽  
Future Narratives ◽  
General Semantic

Traditional analyses of autobiographical construction have tended to focus on the ‘internal’ or episodic details of the narrative. Contemporary studies employing fine-grained scoring measures, however, reveal the ‘external’ component of autobiographical narratives to contain important information relevant to the individual’s life story. Here, we used the recently developed NExt scoring protocol to explore profiles of external details generated by patients with Alzheimer’s disease (AD) (n = 11) and semantic dementia (SD) (n = 13) on a future thinking task. Voxel-based morphometry analyses of structural MRI were used to determine the neural correlates of external detail profiles in each patient group. Overall, distinct NExt profiles were observed across past and future temporal contexts in AD and SD groups, which involved elevations in external details, in the context of reduced internal details, relative to healthy Controls. Specifically, AD patients provided significantly more General Semantic details compared with Controls during past retrieval, whereas Specific Episode external details were elevated during future simulation. These increased external details within future narratives related to grey matter integrity in medial and lateral frontal regions in AD. By contrast, SD patients displayed an elevation of Specific Episode, Extended Episode, and General Semantic details exclusively during future simulation relative to Controls, which related to integrity of medial and lateral parietal regions. Our findings suggest that the compensatory external details generated during future simulation comprise an array of episodic and semantic details that vary in terms of specificity and self-relevance. Moreover, these profiles appear to be differentially affected depending on the locus of underlying neuropathology in dementia. Adopting a fine-grained approach to external details provides important information regarding the interplay between episodic and semantic content during future stimulation and highlights the differential vulnerability and preservation of distinct components of the constructed narrative in clinical disorders.

scholarly journals Improving Topic Coherence Using Entity Extraction Denoising

2018 ◽  
Vol 110 (1) ◽  
pp. 85-101 ◽  
Author(s):  
Ronald Cardenas ◽  
Kevin Bello ◽  
Alberto Coronado ◽  
Elizabeth Villota
Keyword(s):  
Topic Modeling ◽  
Human Perception ◽  
Relevant Information ◽  
Entity Recognition ◽  
Entity Extraction ◽  
Fine Grained ◽  
Job Advertisement ◽  
Coherence Score ◽  
Probabilistic Topic Modeling ◽  
Promising Solution

Abstract Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method and the evaluation of this model is an interesting problem on its own. Topic interpretability measures have been developed in recent years as a more natural option for topic quality evaluation, emulating human perception of coherence with word sets correlation scores. In this paper, we show experimental evidence of the improvement of topic coherence score by restricting the training corpus to that of relevant information in the document obtained by Entity Recognition. We experiment with job advertisement data and find that with this approach topic models improve interpretability in about 40 percentage points on average. Our analysis reveals as well that using the extracted text chunks, some redundant topics are joined while others are split into more skill-specific topics. Fine-grained topics observed in models using the whole text are preserved.

scholarly journals Representation and Presentation of Culinary Tradition as Cultural Heritage

Heritage ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 612-640
Author(s):  
Nikolaos Partarakis ◽  
Danai Kaplanidi ◽  
Paraskevi Doulgeraki ◽  
Effie Karuzaki ◽  
Argyro Petraki ◽  
...  
Keyword(s):  
Social Sciences ◽  
Knowledge Representation ◽  
Cultural Heritage ◽  
Systematic Approach ◽  
Semantic Representation ◽  
Ethnographic Research ◽  
Abstract Form ◽  
Web Based ◽  
Social Sciences And Humanities ◽  
Authoring Environment

This paper presents a knowledge representation framework and provides tools to allow the representation and presentation of the tangible and intangible dimensions of culinary tradition as cultural heritage including the socio-historic context of its evolution. The representation framework adheres to and extends the knowledge representation standards for the Cultural Heritage (CH) domain while providing a widely accessible web-based authoring environment to facilitate the representation activities. In strong collaboration with social sciences and humanities, this work allows the exploitation of ethnographic research outcomes by providing a systematic approach for the representation of culinary tradition in the form of recipes, both in an abstract form for their preservation and in a semantic representation of their execution captured on-site during ethnographic research.

scholarly journals The Kinematics of Social Action: Visual Signals Provide Cues for What Interlocutors Do in Conversation

2021 ◽  
Vol 11 (8) ◽  
pp. 996
Author(s):  
James P. Trujillo ◽  
Judith Holler
Keyword(s):  
Social Action ◽  
Motion Tracking ◽  
Time Window ◽  
Semantic Content ◽  
Visual Signals ◽  
Manual Annotation ◽  
Conversational Interaction ◽  
Fine Grained ◽  
Social Actions ◽  
Rapid Processing

During natural conversation, people must quickly understand the meaning of what the other speaker is saying. This concerns not just the semantic content of an utterance, but also the social action (i.e., what the utterance is doing—requesting information, offering, evaluating, checking mutual understanding, etc.) that the utterance is performing. The multimodal nature of human language raises the question of whether visual signals may contribute to the rapid processing of such social actions. However, while previous research has shown that how we move reveals the intentions underlying instrumental actions, we do not know whether the intentions underlying fine-grained social actions in conversation are also revealed in our bodily movements. Using a corpus of dyadic conversations combined with manual annotation and motion tracking, we analyzed the kinematics of the torso, head, and hands during the asking of questions. Manual annotation categorized these questions into six more fine-grained social action types (i.e., request for information, other-initiated repair, understanding check, stance or sentiment, self-directed, active participation). We demonstrate, for the first time, that the kinematics of the torso, head and hands differ between some of these different social action categories based on a 900 ms time window that captures movements starting slightly prior to or within 600 ms after utterance onset. These results provide novel insights into the extent to which our intentions shape the way that we move, and provide new avenues for understanding how this phenomenon may facilitate the fast communication of meaning in conversational interaction, social action, and conversation.

Modeling the Cognitive Complexity of Visual Displays

1986 ◽  
Vol 30 (7) ◽  
pp. 675-678 ◽  
Author(s):  
Robert G. Eggleston ◽  
Richard A. Chechile ◽  
Rebecca N. Fleischman
Keyword(s):  
Cognitive Complexity ◽  
Task Difficulty ◽  
Semantic Network ◽  
Visual Display ◽  
Visual Displays

An approach for measuring the cognitive complexity of visual display formats is presented. The approach involves modeling both the knowledge that can be extracted from a format and the knowledge an operator brings to a task. A semantic network formalism is developed to capture task-relevant knowledge, from which four orthogonal predictor measures of cognitive complexity are derived. In an experiment, seven different avionic missions, performed with the aid of a horizontal situation display, were studied, and three of the predictor measures were found to correlate significantly with observed task difficulty. The results indicate that a semantic network formalism can be used to produce an objective metric of format quality in terms of cognitive complexity.

Automated Extraction and Presentation of Data Practices in Privacy Policies

2021 ◽  
Vol 2021 (2) ◽  
pp. 88-110
Author(s):  
Duc Bui ◽  
Kang G. Shin ◽  
Jong-Min Choi ◽  
Junbum Shin
Keyword(s):  
User Study ◽  
Personal Information ◽  
Personal Data ◽  
Neural Model ◽  
Automated Analysis ◽  
Entity Recognition ◽  
Automated System ◽  
Privacy Policies ◽  
Fine Grained ◽  
Data Practices

Abstract Privacy policies are documents required by law and regulations that notify users of the collection, use, and sharing of their personal information on services or applications. While the extraction of personal data objects and their usage thereon is one of the fundamental steps in their automated analysis, it remains challenging due to the complex policy statements written in legal (vague) language. Prior work is limited by small/generated datasets and manually created rules. We formulate the extraction of fine-grained personal data phrases and the corresponding data collection or sharing practices as a sequence-labeling problem that can be solved by an entity-recognition model. We create a large dataset with 4.1k sentences (97k tokens) and 2.6k annotated fine-grained data practices from 30 real-world privacy policies to train and evaluate neural networks. We present a fully automated system, called PI-Extract, which accurately extracts privacy practices by a neural model and outperforms, by a large margin, strong rule-based baselines. We conduct a user study on the effects of data practice annotation which highlights and describes the data practices extracted by PI-Extract to help users better understand privacy-policy documents. Our experimental evaluation results show that the annotation significantly improves the users’ reading comprehension of policy texts, as indicated by a 26.6% increase in the average total reading score.

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