Semantic User Research Articles

“Out of Love for You”

This article approaches gender and power through the study of emotion as a means of refreshing and updating the study of Rūs gender using Arabic geographical sources. Considering Arabic geographical and historical sources on the Rūs that deal with the concept of funerary sacrifice, in particular those that posit the possibility of male as well as female sacrificial victims, I examine the conditions attached to the practice of sacrificing men in these sources. I do so primarily by focusing on the semantic usages of ḥubb (love) as it relates to the Rūs, exploring the potential for wider emotional exploration within geographical discussions of the Rūs and in the context of their use by researchers today.

Unsupervised mapping and semantic user localisation from first-person monocular video

We propose an unsupervised probabilistic framework for learning a human-centred representation of a person’s environment from first-person video. Specifically, non-geometric maps modelled as hierarchies of probabilistic place graphs and view graphs are learned. Place graphs model a user’s patterns of transition between physical locations whereas view graphs capture an aspect of user behaviour within those locations. Furthermore, we describe an implementation in which the notion of place is divided into stations and the routes that interconnect them. Stations typically correspond to rooms or areas where a user spends time. Visits to stations are temporally segmented based on qualitative visual motion. We describe how to learn maps online in an unsupervised manner, and how to localise the user within these maps. We report experiments on two datasets, including comparison of performance with and without view graphs, and demonstrate better online mapping than when using offline clustering.

DICTIONARY-BASED COMPARATIVE STUDY OF THE CHINESE “个 (GÈ)” AND THE MALAY “BUAH” NUMERAL CLASSIFIERS

Classifiers are used for categorising nouns according to animacy, form, shape, and size. As one of the Austronesian languages, Malay is said to have an extensive numeral classifier system. Past studies have classified these two classifiers as general classifiers; however, an extensive study comparing the use of these two classifiers have not been attempted. This study aims to highlight the similarities and differences by comparing the Chinese classifier “个 (gè)” and the Malay “buah” using evidence from reference classifier dictionaries of the two languages. The findings show that while some similarities exist and both classifiers can be used interchangeably with some nouns, these classifiers are unique and have distinctive semantic usage. Both个 (gè) and buah are used for organisation, products of nature, buildings and places, and artifacts (things created by humans). However, only个 (gè) can be used for organs or body parts, humans, fruits and food, time, directions, and thought and intellectual products. In contrast, only buah is used for transportation, accessories, home furniture and appliances, publications, musical instruments, and performances.

Leveraging Machine Learning to Analyze Semantic User Interactions in Visual Analytics

Leveraging Machine Learning to Analyze Semantic User Interactions in Visual Analytics

Semantics of INTO in Motion Events and Its Vietnamese Equivalents

In the English language, despite its affinity with other spatial prepositions, the preposition into demonstrates versatility by conveying spatial as well as non-spatial meanings contingent upon contextual factors. Throughout the process of English language acquisition and in the context of translation between English and Vietnamese, a diverse array of interpretations of into has been articulated within distinct discursive settings. The primary objective of examining the corpus of into used in The Complete Sherlock Holmes is to elucidate the nuances and distinctions in its semantic usage and subsequently conduct a comparative analysis with its Vietnamese equivalents.

Biomedical semantic text summarizer

BackgroundText summarization is a challenging problem in Natural Language Processing, which involves condensing the content of textual documents without losing their overall meaning and information content, In the domain of bio-medical research, summaries are critical for efficient data analysis and information retrieval. While several bio-medical text summarizers exist in the literature, they often miss out on an essential text aspect: text semantics.ResultsThis paper proposes a novel extractive summarizer that preserves text semantics by utilizing bio-semantic models. We evaluate our approach using ROUGE on a standard dataset and compare it with three state-of-the-art summarizers. Our results show that our approach outperforms existing summarizers.ConclusionThe usage of semantics can improve summarizer performance and lead to better summaries. Our summarizer has the potential to aid in efficient data analysis and information retrieval in the field of biomedical research.

Knowledge-based recommender systems: overview and research directions.

Recommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-based filtering, knowledge-based recommenders exploit semantic user preference knowledge, item knowledge, and recommendation knowledge, to identify user-relevant items which is of specific relevance when dealing with complex and high-involvement items. Such recommenders are primarily applied in scenarios where users specify (and revise) their preferences, and related recommendations are determined on the basis of constraints or attribute-level similarity metrics. In this article, we provide an overview of the existing state-of-the-art in knowledge-based recommender systems. Different related recommendation techniques are explained on the basis of a working example from the domain of survey software services. On the basis of our analysis, we outline different directions for future research.

Your Cursor Reveals: On Analyzing Workers’ Browsing Behavior and Annotation Quality In Crowdsourcing Tasks

In this work, we investigate the connection between browsing behavior and task quality of crowdsourcing workers performing annotation tasks that require information judgements. Such information judgements are often required to derive ground truth answers to information retrieval queries. We explore the use of workers’ browsing behavior to directly determine their annotation result quality. We hypothesize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user attention</i> to be the main factor contributing to a worker’s annotation quality. To predict annotation quality at the task level, we model two aspects of task-specific user attention, also known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">general</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">semantic user attentions</i> . Both aspects of user attention can be modeled using different types of browsing behavior features but most previous research mostly focuses on the former. This work therefore proposes to model semantic user attention by capturing the worker’s understanding of task content using task-semantics specific behavior features. We develop a web-based annotation interface for gathering user behavior data when workers perform a knowledge path retrieval task. With the collected data, we train several prediction models using behavior features corresponding to different aspects of user attention and conduct experiments on a set of annotation tasks performed by 51 Amazon Mechanical Turk workers. We show that the prediction model using both general and semantic user attention features can achieve the best performance of nearly 75% accuracy.

The EPOS multi-disciplinary Data Portal for integrated access to solid Earth science datasets

The European Plate Observing System (EPOS) is a long-term initiative aimed at integrating research infrastructures for solid Earth science in Europe. EPOS provides a sustainable, multidisciplinary user-oriented platform - the EPOS Data Portal - that facilitates data integration, access, use, and re-use, while adhering to the FAIR principles. The paper describes the key governance, community building, and technical aspects for achieving multidisciplinary data integration through the portal. It also outlines the key portal features for aggregating approximately 250 data sources from more than ten different scientific communities. The main architectural concepts underpinning the portal, namely the rich-metadata, the service-driven data provision, and the usage of semantics, are outlined. The paper discusses the challenges encountered during the creation of the portal, describes the community engagement process, and highlights the benefits to the scientific community and society. Future work includes expanding portal functionalities to include data analysis, processing, and visualization and releasing the portal as an open-source software package.

Sound Change, Value of PL-SKT Loanwords and Reflections of Society and Beliefs in the Epic Thao Hung or Cheuang

The objectives of this study were to explore the linguistic changes, to discover the value of Pali (PL) and Sanskrit (SKT) loanwords in an old and important epic work entitled “Thao Hung or Cheuang”, and to describe the society depicted in this work and its beliefs. The conceptual framework of this study consists of (1) usage of PL-SKT words through sound change theories (2) values of the PL-SKT loanwords applied in Thao Hung or Cheuang and (3) reflections on the society and beliefs of the people in this epic. A qualitative method was employed and the data were collected from volumes 1-4 of the Dictionary of the Vocabulary in the Northeastern Thai Literary Work Thao Hung or Thao Cheuang published by the Royal Society of Thailand. The findings revealed that there are 679 PL-SKT loanwords in the work. The vowel and consonant sound changes were made for the sake melodiousness of the poetry of the work. The values of PL-SKT words can be categorized into five dimensions, namely, 1) language usage 2) word usage 3) sound usage 4) semantic usage and 5) aesthetic usage. The work also reflects the society it depicts, namely: urban society on a plateau which is class-based and agricultural, in which there is gender inequality between men and women and marriage between relatives of the ruling class, and in which there are multiple cultural influences. The following two types of beliefs were found in the society, namely: old beliefs and Brahman and Buddhist beliefs.

Automated Generation of Room Usage Semantics from Point Cloud Data

Room usage semantics in models of large indoor environments such as public buildings and business complex are critical in many practical applications, such as health and safety regulations, compliance, and emergency response. Existing models such as IndoorGML have very limited semantic information at room level, and it remains difficult to capture semantic knowledge of rooms in an efficient way. In this paper, we formulate the task of generating rooms usage semantics as a special case of room classification problems. Although methods for room classification tasks have been developed in the field of social robotics studies and indoor maps, they do not deal with room usage and occupancy aspects of semantics, and they ignore the value of furniture objects in understanding room usage. We propose a method for generating room usage semantics based on the spatial configuration of room objects (e.g., furniture, walls, windows, doors). This method uses deep learning architecture to support a room usage classifier that can learn spatial configuration features directly from semantically labelled point cloud (SLPC) data that represent room scenes with furniture objects in place. We experimentally assessed the capacity of our method in classifying rooms in office buildings using the Stanford 3D (S3DIS) dataset. The results showed that our method was able to achieve an overall accuracy of 91% on top-level room categories (e.g., offices, conference rooms, lounges, storage) and above 97% accuracy in recognizing offices and conference rooms. We further show that our classifier can distinguish fine-grained categories of of offices and conference rooms such as shared offices, single-occupancy offices, large conference rooms, and small conference rooms, with comparable intelligence to human coders. In general, our method performs better on rooms with a richer variety of objects than on rooms with few or no furniture objects.

Enhancing Recommender Systems with Semantic User Profiling through Frequent Subgraph Mining on Knowledge Graphs

Recommender systems play a crucial role in personalizing online user experiences by creating user profiles based on user–item interactions and preferences. Knowledge graphs (KGs) are intricate data structures that encapsulate semantic information, expressing users and items in a meaningful way. Although recent deep learning-based recommendation algorithms that embed KGs have demonstrated impressive performance, the richness of semantics and explainability embedded in the KGs are often lost due to the opaque nature of vector representations in deep neural networks. To address this issue, we propose a novel user profiling method for recommender systems that can encapsulate user preferences while preserving the original semantics of the KGs, using frequent subgraph mining. Our approach involves creating user profile vectors from a set of frequent subgraphs that contain information about user preferences and the strength of those preferences, measured by frequency. Subsequently, we trained a deep neural network model to learn the relationship between users and items, thereby facilitating effective recommendations using the neural network’s approximation ability. We evaluated our user profiling methodology on movie data and found that it demonstrated competitive performance, indicating that our approach can accurately represent user preferences while maintaining the semantics of the KGs. This work, therefore, presents a significant step towards creating more transparent and effective recommender systems that can be beneficial for a wide range of applications and readers interested in this field.

Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks

Massive open online courses (MOOCs), which offer open access and widespread interactive participation through the internet, are quickly becoming the preferred method for online and remote learning. Several MOOC platforms offer the service of course recommendation to users, to improve the learning experience of users. Despite the usefulness of this service, we consider that recommending courses to users directly may neglect their varying degrees of expertise. To mitigate this gap, we examine an interesting problem of concept recommendation in this paper, which can be viewed as recommending knowledge to users in a fine-grained way. We put forward a novel approach, termedHinCRec-RL, forConceptRecommendation in MOOCs, which is based onHeterogeneousInformationNetworks andReinforcementLearning. In particular, we propose to shape the problem of concept recommendation within a reinforcement learning framework to characterize the dynamic interaction between users and knowledge concepts in MOOCs. Furthermore, we propose to form the interactions among users, courses, videos, and concepts into aheterogeneous information network (HIN)to learn the semantic user representations better. We then employ an attentional graph neural network to represent the users in the HIN, based on meta-paths. Extensive experiments are conducted on a real-world dataset collected from a Chinese MOOC platform,XuetangX, to validate the efficacy of our proposed HinCRec-RL. Experimental results and analysis demonstrate that our proposed HinCRec-RL performs well when compared with several state-of-the-art models.

The Attributed-noun in The Holy Quran ASemantic and Grammatical Study

Objectives: This research aims to shape the rules of attributed nouns by analyzing and deducing the findings in this field, particularly regarding the application and implementation of genealogical examples found in the Holy Quran. It also seeks to clarify the semantic aspect of genealogical nouns and their positions within the text of the Holy Quran.&#x0D; Methods: The research adopts a descriptive analytical method, which involves formulating genealogical rules, extracting words related to genealogy, and analyzing the semantic aspect conveyed by these words.&#x0D; Results: One of the most important findings is that the Quran includes numerous genealogical words that follow grammatical rules, with significant variation in terms of race and kinship. These words encompass various functions, such as indicating miracles, praise, condemnation, denial, significance of place, and interpretation.&#x0D; Conclusions: The relationship between the Holy Quran and the Arabic language is one of a thing to its source. The Quran serves as an exemplary model for measuring syntax, phonetics, and morphology in language. Therefore, the study recommends a more practical approach to the application of attributed nouns. The study concluded that the Holy Quran is the primary source upon which linguists relied to codify the grammar of the Arabic language, extrapolate its vocabulary, and determine its semantic usage. As a result, the relationship between the Holy Quran and the Arabic language is a close and significant one.

Diverse Applications of the Case Markers: Urdu Ko and Punjabi Nun

All the Indo-Aryan languages including Urdu and Punjabi use case markers to express the multiple semantic usages of the languages. The present study investigates the variant semantic functions of Urdu ko and Punjabi nun. It aims at the description as well as the analysis of the study samples that show the differing application of Urdu ko and Punjabi nun. Qualitative methodology is used to state the spatial as well as non -spatial usages of the case markers. The results show that the case marker ko and nun mark the accusative as well as dative. These are also used to show a few other temporal and spatial applications..

Personalized Recommendation System Design for Library Resources through Deep Belief Networks

In recent years, with the continuous development of science and Internet technology, people’s lifestyles are changing dramatically, especially with the development of information technology, which has contributed to the transformation of digital libraries. As an essential information infrastructure and a new source of knowledge, digital libraries have brought great convenience to users. To be specific, with the widespread use of smart devices and internet of things technology, users are eager to be intelligent in their information needs while enjoying services, which makes the resource recommendation service of digital libraries increasingly important. In addition, as a provider of knowledge and information services, libraries should organically combine advanced information technology with existing resources to promote the construction of libraries in the information age. However, in the era of big data, users can only passively receive a large amount of information and services in the face of the ever-expanding mass of resources in digital libraries. In this context, libraries might only provide a single set of information resources and services, which cannot meet the individual needs of users and ultimately leads to inefficient allocation of resources and information. After all, users of digital libraries want to be better able to receive personalized recommendations for library resources through relevant technologies. At the same time, libraries are increasing their research and development efforts on algorithms and technologies for personalized recommendations. Also, with the explosive growth of the total amount of information worldwide, people are entering the information age. Massive amounts of data are constantly being generated, and the problem of information overload is becoming more and more serious. The sheer volume of this data and information increases the degree of difficulty in accessing the information people need. In this situation, it is necessary for digital libraries to dynamically analyze user behavior and interests while responding to user requests in a timely manner and accordingly take the initiative to recommend information resources and knowledge services that meet users’ individual needs. As a result, this study uses a deep belief network model for multimodal feature learning and designs a personalized recommendation system for library resources by fusing features from multiple modalities. Furthermore, this research implements the construction of a semantic user interest model and the design of a personalized recommendation algorithm to achieve an accurate description of user interest preferences and semantic personalized recommendation functions.

EMD Based Semantic User Similarity using Past Travel Histories

The cost-effective and easy availability of handheld mobile devices and ubiquity of location acquisition services such as GPS and GSM networks has helped expedient logging and sharing of location histories of mobile users. This work aims to find semantic user similarity using their past travel histories. Application of the semantic similarity measure can be found in tourism-related recommender systems and information retrieval. The paper presents Earth Mover’s Distance (EMD) based semantic user similarity measure using users' GPS logs. The similarity measure is applied and evaluated on the GPS dataset of 182 users collected from April 2007 to August 2012 by Microsoft's GeoLife project. The proposed similarity measure is compared with conventional similarity measures used in literature such as Jaccard, Dice, and Pearsons’ Correlation. The percentage improvement of EMD based approach over existing approaches in terms of average RMSE is 10.70%, and average MAE is 5.73%.

Representation, detection and usage of the content semantics of comments in a social platform

The analysis of people’s comments in social platforms is a widely investigated topic because comments are the place where people show their spontaneity most clearly. In this article, we present a network-based data structure and a related approach to represent and manage the underlying semantics of a set of comments. Our approach is based on the extraction of text patterns that take into account not only the frequency, but also the utility of the analysed comments. Our data structure and approach are ‘multidimensional’ and ‘holistic’, in the sense that they can simultaneously handle content semantics from multiple perspectives. They are also easily extensible, because additional content semantics perspectives can be easily added to them. Furthermore, our approach is able to evaluate the semantic similarity of two sets of comments. In this article, we also illustrate the results of several tests we conducted on Reddit comments, even if our approach can be applied to any social platform. Finally, we provide an overview of some possible applications of this research.

Convolutional Neural Network-Based Cross-Media Semantic Matching and User Adaptive Satisfaction Analysis Model.

In this paper, an in-depth study of cross-media semantic matching and user adaptive satisfaction analysis model is carried out based on the convolutional neural network. Based on the existing convolutional neural network, this paper uses rich information. The spatial correlation of cross-media semantic matching further improves the classification accuracy of hyperspectral images and reduces the classification time under user adaptive satisfaction complexity. Aiming at the problem that it is difficult for the current hyperspectral image classification method based on convolutional neural network to capture the spatial pose characteristics of objects, the problem is that principal component analysis ignores some vital information when retaining a few components. This paper proposes a polymorphism based on extension Attribute Profile Feature (EMAP) Stereo Capsule Network Model for Hyperspectral Image Classification. To ensure the model has good generalization performance, a new remote sensing image Pan sharpening algorithm based on convolutional neural network is proposed, which increases the model's width to extract the feature information of the image and uses dilated instead of traditional convolution. The experimental results show that the algorithm has good generalization while ensuring self-adaptive satisfaction.

Misalignment in Semantic User Model Elicitation via Conversational Agents: A Case Study in Navigation Support for Visually Impaired People

Disabled people can benefit greatly from assistive digital technologies. However, this increased human-machine symbiosis makes it important that systems are personalized and transparent to users. Existing work often uses data-oriented approaches. However, these approaches lack transparency and make it hard to influence the system’s behavior. In this paper, we use knowledge-based techniques for personalization, introducing the concept of Semantic User Models for representing the behavior, values and capabilities of users. To allow the system to construct such a user model, we investigate the use of a conversational agent which can elicit the relevant information from users through dialogue. A conversational interface is essential for our case study of navigation support for visually impaired people, but in general, has the potential to enhance transparency as users know what the system represents about them. For such a dialogue to be effective, it is crucial that the user understands what the conversational agent is asking, i.e., that misalignments that decrease the transparency are avoided or resolved. In this paper, we investigate whether we can use a conversational agent for Semantic User Model elicitation, which types of misalignments can occur in this process and how they are related, and how misalignments can be reduced. We investigate this in two (iterative) qualitative studies (n = 7 & n = 8) with visually impaired people in which a personalized user model for navigation support is elicited via a dialogue with a conversational agent. Our results show four hierarchically structured levels of human-agent misalignment. We identify several design solutions for reducing misalignments, which point to the need for restricting the generic user model to what is needed in the domain under consideration. With this research, we lay a foundation for conversational agents capable of eliciting Semantic User Models.