X3D Ontology for Querying 3D Models on the Semantic Web

In this paper we explore some of the opportunities and challenges for machine learning on the Semantic Web. The Semantic Web provides standardized formats for the representation of both data and ontological background knowledge. Semantic Web standards are used to describe meta data but also have great potential as a general data format for data communication and data integration. Within a broad range of possible applications machine learning will play an increasingly important role: Machine learning solutions have been developed to support the management of ontologies, for the semi-automatic annotation of unstructured data, and to integrate semantic information into web mining. Machine learning will increasingly be employed to analyze distributed data sources described in Semantic Web formats and to support approximate Semantic Web reasoning and querying. In this paper we discuss existing and future applications of machine learning on the Semantic Web with a strong focus on learning algorithms that are suitable for the relational character of the Semantic Web's data structure. We discuss some of the particular aspects of learning that we expect will be of relevance for the Semantic Web such as scalability, missing and contradicting data, and the potential to integrate ontological background knowledge. In addition we review some of the work on the learning of ontologies and on the population of ontologies, mostly in the context of textual data.

Since archaeology in particular and humanities in general are interdisciplinary disciplines, there is an imperious need to enhance the accessibility and harmonise data integration, given their varied resource types (books, archives, scientific data, GIS, 3D models etc) and their different conceptual and technological structures and standards. A factor that further reduces accessibility and query performances is related to storing, such as physical location of data, language, and interface. The advent of Semantic Web technology represents an important advance in creating networks of archaeological knowledge based on various resources available on-line. While the valuable use of CIDOC-CRM (Comite International pour la Documentation -Conceptual Reference Model) ontology for specific CH (Cultural Heritage) domains (e.g. museums, libraries, etc) has been partially demonstrated, the interdisciplinary implementations are limited. In this article, we explore the potential of the CIDOC-CRM for a cross-domain implementation of CH data integration. We conducted tha mappings of different on-line resources related to Stonehenge to CIDOC-CRM, and evaluated them in the MAD database, a web-based application that manages natively XML-based datasets. The result ensures the validity of mapping mechanism and the semantic integration of CH data from different sources.

This work introduces a formal framework for the social acquisition of ontologies which are constructed dynamically from overhearing the possibly conflicting symbolic interaction of autonomous information sources, and an approach to the pragmatics of communicated ontological axioms. Technically, the framework is based on distributed variants of description logic for the formal contextualization of statements w.r.t. their respective provenance, speaker's attitude, addressees, and subjective degree of confidence. Doing so, our approach demarcates from the dominating more or less informal approaches to context and provenance representation on the semantic web, and carefully distinguishes between communication attitudes such as public assertion and intention exhibited on the (semantic) web on the one hand, and mental attitudes such as private belief on the other. Furthermore, our framework provides formal means for the probabilistic fusion of controversial opinions, and presents a semantics of information publishing acts. Our approach provides an incomplex and genuinely social approach to knowledge acquisition and representation, and is thus expected to be widely applicable in fields such as the semantic web and social software, and possibly also in other distributed environments such as P2P systems.

Ontology is the fundamental part of Semantic Web. The goal of W3C is to bring the web into (its full potential) a semantic web with reusing previous systems and artifacts. Most legacy systems have been documented in structural analysis and structured design (SASD), especially in simple or Extended ER Diagram (ERD). Such systems need up-gradation to become the part of semantic web. In this paper, we present ERD to OWL-DL ontology transformation rules at concrete level. These rules facilitate an easy and understandable transformation from ERD to OWL. The set of rules for transformation is tested on a structured analysis and design example. The framework provides OWL ontology for semantic web fundamental. This framework helps software engineers in upgrading the structured analysis and design artifact ERD, to components of semantic web. Moreover our transformation tool, ER2OWL, reduces the cost and time for building OWL ontologies with the reuse of existing entity relationship models.

Most of the challenges faced when building the Semantic Web require a substantial amount of human labor and intelligence. Despite significant advancement in ontology learning and human language technology, the tasks of ontology construction, semantic annotation, and establishing alignments between multiple ontologies remain highly dependent on human intelligence. This means that individuals need to contribute time and sometimes other resources. Unfortunately, we observe a serious lack of user involvement in the aforementioned tasks, which may be due to the absence of motivations for people who contribute. As a novel solution, we (1) propose to masquerade the core tasks of weaving the Semantic Web behind online, multi-player game scenarios, in order to create proper incentives for human users to get involved. Doing so, we adopt the findings from the already famous “games with a purpose” by von Ahn, who has shown that presenting a useful task, which requires human intelligence, in the form of an online game can motivate a large amount of people to work heavily on this task, and this for free. Then, we (2) describe our generic OntoGame platform, and (3) several gaming scenarios for various tasks plus our respective prototypes. Based on the analysis of user data and interviews with players, we provide preliminary evidence that users (4) enjoy the games and are willing to dedicate their time to those games, (5) are able to produce high-quality conceptual choices. Eventually we show how users entertaining themselves by online games can unknowingly help weave and maintain the Semantic Web.

Semantic Web (SW) languages are supposed to be compatible with each other in a meaningful way, so as to facilitate machine understanding. Recent research, however, shows that the semantics of the standard SW annotation language RDF (as well as its ontological extension RDFS) and that of the standard SW ontology language OWL DL are not compatible with each other. This paper investigates some issues behind this incompatibility and proposes a novel modification of RDF(S) as a firm semantic foundation for many of the latest description logics-based SW ontology languages, including OWL DL. Furthermore, the bidirectional one-to-one mapping between RDFS(FA) axioms in strata 0-2 and OWL DL axioms has been established, which enables RDFS(FA)-agents and OWL DL-agents to communicate with each other more easily. As a result, the introduction of RDFS(FA) clarifies the vision of the semantic Web and solidifies RDF(S)'s proposed role as the base of the semantic Web

The emergence of Semantic Web (SW) and the related technologies promise to make the Web a meaningful experience. Conversely, success of SW and its applications depends largely on utilization and interoperability of well-formulated ontology bases in an automated heterogeneous environment. This creates a need to investigate utilization of an (materialized) ontology view as an alternative version of an ontology. However, high level modeling, design and querying techniques still proves to be a challenging task for SW paradigm, as, unlike classical database systems, ontology view definitions and querying have to be done at high-level abstraction. In order to address such an issue, in this chapter, we describe an abstract view formalism forSW (SW-view) with conceptual and logical extensions. SW-views provides the needed conceptual and logical semantics to engineer ontology bases using three levels of abstraction, namely (1) conceptual, (2) logical/schema and (3) instance levels. We first provide the view model and it formal properties including a set of conceptual operators, which enable us to do ontology extraction at the conceptual level. Later, we provide a schemata transformation methodology to materialize SW-views under the Ontology Extraction Methodology (OEM) framework.

The Hypertext 2015 Doctorial Consortium session is held the first day of the 26th ACM Conference on Hypertext and Social Media. It offers Ph.D. students an opportunity to present their ongoing research towards obtaining a Ph.D. degree in the disciplines related to the conference, mostly in Computer and Information Sciences but not limited to them. The themes of the submissions are strongly connected to the research tracks of this year's conference: Digital Connectivity, Data Connectivity and Digital Humanities. The Digital Connectivity track targets developing insights into the mechanisms of information generation and dissemination, characterization of evolutionary processes on online social networks, and studies of models and systems that support these processes. The second track, Data Connectivity, deals with the methods, techniques and technologies that can be used to make data available on the Web, with a special focus on how heterogeneous data sources can be connected to each other. Finally, the track of Digital Humanities seeks to attract work from an interdisciplinary perspective, on the intersection between computer science on one hand, and the humanities and social sciences on the other. During the DC session, students receive constructive feedback from their peers and from a panel of mentors to support them in envisioning added-value contributions to the state-of-the-art research in Hypertext and Social Media. The consortium session is open to all doctorial students by application. Each submission was reviewed by three senior researchers in the topics the students made their submissions. The DC session is aimed in particular at students who have defined a dissertation topic but are still more than one year from graduating at the time of application in order to obtain benefits from feedback. This year, the committee has accepted three contributions to be presented during the Doctorial Consortium session: Automated Methods for Identity Resolution in Heterogeneous Social Platforms by Paridhi Jahin. In this work, connected to the digital and data connectivity tracks of the conference, the author proposes novel methods to search and link user identities scattered across heterogeneous social networks. The methods consider carefully users' privacy, so they are designed to access only public and historic data. The evaluation is proposed on large datasets over multiple platforms to prove their significance in identity resolution of an online user. Language Innovation and Change in On-line Social Networks by Daniel Kershaw. As a fundamental aspect for human communication, this research focuses on forecasting online language change through the use of predictive and descriptive methodologies. This work is framed within structuration theory which helps the researcher in structuring the analysis of the dynamics of language (re)production - i.e. by the agent (user), the social structure and their interplay. A Framework to Provide Customized Reuse of Open Corpus Content for Adaptive Systems by Mostafa Bayomi. This work deals with issues of reusability of contents on adaptive systems. Adaptive systems tailor content specific to user's needs, but they face two big problems: (a) they use a closed corpus content that has been prepared for them a priori, and (b) the content is tightly coupled with other parts of the system, which hinders its reusability. This work presents a proposal that leverages the semantic web by extending an existing content provision system, Slice-pedia.

Ontology provides a shared and reusable piece of knowledge about a specific domain, and has been applied in many fields, such as semantic Web, e-commerce and information retrieval, etc. However, building ontology by hand is a very hard and error-prone task. Learning ontology from existing resources is a good solution. Because relational database is widely used for storing data and OWL is the latest standard recommended by W3C, this paper proposes an approach of learning OWL ontology from data in relational database. Compared with existing methods, the approach can acquire ontology from relational database automatically by using a group of learning rules instead of using a middle model. In addition, it can obtain OWL ontology, including the classes, properties, properties characteristics, cardinality and instances, while none of existing methods can acquire all of them. The proposed learning rules have been proven to be correct by practice.

The Semantic Web is an attempt to add meaningful annotations to Web resources, services and content that requires developing reference ontologies, which help to understand these annotations. The venue of the Web of Data makes the geographic information, which has become an important part of the current Web, widely usable. This paper demonstrates how the Geospatial Web might take advantage from the Semantic Web. The show case is a services catalog dedicated to support the visualization applications based on on-the-fly data integration. The presented infrastructure for improving the catalog functionality applies an administrative geography, i.e. an ontology of political organization of the territory, published as Linked Data. The principal advantage of this approach is reflected by enhancing the functionality of the user application.

Mathematics is a ubiquitous foundation of science, technology, and engineering. Specific areas of mathematics, such as numeric and symbolic computation or logics, enjoy considerable software support. Working mathematicians have recently started to adopt Web 2.0 environments, such as blogs and wikis, but these systems lack machine support for knowledge organization and reuse, and they are disconnected from tools such as computer algebra systems or interactive proof assistants. We argue that such scenarios will benefit from Semantic Web technology.Conversely, mathematics is still underrepresented on the Web of [Linked] Data. There are mathematics-related Linked Data, for example statistical government data or scientific publication databases, but their mathematical semantics has not yet been modeled. We argue that the services for the Web of Data will benefit from a deeper representation of mathematical knowledge.Mathematical knowledge comprises structures given in a logical language --formulae, statements e.g. axioms, and theo-ries --, a mixture of rigorous natural language and symbolic notation in documents, application-specific metadata, and discussions about conceptualizations, formalizations, proofs, and counter-examples. Our review of vocabularies for representing these structures covers ontologies for mathematical problems, proofs, interlinked scientific publications, scientific discourse, as well as mathematical metadata vocabularies and domain knowledge from pure and applied mathematics.Many fields of mathematics have not yet been implemented as proper Semantic Web ontologies; however, we show that MathML and OpenMath, the standard XML-based exchange languages for mathematical knowledge, can be fully integrated with RDF representations in order to contribute existing mathematical knowledge to the Web of Data.We conclude with a roadmap for getting the mathematical Web of Data started: what datasets to publish, how to interlink them, and how to take advantage of these new connections.

In the Internet of Things (IoT), small-scale embedded devices monitor and control real-world objects with different kinds of sensors and actuators. Often, devices communicate measurements and states using proprietary encodings, with the benefit of having all data stored in a central location, e.g., the Cloud. The drawbacks are that all data needs to be collected in advance, even if it is not used afterwards, and that nonstandard protocols make it hard to extend existing networks with new devices. In contrast, the Semantic Web (or Web of Data) represents the successful efforts towards linking and sharing data over the Web. The cornerstones of the Web of Data are RDF as data format and SPARQL as de-facto standard query language. Recent trends show the evolution of the integration of these two efforts into the Web of Things. We propose to elevate embedded devices to first-class citizens of the Web of Things by allowing storage and processing of RDF data. Our framework abstracts from individual deployments to represent them as common data sources in line with the ideas behind the Semantic Web. This includes the execution of SPARQL queries over the data from a pool of embedded devices and/or external data sources. Handling verbose RDF data and executing SPARQL queries in an embedded network pose major challenges to minimize the involved processing and communication cost. We therefore present an in-network query processor aiming to push processing steps onto devices. We demonstrate the practical application and the potential benefits of our framework in an evaluation using a real-world deployment and a range of SPARQL queries stemming from common use cases of the Web of Things.

This chapter is an introduction to the Semantic Web, the Web of Data, regulatory models, and the law. It does not take anything for granted. The first part of the chapter describes the languages of the Semantic Web, and shows how the perspective of the Web of Services and Linked Data is related to the conditions under which services can be offered, managed and used. The Web has been massively populated with both data and services. Semantically structured data, the Linked Data Cloud, allows and fosters human-machine interaction. Linked Data aims at creating ecosystems to facilitate the browsing, discovery, exploitation and reuse of datasets for applications. Licensed Linked Data is offered along with information about the rights involved. Rights Expression Languages are able to regulate half-automatically the use and reuse of content.

Ontologies and knowledge bases are fundamental cornerstones in semantic search systems based on which efficient web services and sophisticated search mechanisms are designed. High quality domain ontologies are essential for successful employment of semantic Web services. However, acquisition of ontologies is difficult and costly, thus hindering the development of this field. In this work, the knowledge acquisition problem in the semantic Web is cast as the task of ontology construction. We construct an ontology for “polycystic kidney disease” from domain text by a pattern based method which requires minimum human intervention. We also create ontology manually to present the evaluation results of the constructed ontology.KeywordsOntologyontology constructionpatternsconcept extraction

Understanding the world in three dimensions has long been a scientific challenge with significant practical implications. In the era of deep learning, researchers have made substantial progress in developing 3D representations using deep neural networks. Among the myriad of entities in our complex environment, humans stand out due to their general significance in science and their specific relevance in various applications. This thesis focuses on human-centric 3D representation learning within the framework of deep learning. Specifically, we explore three key areas: human-centric 3D perception, human reconstruction, and 3D human generation. The thesis presents five studies that collectively address these topics. In the realm of human-centric 3D perception, we introduce a versatile multi-modal pre-training approach. By harnessing the diverse modalities of human data, such as RGB images, depth, and 2D keypoints, we present a general framework HCMoCo for effective human-centric representation learning. This framework achieves state-of-the-art performance across four human perception tasks, including DensePose prediction, human parsing, and 3D keypoint prediction. In the area of human reconstruction, we investigate garment reconstruction from 4D point clouds of dressed individuals. To address the ambiguities inherent in 2D images, we propose a principled framework called Garment4D, which enables separable and interpretable garment reconstruction. Notably, Garment4D can effectively reconstruct and model the non-rigid deformations of loose garments (e.g., skirts) that do not share the same topology as the human body. We present two distinct approaches to 3D human generation. In our first work, AvatarCLIP, we generate and animate avatars based on text descriptions of body shapes, appearances, and motions. By leveraging differentiable rendering and large- scale vision-language pre-trained models, we achieve avatar and motion synthesis without the need for supervised training or paired data. To further enhance the quality of the generated avatars, we introduce a second approach, EVA3D, which is a high-quality unconditional 3D human generative model that requires only 2D image collections for training. We design an efficient compositional human NeRF representation to facilitate high-resolution 3D human sampling and rendering, which is employed in adversarial training. Finally, by harnessing the capabilities of large language models (LLMs), we explore 3D human representation learning with a focus on human motion and the integration of perception, reconstruction, and generation. We introduce EgoLM, a versatile framework for understanding egocentric motion using multi-modal data. This framework incorporates rich contextual information from egocentric videos and motion sensors provided by wearable devices. EgoLM unifies various motion learning tasks, including motion understanding from video and motion data, as well as motion tracking and generation from text or sparse sensor input. Additionally, it enables a novel task unique to wearable devices: generating text descriptions from sparse sensor data. By investigating 3D human representation learning from three distinct perspectives, we have attained a comprehensive understanding of humans that spans both high-level semantics and low-level geometry. This thesis offers a cohesive exploration of human-centric 3D representations, contributing significantly to the field of human-centric vision.