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The purpose of this paper is to explore a semantics representation framework for narrative images, conforming to the image-interpretation process.

This paper explores the essential features of semantics evolution in the process of narrative images interpretation. It proposes a novel semantics representation framework, ESImage (evolution semantics of image) for narrative images. ESImage adopts a hierarchical architecture to progressively organize the semantic information in images, enabling the evolutionary interpretation under the support of a graph-based semantics data model. Also, the study shows the feasibility of this framework by addressing the issues of typical semantics representation with the scenario of the Dunhuang fresco.

The process of image interpretation mainly concerns three issues: bottom-up description, the multi-faceted semantics representation and the top-down semantics complementation. ESImage can provide a comprehensive solution for narrative image semantics representation by addressing the major issues based on the semantics evolution mechanisms of the graph-based semantics data model.

ESImage needs to be combined with machine learning to meet the requirements of automatic annotation and semantics interpretation of large-scale image resources.

This paper sorts out the characteristics of the gradual interpretation of narrative images and has discussed the major issues in its semantics representation. Also, it proposes the semantic framework ESImage which deploys a flexible and sound mechanism to represent the semantic information of narrative images.

This paper presents a systematic XML Schema design approach which conceptually captures semantics of the problem domain at conceptual level and represents such semantics in XML Schema at schema level. At the conceptual level, objects, their inter‐relationships and constraints are semantically powered by object‐oriented models. At the schema level, these conceptual semantics are comprehensively represented in textbased representation of XML Schema using various schema components and design styles, each of which offers different quality characteristics. Two primary design styles in use are nesting and linking. The nesting design styles are developed based on the choice of schema components and their definition/declaration scopes (global vs. local), whereas the linking design styles use referencing facilities provided by XML Schema and other XML technologies such as XLink and XPointer. With an in‐depth analysis of outstanding problems of existing approaches, the proposed design approach is motivated to help improve the quality and robustness of the XML documents in large‐scale XML‐based applications.

Mining user-concerned actionable and interpretable hot topics will help management departments fully grasp the latest events and make timely decisions. Existing topic models primarily integrate word embedding and matrix decomposition, which only generates keyword-based hot topics with weak interpretability, making it difficult to meet the specific needs of users. Mining phrase-based hot topics with syntactic dependency structure have been proven to model structure information effectively. A key challenge lies in the effective integration of the above information into the hot topic mining process.

This paper proposes the nonnegative matrix factorization (NMF)-based hot topic mining method, semantics syntax-assisted hot topic model (SSAHM), which combines semantic association and syntactic dependency structure. First, a semantic–syntactic component association matrix is constructed. Then, the matrix is used as a constraint condition to be incorporated into the block coordinate descent (BCD)-based matrix decomposition process. Finally, a hot topic information-driven phrase extraction algorithm is applied to describe hot topics.

The efficacy of the developed model is demonstrated on two real-world datasets, and the effects of dependency structure information on different topics are compared. The qualitative examples further explain the application of the method in real scenarios.

Most prior research focuses on keyword-based hot topics. Thus, the literature is advanced by mining phrase-based hot topics with syntactic dependency structure, which can effectively analyze the semantics. The development of syntactic dependency structure considering the combination of word order and part-of-speech (POS) is a step forward as word order, and POS are only separately utilized in the prior literature. Ignoring this synergy may miss important information, such as grammatical structure coherence and logical relations between syntactic components.

This research aims to argue that manual geometric modeling is blocking the building information modeling (BIM) promotion to small-size companies. Therefore, it is necessary to study a manner of automated modeling to reduce the dependence of BIM implementation on manpower. This paper aims to make a study into such a system to propose both its theory and prototype.

This research took a prototyping as the methodology, which consists of three steps: (1) proposing a theoretical framework supporting automated geometric modeling process; (2) developing a prototype system based on the framework; (3) conducting a testing for the prototype system on its performance.

Previous researches into automated geometric modeling only respectively focused on a specific procedure for a particular engineering domain. No general model was abstracted to support generic geometric modeling. This paper, taking higher level of abstraction, proposed such a model that can describe general geometric modeling process to serve generic automated geometric modeling systems.

This paper focused on only geometric modeling, skipping non-geometric information of BIM. A complete BIM model consists of geometric and non-geometric data. Therefore, the method of combination of them is on the research agenda.

The model proposed by this paper provide a mechanism to translate engineering geometric objects into textual representations, being able to act as the kernel of generic automated geometric modeling systems, which are expected to boost BIM promotion in industry.

To 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.

After 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.

Results 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.

DSA 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.

Business process modeling can involve multiple stakeholders, so it is natural that problems may occur during the designing and understanding processes. One way to perceive these problems is to evaluate the comprehension of business process models through the collection of data related to the readers' eye movement via an eye-tracking device. The purpose of this paper is to provide a comprehensive overview of the use of eye-trackers in understanding process models and to offer a research roadmap to challenge the community to address the identified limitations and open issues that require further investigation.

To achieve this goal, Systematic Literature Review (SLR) was performed following good practices from the Evidence-Based Software Engineering's (EBSE) field.

This study resulted in 10 primary studies selected for analysis and data extraction, from the 1,482 initially retrieved. The major findings indicate that the business process community still benefits little from the use of eye-tracking, e.g. not offering sufficient support for inexperienced designers and not having an explicit standardization in its use. These and other findings are synthesized in a research roadmap which results would benefit researchers and practitioners.

In the studies found, the methods used to explore eyes' movement in process models' comprehension analysis were presented as an advantage of the current study. Additionally, another aspect presented in this SRL as an originality is presenting a set of open questions, suggesting valuable topics for future research through a research script (research roadmap).

The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build conceptual models from data.

A semantic data model named meaning graph (MGraph) is introduced to represent knowledge concepts to organize the knowledge instances in a graph-based knowledge base. MGraph uses directed acyclic graph–like types as concept schemas to specify the structural features of knowledge with intention variety. It also proposes several specialization mechanisms to enable knowledge evolution. Based on MGraph, a paradigm is introduced to model the evolutionary concept schemas, and a scenario on video semantics modeling is introduced in detail.

MGraph is fit for the evolution features of representing knowledge from big data and lays the foundation for building a knowledge base under the big data circumstance.

The representation approach based on MGraph can effectively and coherently address the major issues of evolutionary knowledge from big data. The new approach is promising in building a big knowledge base.

The purpose of this paper is to explore the basics of semiotic analysis and concept theory that represent two dominant approaches to knowledge representation, and explore how these approaches are fruitful for knowledge organization.

In particular the semiotic theory formulated by the American philosopher C.S. Peirce and the concept theory formulated by Ingetraut Dahlberg are investigated. The paper compares the differences and similarities between these two theories of knowledge representation.

The semiotic model is a general and unrestricted model of signs and Dahlberg's model is thought from the perspective and demand of better knowledge organization system (KOS) development. It is found that Dahlberg's concept model provides a detailed method for analyzing and representing concepts in a KOS, where semiotics provides the philosophical context for representation.

This paper is the first to combine theories of knowledge representation, semiotic and concept theory, within the context of knowledge organization.

The purpose of this paper is to introduce the advantages of semantics‐aware representation formalisms in the integration of digital rights management (DRM) infrastructures grounded on heterogeneous formats.

After discussing the notion of semantics‐aware IPR and its relationship with Semantic Web‐style metadata, we exemplify the advantages of adopting it by providing two different use cases. XML‐based DRMLs are mapped with a shared ontology‐based representation in such a way that dependencies between elements can be drawn.

Individual formalisms may take advantage of a semantics‐aware infrastructure to check consistency of DRM policies according to dependencies not explicit in the specification language. On the other hand, distinct formalisms can be integrated with each other according to fine‐grained translation mechanisms.

Inference procedures can pre‐process this knowledge base and derive implicit knowledge that can be used by programming logic in the actual enforcement of DRM policies.

The purpose of this paper is to provide a framework for enhancing security policy management in the Grid.

The Grid security policy reconciliation problem is presented. A generic view on the security policy notion is adopted and the security policy ontology notion is introduced and used.

In the course of this work it was found that, in order to enhance security policy management in the Grid, Grid entities should have the ability to negotiate their security policies. It was also found that, in order to achieve security policy negotiation, effective security policy semantics manipulation towards security policy reconciliation is needed. Finally, it was established, through the use of an example, that if appropriate means are used for security policy reconciliation then incompatible security policy representations can be transformed into compatible ones.

Research limitations stem from the adoption of a generic view on the security policy notion and the selection of identification and authentication security policies as the focal point of the proposed framework. Research implications include the possibility of examining how existing security policy reconciliation models can be incorporated in this generic framework. The possibility of investigating how such a framework can lead to a security policy knowledge management tool for Grid administrators is also demonstrated.

Practical implications of this work include the establishment of a common framework for security information exchange between Grid entities.

This paper proposes a framework for enhancing security policy management in the Grid. The proposed framework can be used by researchers as a reference and by security experts in order to reduce ambiguity concerning the interpretation of security policies expressed in different forms, by negotiating Grid entities.