A personalized semantic portal for enhanced user support

Although many advances have been made in semantic portal research, often links to relevant pages are not shown and the same content/links are presented to users that have different background and interests. This paper introduces a novel semantic portal, SEMPort, to support the browsing of users based on personalization and enriched semantic hyperlinks. Our semantic portal makes a novel contribution by integrating adaptive hypermedia methods and enriched semantic hyperlinks into semantic portal technologies to provide better navigation. SEMPort supports different personalization such as adaptive link sorting and adaptive link annotation based on interests of individual users. Enriched semantic links are also supplied to guide users to relevant pages. In addition, easy-to-use and real-time content maintenance mechanisms are provided, which is important for the evolution of the content. Evaluations carried out to assess the semantic portal include performance evaluations, interface usability using Nielsen's heuristics and empirical user studies. This paper also provides an overview and comparison to the state-of-the-art as well as outlining future directions for semantic portals.     

Video scene segmentation and semantic representation using a novel scheme

Grouping video content into semantic segments and classifying semantic scenes into different types are the crucial processes to content-based video organization, management and retrieval. In this paper, a novel approach to automatically segment scenes and semantically represent scenes is proposed. Firstly, video shots are detected using a rough-to-fine algorithm. Secondly, key-frames within each shot are selected adaptively with hybrid features, and redundant key-frames are removed by template matching. Thirdly, spatio-temporal coherent shots are clustered into the same scene based on the temporal constraint of video content and visual similarity between shot activities. Finally, under the full analysis of typical characters on continuously recorded videos, scene content is semantically represented to satisfy human demand on video retrieval. The proposed algorithm has been performed on various genres of films and TV program. Promising experimental results show that the proposed method makes sense to efficient retrieval of interesting video content.

A semantic approach to ETL technologies

Data warehouse architectures rely on extraction, transformation and loading (ETL) processes for the creation of an updated, consistent and materialized view of a set of data sources. In this paper, we support these processes by proposing a tool that: (1) allows the semi-automatic definition of inter-attribute semantic mappings, by identifying the parts of the data source schemas which are related to the data warehouse schema, thus supporting the extraction process; and (2) groups the attribute values semantically related thus defining a transformation function for populating with homogeneous values the data warehouse.Our proposal couples and extends the functionalities of two previously developed systems: the MOMIS integration system and the RELEVANT data analysis system. The system has been experimented within a real scenario concerning the creation of a data warehouse for enterprises working in the beverage and food logistic area. The results showed that the coupled system supports effectively the extraction and transformation processes.     

IRS-III: A broker-based approach to semantic Web services

A factor limiting the take up of Web services is that all tasks associated with the creation of an application, for example, finding, composing, and resolving mismatches between Web services have to be carried out by a software developer. Semantic Web services is a combination of semantic Web and Web service technologies that promise to alleviate these problems. In this paper we describe IRS-III, a framework for creating and executing semantic Web services, which takes a semantic broker-based approach to mediating between service requesters and service providers. We describe the overall approach and the components of IRS-III from an ontological and architectural viewpoint. We then illustrate our approach through an application in the eGovernment domain.     

Visual query interfaces for semantic datasets: An evaluation study

The rapid growth of the Linked Open Data cloud, as well as the increasing ability to lift relational enterprise datasets to a semantic, ontology-based level means that vast amounts of information are now available in a representation that closely matches the conceptualizations of the potential users of this information. This makes it interesting to create ontology based, user-oriented tools for searching and exploring this data. Although initial efforts were intended for tech users with knowledge of SPARQL/RDF, there are ongoing proposals designed for lay users. One of the most promising approaches is to use visual query interfaces, but more user studies are needed to assess their effectiveness. In this paper, we compare the effect on usability of two important paradigms for ontology-based query interfaces: form-based and graph-based interfaces. In order to reduce the number of variables affecting the comparison, we performed a user study with two state-of-the-art query tools developed by ourselves, sharing a large part of the code base: the graph-based tool OptiqueVQS*, and the form-based tool PepeSearch. We evaluated these tools in a formal comparison study with 15 participants searching a Linked Open Data version of the Norwegian Company Registry. Participants had to respond to 6 non-trivial search tasks using alternately OptiqueVQS* and PepeSearch. Even without previous training, retrieval performance and user confidence were very high, thus suggesting that both interface designs are effective for searching RDF datasets. Expert searchers had a clear preference for the graph-based interface, and mainstream searchers obtained better performance and confidence with the form-based interface. While a number of participants spontaneously praised the capability of the graph interface for composing complex queries, our results evidence that graph interfaces are difficult to grasp. In contrast, form interfaces are more learnable and relieve problems with disorientation for mainstream users. We have also observed positive results introducing faceted search and dynamic term suggestion in semantic search interfaces.     

User interfaces for semantic authoring of textual content: A systematic literature review

Practical approaches for managing and supporting the life-cycle of semantic content on the Web of Data have recently made quite some progress. In particular in the area of the user-friendly manual and semi-automatic creation of rich semantic content we have observed recently a large number of approaches and systems being described in the literature. With this survey we aim to provide an overview on the rapidly emerging field of Semantic Content Authoring (SCA). We conducted a systematic literature review comprising a thorough analysis of 31 primary studies out of 175 initially retrieved papers addressing the semantic authoring of textual content. We obtained a comprehensive set of quality attributes for SCA systems together with corresponding user interface features suggested for their realization. The quality attributes include aspects such as usability, automation, generalizability, collaboration, customizability and evolvability. The primary studies were surveyed in the light of these quality attributes and we performed a thorough analysis of four SCA systems. The proposed quality attributes and UI features facilitate the evaluation of existing approaches and the development of novel more effective and intuitive semantic authoring interfaces.     

The relationship between user participation and system success: a simultaneous contingency approach

The relationship between user participation and information system (IS) success has drawn attention from researchers for some time. It is assumed that strong participation of future users in the design of IS would lead to successful outcomes in terms of more IS usage, greater user acceptance, and increased user satisfaction. However, in spite of this, much of the empirical research so far has been unable to demonstrate its benefits. This paper examines the participation–success relationship in a broader context, where the effects of user participation and two other factors, user attitudes and user involvement, on system success occur simultaneously. Other contingency variables considered here are: system impact, system complexity, and development methodology. The theoretical framework and the associated hypotheses are empirically tested by a survey of 32 organizations. Empirical results corroborate the positive link between user participation and user satisfaction and provide evidence on the interplay between user attitudes and user involvement.     

A semantic approach to a framework for business domain software systems

In enterprise firms, enormous amounts of electronic documents are generated by business analysts and other business domain application users. Applications that use these documents are often driven by business logic that is hard-coded together with application logic. One approach to the separation of business logic from applications is to create and maintain business and information extraction rules in an external, user-friendly format. The drawback of such an externalization is that the business rules, usually, do not have machine interpretable semantics. This situation often leads to misinterpretation of domain analysis documents, which can inhibit the productivity of computer-assisted analytical work and the effectiveness of business solutions. This paper proposes an ontology and rule-based framework for the development of business domain applications, which includes semantic processing of externalized business rules and to some extent externalization of application logic. The creation of external information extraction rules by the business analyst is a cumbersome and time consuming task. In order to overcome this problem, the framework also includes a rule learning system to semi-automate the generation of information extraction rules from source documents with the help of manual annotations. The main idea behind the work presented in this paper is to re-engineer very large enterprise information systems to adapt to Semantic Web computing techniques. The work presented in this paper is inspired by an industrial project.     

基于语义分割的文本观点倾向分析方法研究

提出了一种基于语义的观点倾向分析方法。按照文本结构特点,依据语义相近的原则,将文本分割为若干语义段,对语义段采用条件随机场模型进行主观内容提取和观点倾向识别,计算各个语义段的权值,确定文本的观点倾向。实验表明,与传统机器学习方法相比,该方法能有效提高文本观点倾向分析的准确率。     

Start making sense: The Chatty Web approach for global semantic agreements

This paper describes a novel approach for obtaining semantic interoperability in a bottom–up, semi-automatic manner without relying on pre-existing, global semantic models. We assume that large amounts of data exist that have been organized and annotated according to local schemas. Seeing semantics as a form of agreement, our approach enables the participating data sources to incrementally develop global agreements in an evolutionary and completely decentralized process that solely relies on pair-wise, local interactions.     

LRUNet:轻量级脑肿瘤快速语义分割网络

目的 针对目前基于深度学习的脑肿瘤分割算法参数量大、计算复杂和快速性差的问题,提出了一种超轻量级快速语义分割网络LRUNet （lightweight rapid UNet）,在保证分割精度提升的同时,极大地减少了网络的参数量与计算量,达到快速分割的效果。方法 LRUNet网络结构基于UNet,将3D-UNet的通道数减少为原来的1/4,减少原先3D-UNet过多的参数量;将UNet网络中除最后一层外的所有传统卷积变为深度可分离卷积,深度可分离卷积以牺牲极少精度,大大减少网络参数量,实现网络的轻量级;使用空间—通道压缩和激发模块（spatial and channel squeeze&excitation block,scSE）,该模块能够放大特征图中对模型有利的参数的权重,缩小对模型不利参数的权重,提升网络分割的精度。结果 在BraTS 2018（Brain Tumor Segmentation Challenge 2018）数据集上的在线验证结果显示,该模型在全肿瘤、核心区肿瘤和增强区肿瘤分割的平均Dice系数分别为0.893 6、0.804 6和0.787 2。LRUNet与同为轻量级网络的S3D-UNet相比Dice有所提升,但是,参数量仅为S3D-UNet的1/4,FLOPs （floating point operations per second）仅为1/2。结论 与3D-UNet、S3D-UNet和3D-ESPNet等算法相比,LRUNet算法不仅保证精度得到提升,而且极大地减少网络中计算的参数量与计算成本消耗,同时网络模型的预测速度得到很大提升,使得快速语义分割在3维医学图像领域成为可能。     

A novel approach to conduct the importance-satisfaction analysis for acquiring typical user groups in business-intelligence systems

Business intelligence (BI) is a powerful tool to conduct causality analysis and corporate diagnoses since it provides a data-driven approach to link firms' strategic goals into tactical policies and operational actions. Specifically, BI consists of a series of architectures and techniques like database, data warehousing, and data mining that transform raw data into useful information to provide decision supports. In reality, typical BI user groups involve financial analysts, marketing planners, general managers, field staffs, upstream suppliers, and downstream customers. Inspired by the concept of STP (segmentation-target-positioning) and product family design, BI systems need to be customized to satisfy diverse user groups and tailored to a firm for solving complicated but specific business problems. Consequently, a novel framework is proposed to fulfill the following goals: (1) incorporating user preferences to identify key design features that best fit BI's main segments for achieving customer acquisition, (2) transforming user perceptions into quantitative degrees of satisfaction for accomplishing customer retention, and (3) conducting the importance-satisfaction analysis to generate managerial insights for developing next-generation BI systems.     

From form features to semantic features in existing MCAD: An ontological approach

Today’s product development processes rely on Mechanical Computer-Aided Design (MCAD) systems that implement a geometric-centered perspective in design. The development of long discussed feature-based MCAD has not yet led to systems that truly support semantic and functional representation of features, which hampers also the use of these models for functional reasoning. This paper investigates the present feature-based MCAD limitations. It illustrates, through simple examples, how to use ontological analysis and feature re-classification to introduce software extensions in existing MCAD that achieve a newer level of semantic representation of features, and enhance the cognitive understanding of the final model. The proposal also shows how to automatically validate these features from the functional viewpoint.     

XChange: A semantic diff approach for XML documents

XML documents are extensively used in several applications and evolve over time. Identifying the semantics of these changes becomes a fundamental process to understand their evolution. Existing approaches related to understanding changes (diff) in XML documents focus only on syntactic changes. These approaches compare XML documents based on their structure, without considering the associated semantics. However, for large XML documents, which have undergone many changes from a version to the next, a large number of syntactic changes in the document may correspond to fewer semantic changes, which are then easier to analyze and understand. For instance, increasing the annual salary and the gross pay, and changing the job title of an employee (three syntactic changes) may mean that this employee was promoted (one semantic change). In this paper, we explore this idea and present the XChange approach. XChange considers the semantics of the changes to calculate the diff of different versions of XML documents. For such, our approach analyzes the granular syntactic changes in XML attributes and elements using inference rules to combine them into semantic changes. Thus, differently from existing approaches, XChange proposes the use of syntactic changes in versions of an XML document to infer the real reason for the change and support the process of semantic diff. Results of an experimental study indicate that XChange can provide higher effectiveness and efficiency when used to understand changes between versions of XML documents when compared with the (syntactic) state-of-the-art approaches.     

RAFNet: RGB-D attention feature fusion network for indoor semantic segmentation

Semantic segmentation based on the complementary information from RGB and depth images has recently gained great popularity, but due to the difference between RGB and depth maps, how to effectively use RGB-D information is still a problem. In this paper, we propose a novel RGB-D semantic segmentation network named RAFNet, which can selectively gather features from the RGB and depth information. Specifically, we construct an architecture with three parallel branches and propose several complementary attention modules. This structure enables a fusion branch and we add the Bi-directional Multi-step Propagation (BMP) strategy to it, which can not only retain the feature streams of the original RGB and depth branches but also fully utilize the feature flow of the fusion branch. There are three kinds of complementary attention modules that we have constructed. The RGB-D fusion module can effectively extract important features from the RGB and depth branch streams. The refinement module can reduce the loss of semantic information and the context aggregation module can help propagate and integrate information better. We train and evaluate our model on NYUDv2 and SUN-RGBD datasets, and prove that our model achieves state-of-the-art performances.     

Using semantic components to search for domain-specific documents: An evaluation from the system perspective and the user perspective

We seek to leverage an expert user's knowledge about how information is organized in a domain and how information is presented in typical documents within a particular domain-specific collection, to effectively and efficiently meet the expert's targeted information needs. We have developed the semantic components model to describe important semantic content within documents. The semantic components model for a given collection (based on a general understanding of the type of information needs expected) consists of a set of document classes, where each class has an associated set of semantic components. Each semantic component instance consists of segments of text about a particular aspect of the main topic of the document and may not correspond to structural elements in the document. The semantic components model represents document content in a manner that is complementary to full text and keyword indexing. This paper describes how the semantic components model can be used to improve an information retrieval system. We present experimental evidence from a large interactive searching study that compared the use of semantic components in a system with full text and keyword indexing, where we extended the query language to allow users to search using semantic components, to a base system that did not have semantic components. We evaluate the systems from a system perspective, where semantic components were shown to improve document ranking for precision-oriented searches, and from a user perspective. We also evaluate the systems from a session-based perspective, evaluating not only the results of individual queries but also the results of multiple queries during a single interactive query session.