Semantic Analytics Research Articles

A Novel Approach for Mining Big Data Using Multi-Model Fusion Mechanism (MMFM)

Big data processing and analytics require sophisticated systems and cutting-edge methodologies to extract useful data from the available data. Extracted data visualization is challenging because of the processing models' dependence on semantics and classification. To categorize and improve information-based semantics that have accumulated over time, this paper introduces the Multi-model fusion mechanism for data mining (MMFM) approach. Information dependencies are organized based on the links between the data model based on attribute values. This method divides the attributes under consideration based on processing time to handle complicated data in controlled amount of time. The proposed MMFM’s performance is assessed with real-time weather prediction dataset where the data is acquired from sensor (observed) and image data. MMFM is used to conduct semantic analytics and similarity-based classification on this collection. The processing time based on records and samples are investigated for the various data sizes, instances, and entries. It is found that the proposed MMFM gets 70 seconds of processing time for 2GB data and 0.99 seconds while handling 5000 records for various classification instances.

SimE4KG: Distributed and Explainable Multi-Modal Semantic Similarity Estimation for Knowledge Graphs

In recent years, exciting sources of data have been modeled as knowledge graphs (KGs). This modeling represents both structural relationships and the entity-specific multi-modal data in KGs. In various data analytics pipelines and machine learning (ML), the task of semantic similarity estimation plays a significant role. Assigning similarity values to entity pairs is needed in recommendation systems, clustering, classification, entity matching/disambiguation and many others. Efficient and scalable frameworks are needed to handle the quadratic complexity of all-pair semantic similarity on Big Data KGs. Moreover, heterogeneous KGs demand multi-modal semantic similarity estimation to cover the versatile contents like categorical relations between classes or their attribute literals like strings, timestamps or numeric data. In this paper, we propose the SimE4KG framework as a resource providing generic open-source modules that perform semantic similarity estimation in multi-modal KGs. To justify the computational costs of similarity estimation, the SimE4KG generates reproducible, reusable and explainable results. The pipeline results are a native semantic RDF KG, including the experiment results, hyper-parameter setup and explanation of the results, like the most influential features. For fast and scalable execution in memory, we implemented the distributed approach using Apache Spark. The entire development of this framework is integrated into the holistic distributed Semantic ANalytics StAck (SANSA).

Students’ competencies discovery and assessment using learning analytics and semantic web

Traditional content-based assessment systems, which depend on the score as a key criterion for students’ evaluation, have proven to have many drawbacks, especially with the development of learning methods in recent years. Based on these developments, there is a need to adopt new assessment methods to assess the actual skills of students in the digital age. Therefore, the competency-based assessment approach is adopted in this paper to address the subject of students’ competency modelling and discovery in technology-enhanced learning systems. This method of assessment is perfectly suited to modern teaching trends. The authors proposed an approach of assessment semantic analytics to be used for discovering and assessing students’ competencies. This study notes that, all knowledge about students and their competencies has been modelled by semantic representations. Student’s models have been subjected to a set of learning analytics approaches to analyse data generated by students’ activities in order to discover their explicit and latent competencies hidden behind their activities. This experimental study indicates that the competency-based assessment approach is efficient and expected to show significant advantages in evaluating students’ competencies. Implications for practice or policy: Students become able to organise their gains, then integrate and employ them in solving life's problems. Educators get to know more about the extent to which the objectives of their educational process are achieved by evaluating the intellectual, cultural, knowledge, and skilful assets that the learners obtain. Educational policymakers can have a pedagogical and technical vision to move from the culture of content-based evaluations to a competency-based assessment.

The impact of service attributes and category on eWOM helpfulness: An investigation of extremely negative and positive ratings using latent semantic analytics and regression analysis

Online reviews have become irreplaceable product/service information for many consumers. However, not all consumer reviews are deemed to be helpful. Drawing upon expectation-confirmation theory and the attribute-based model of consumer decision-making, this study investigates the role of service attributes and the moderating role of service category in the prediction of extremely positive and extremely negative ratings helpfulness. This study uses latent semantic analysis and regression analysis using a sample of 490 extremely negative and 3757 extremely positive ratings of hotels from TripAdvisor.com. Results show that some product attributes discussed in consumer reviews are particularly important in the determination of the helpfulness of consumer reviews associated with extreme ratings. Findings also suggest the moderating role of service category (i.e. hotel stars) in the relationship between hotel attributes and extreme ratings helpfulness. This study stresses the importance of service attributes and classification in the evaluation of extremely positive and extremely negative ratings helpfulness. Theoretical and managerial implications are discussed.

End‐to‐end learning interpolation for object tracking in low frame‐rate video

In many scenarios, where videos are transmitted through bandwidth-limited channels for subsequent semantic analytics, the choice of frame rates has to balance between bandwidth constraints and analytics performance. Faced with this practical challenge, this study focuses on enhancing object tracking at low frame rates and proposes a learning Interpolation for tracking framework. This framework embeds an implicit video frame interpolation sub-network, which is concatenated and jointly trained with another object tracking sub-network. Once a low frame-rate video is an input, it is first mapped into a high frame-rate latent video, based on which the tracker is learned. Novel strategies and loss functions are derived to ensure the effective end-to-end optimisation of the authors’ network. On several challenging benchmarks and settings, their method achieves a highly competitive tradeoff between frame rate and tracking accuracy. As is known, the implications of interpolation on semantic video analytics and tracking remain unexplored, and the authors expect their method to find many applications in mobile embedded vision, Internet of Things and edge computing.

EventKG – the hub of event knowledge on the web – and biographical timeline generation

One of the key requirements to facilitate the semantic analytics of information regarding contemporary and historical events on the Web, in the news and in social media is the availability of reference knowledge repositories containing comprehensive representations of events, entities and temporal relations. Existing knowledge graphs, with popular examples including DBpedia, YAGO and Wikidata, focus mostly on entity-centric information and are insufficient in terms of their coverage and completeness with respect to events and temporal relations. In this article we address this limitation, formalise the concept of a temporal knowledge graph and present its instantiation - EventKG. EventKG is a multilingual event-centric temporal knowledge graph that incorporates over 690 thousand events and over 2.3 million temporal relations obtained from several large-scale knowledge graphs and semi-structured sources and makes them available through a canonical RDF representation. Whereas popular entities often possess hundreds of relations within a temporal knowledge graph such as EventKG, generating a concise overview of the most important temporal relations for a given entity is a challenging task. In this article we demonstrate an application of EventKG to biographical timeline generation, where we adopt a distant supervision method to identify relations most relevant for an entity biography. Our evaluation results provide insights on the characteristics of EventKG and demonstrate the effectiveness of the proposed biographical timeline generation method.

Semantics in the Deep: Semantic Analytics for Big Data

One cannot help but classify the continuous birth and demise of Artificial Intelligence (AI) trends into the everlasting theme of the battle between connectionist and symbolic AI [...]

Semalytics: a semantic analytics platform for the exploration of distributed and heterogeneous cancer data in translational research.

Each cancer is a complex system with unique molecular features determining its dynamics, such as its prognosis and response to therapies. Understanding the role of these biological traits is fundamental in order to personalize cancer clinical care according to the characteristics of each patient’s disease. To achieve this, translational researchers propagate patients’ samples through in vivo and in vitro cultures to test different therapies on the same tumor and to compare their outcomes with the molecular profile of the disease. This in turn generates information that can be subsequently translated into the development of predictive biomarkers for clinical use. These large-scale experiments generate huge collections of hierarchical data (i.e. experimental trees) with relative annotations that are extremely difficult to analyze. To address such issues in data analyses, we came up with the Semalytics data framework, the core of an analytical platform that processes experimental information through Semantic Web technologies. Semalytics allows (i) the efficient exploration of experimental trees with irregular structures together with their annotations. Moreover, (ii) the platform links its data to a wider open knowledge base (i.e. Wikidata) to add an extended knowledge layer without the need to manage and curate those data locally. Altogether, Semalytics provides augmented perspectives on experimental data, allowing the generation of new hypotheses, which were not anticipated by the user a priori.In this work, we present the data core we created for Semalytics, focusing on its semantic nucleus and on how it exploits semantic reasoning and data integration to tackle issues of this kind of analyses. Finally, we describe a proof-of-concept study based on the examination of several dozen cases of metastatic colorectal cancer in order to illustrate how Semalytics can help researchers generate hypotheses about the role of genes alterations in causing resistance or sensitivity of cancer cells to specific drugs.

A novel network virtualization based on data analytics in connected environment

Big data analytics is a growing trend for network and service management. Some approaches such as statistical analysis, data mining and machine learning have become promising techniques to improve operations and management of information technology systems and networks. In this paper, we introduce a novel approach for network management in terms of abnormality detection based on data analytics. Particularly, the main research focuses on how the network configuration can be automatically and adaptively decided, given various dynamic contexts (e.g., network interference, heterogeneity and so on). Specifically, we design a context-based data-driven framework for network operation in connected environment which includes three layer architecture: (i) network entity layer; (ii) complex semantic analytics layer and (iii) action provisioning layer. A case study on interference-based abnormal detection for connected vehicle explains more detail about our work.

Connecting: the Semantic HCI Textbook and Cross-Institutional Learning Analytics

Connecting: the Semantic HCI Textbook and Cross-Institutional Learning Analytics

Design of user-centric semantic rights model for validation of user-generated content

In these days, the creative user really enjoys creating digital items and sharing their works with other people on the Web. Most users, who create digital contents, want to make secured packages of their works and distribute them with the attachment of valid licenses. Current digital rights management (DRM) systems, however, do not provide the functionality that supports the requirement of the creative users who are considered as just consumers by the current available DRM systems. To make the user-centric DRM functionality possible, we found that license management should be more intelligent to enable users make appropriate licenses for the secured distribution of their created works. In this paper, we define the semantic-based rights expression and management model for the user generated content. Each user created content can have one or more licenses with different types, reproduction, distribution, and usage. Based on our semantic license model and big data analytics, we can support a new business model in which users can sell and buy their created digital items in a secure environment.

An IoT Knowledge Reengineering Framework for Semantic Knowledge Analytics for BI-Services

In a progressive business intelligence (BI) environment, IoT knowledge analytics are becoming an increasingly challenging problem because of rapid changes of knowledge context scenarios along with increasing data production scales with business requirements that ultimately transform a working knowledge base into a superseded state. Such a superseded knowledge base lacks adequate knowledge context scenarios, and the semantics, rules, frames, and ontology contents may not meet the latest requirements of contemporary BI-services. Thus, reengineering a superseded knowledge base into a renovated knowledge base system can yield greater business value and is more cost effective and feasible than standardising a new system for the same purpose. Thus, in this work, we propose an IoT knowledge reengineering framework (IKR framework) for implementation in a neurofuzzy system to build, organise, and reuse knowledge to provide BI-services to the things (man, machines, places, and processes) involved in business through the network of IoT objects. The analysis and discussion show that the IKR framework can be well suited to creating improved anticipation in IoT-driven BI-applications.

Text-mining-assisted biocuration workflows in Argo.

Biocuration activities have been broadly categorized into the selection of relevant documents, the annotation of biological concepts of interest and identification of interactions between the concepts. Text mining has been shown to have a potential to significantly reduce the effort of biocurators in all the three activities, and various semi-automatic methodologies have been integrated into curation pipelines to support them. We investigate the suitability of Argo, a workbench for building text-mining solutions with the use of a rich graphical user interface, for the process of biocuration. Central to Argo are customizable workflows that users compose by arranging available elementary analytics to form task-specific processing units. A built-in manual annotation editor is the single most used biocuration tool of the workbench, as it allows users to create annotations directly in text, as well as modify or delete annotations created by automatic processing components. Apart from syntactic and semantic analytics, the ever-growing library of components includes several data readers and consumers that support well-established as well as emerging data interchange formats such as XMI, RDF and BioC, which facilitate the interoperability of Argo with other platforms or resources. To validate the suitability of Argo for curation activities, we participated in the BioCreative IV challenge whose purpose was to evaluate Web-based systems addressing user-defined biocuration tasks. Argo proved to have the edge over other systems in terms of flexibility of defining biocuration tasks. As expected, the versatility of the workbench inevitably lengthened the time the curators spent on learning the system before taking on the task, which may have affected the usability of Argo. The participation in the challenge gave us an opportunity to gather valuable feedback and identify areas of improvement, some of which have already been introduced.Database URL: http://argo.nactem.ac.uk

Putting “Human Crowds” in the Loop of Bibliography Evaluation: A Collaborative Working Environment for CSCW Publications

The current impact of financial crisis on societal and scientific frameworks has raised the need to harvest and evaluate vast volumes of data in a socially-mediated interaction context to reduce knowledge gaps and accelerate innovation at a global scale. Existing mechanisms are inefficient for a single human to classify and transform data into knowledge patterns from a large number of publications. This time-consuming and computationally difficult activity requires a substantial cognitive effort grounded on scientific metrics and theoretical foundations to produce quality metadata through different knowledge representations. This paper reports on a work in progress community self-organizing bibliographic information system for semantic analytics focused on what scientific research data mean, and how they can be best interpreted through a division of intellectual labor among social, computer, and citizen scientists. Such a crowd labor ecosystem should be not restricted to traditional bibliometric approaches, attempting to uncover patterns and trends in publication data sets whilst intellectual connections can be examined to demonstrate how a scientific field is conceptually, intellectually, and socially structured.

M-Finder: Uncovering functionally associated proteins from interactome data integrated with GO annotations

BackgroundProtein-protein interactions (PPIs) play a key role in understanding the mechanisms of cellular processes. The availability of interactome data has catalyzed the development of computational approaches to elucidate functional behaviors of proteins on a system level. Gene Ontology (GO) and its annotations are a significant resource for functional characterization of proteins. Because of wide coverage, GO data have often been adopted as a benchmark for protein function prediction on the genomic scale.ResultsWe propose a computational approach, called M-Finder, for functional association pattern mining. This method employs semantic analytics to integrate the genome-wide PPIs with GO data. We also introduce an interactive web application tool that visualizes a functional association network linked to a protein specified by a user. The proposed approach comprises two major components. First, the PPIs that have been generated by high-throughput methods are weighted in terms of their functional consistency using GO and its annotations. We assess two advanced semantic similarity metrics which quantify the functional association level of each interacting protein pair. We demonstrate that these measures outperform the other existing methods by evaluating their agreement to other biological features, such as sequence similarity, the presence of common Pfam domains, and core PPIs. Second, the information flow-based algorithm is employed to discover a set of proteins functionally associated with the protein in a query and their links efficiently. This algorithm reconstructs a functional association network of the query protein. The output network size can be flexibly determined by parameters.ConclusionsM-Finder provides a useful framework to investigate functional association patterns with any protein. This software will also allow users to perform further systematic analysis of a set of proteins for any specific function. It is available online at http://bionet.ecs.baylor.edu/mfinder

Scalable semantic analytics on social networks for addressing the problem of conflict of interest detection

In this article, we demonstrate the applicability of semantic techniques for detection of Conflict of Interest (COI). We explain the common challenges involved in building scalable Semantic Web applications, in particular those addressing connecting-the-dots problems. We describe in detail the challenges involved in two important aspects on building Semantic Web applications, namely, data acquisition and entity disambiguation (or reference reconciliation). We extend upon our previous work where we integrated the collaborative network of a subset of DBLP researchers with persons in a Friend-of-a-Friend social network (FOAF). Our method finds the connections between people, measures collaboration strength, and includes heuristics that use friendship/affiliation information to provide an estimate of potential COI in a peer-review scenario. Evaluations are presented by measuring what could have been the COI between accepted papers in various conference tracks and their respective program committee members. The experimental results demonstrate that scalability can be achieved by using a dataset of over 3 million entities (all bibliographic data from DBLP and a large collection of FOAF documents).

Relationship Web: Blazing Semantic Trails between Web Resources

The authors argue that relationships are at the heart of semantics, and, as such, they envision a Web of relationships to relate content across Web resources. Under this powerful new paradigm, information access over the Web would switch from a mere document-retrieval operation to an information framework that supports insight elicitation and semantic analytics over Web resources.

Geospatial Ontology Development and Semantic Analytics

Geospatial ontology development and semantic knowledge discovery addresses the need for modeling, analyzing and visualizing multimodal information, and is unique in offering integrated analytics that encompasses spatial, temporal and thematic dimensions of information and knowledge. The comprehensive ability to provide integrated analysis from multiple forms of information and use of explicit knowledge make this approach unique. This also involves specification of spatiotemporal thematic ontologies and populating such ontologies with high quality knowledge. Such ontologies form the basis for defining the meaning of important relations terms, such as near or surrounded by, and enable computation of spatiotemporal thematic proximity measures we define. SWETO (Semantic Web Technology Evaluation Ontology) and geospatial extension SWETO-GS are examples of these ontologies. The Geospatial Semantics Analytics (GSA) framework incorporates: (1) the ability to automatically and semi-automatically tract metadata from syntactically (including unstructured, semi-structured and structured data) and semantically heterogeneous and multimodal data from diverse sources; and (2) analytical processing that exploits these ontologies and associated knowledge bases, with integral support for what we term spatiotemporal thematic proximity (STTP) reasoning and interactive visualization capabilities. This paper discusses the results of our geospatial ontology development efforts as well as some new semantic analytics methods on this ontology such as STTP.