Fulfilling the needs of a metadata creator and analyst : An investigation of RDF browsing and visualization tools

The realization of Semantic Web vision is based on the creation and use of semantic web content which needs software tools both for semantic web developers and end users. Over the past few years, semantic web software tools like ontology editors and triple storage systems have emerged and are growing in maturity with time. While working on a large triple dataset during the course of a research aiming at a life-long “semantic” repository of personal information, besides other semantic web tools, we used several RDF browsing and visualization tools for analyzing our data. This analysis included ensuring the correctness of the data, conformance of instance data to the ontology, finding patterns and trails in the data, cross-checking and evaluating inferred data, etc. We found that many of the features needed by a metadata creator and analyst are missing from these tools. This paper presents an investigation of the tools that are used for browsing and visualizing RDF datasets. It first identifies the browsing and visualization features required by a semantic web developer and a metadata creator and analyst and then based on those features evaluates the most common RDF browsing and visualization tools available till date. We conclude this paper with recommendations for requirements to be fulfilled for future semantic web browsing and visualizationThe past and the future of information systems: 1976-2006 and beyondRed de Universidades con Carreras en Informática (RedUNCI

Building XML data warehouse based on frequent patterns in user queries

[Abstract]: With the proliferation of XML-based data sources available across the Internet, it is increasingly important to provide users with a data warehouse of XML data sources to facilitate decision-making processes. Due to the extremely large amount of XML data available on web, unguided warehousing of XML data turns out to be highly costly and usually cannot well accommodate the users’ needs in XML data acquirement. In this paper, we propose an approach to materialize XML data warehouses based on frequent query patterns discovered from historical queries issued by users. The schemas of integrated XML documents in the warehouse are built using these frequent query patterns represented as Frequent Query Pattern Trees (FreqQPTs). Using hierarchical clustering technique, the integration approach in the data warehouse is flexible with respect to obtaining and maintaining XML documents. Experiments show that the overall processing of the same queries issued against the global schema become much efficient by using the XML data warehouse built than by directly searching the multiple data sources

ONGOING SOCIAL SUSTAINABILITY COMPLIANCE MONITORING IN SUPPLY CHAINS

Social sustainability issus such as child labour at a supplier pose significant reputational risk for companies. Therefore, many companies now require that suppliers follow certain standards or codes of conduct. However, in today´s complex supply chains with hundreds of sourcing locations, ongoing monitoring of compliance through audits for every supplier is hardly practical. Consequntly, an information technology system is investigated as a tool to establish ongoing monitoring of suppliers based on available information with regard to the risk that suppliers breach the compliance rules defined. This paper describes work on a system that uses a Bayesian network to integrate evidence from multiple public and private data sources in order to rank suppliers dynamically. A particular focus of future work will be a prototype based on the issu of child labour and the advantages of applying text mining methods

A persuasive approach for indoor environment tidiness

The tidiness of an environment can provide important information about individuals who are involved in the target environment, such as their behavior, health and mental condition. In this paper, we describe a vision-based approach for measuring tidiness and persuading users to be tidy. Tidiness measurement is based on analyzing changes in an image series and comparing them to an image from an ideally ordered environment. The persuasion process will be carried out by a combination of notifications and rankings. The existing personal and social communication channels of users, such as email and social networking accounts will be employed to persuade them to change their behavior and be-come more obliged to tidiness

Fulfilling the needs of a metadata creator and analyst : An investigation of RDF browsing and visualization tools

The realization of Semantic Web vision is based on the creation and use of semantic web content which needs software tools both for semantic web developers and end users. Over the past few years, semantic web software tools like ontology editors and triple storage systems have emerged and are growing in maturity with time. While working on a large triple dataset during the course of a research aiming at a life-long “semantic” repository of personal information, besides other semantic web tools, we used several RDF browsing and visualization tools for analyzing our data. This analysis included ensuring the correctness of the data, conformance of instance data to the ontology, finding patterns and trails in the data, cross-checking and evaluating inferred data, etc. We found that many of the features needed by a metadata creator and analyst are missing from these tools. This paper presents an investigation of the tools that are used for browsing and visualizing RDF datasets. It first identifies the browsing and visualization features required by a semantic web developer and a metadata creator and analyst and then based on those features evaluates the most common RDF browsing and visualization tools available till date. We conclude this paper with recommendations for requirements to be fulfilled for future semantic web browsing and visualizationThe past and the future of information systems: 1976-2006 and beyondRed de Universidades con Carreras en Informática (RedUNCI

Evaluating Prompt-Based Question Answering for Object Prediction in the Open Research Knowledge Graph

Recent investigations have explored prompt-based training of transformer language models for new text genres in low-resource settings. This approach has proven effective in transferring pre-trained or fine-tuned models to resource-scarce environments. This work presents the first results on applying prompt-based training to transformers for scholarly knowledge graph object prediction. Methodologically, it stands out in two main ways: 1) it deviates from previous studies that propose entity and relation extraction pipelines, and 2) it tests the method in a significantly different domain, scholarly knowledge, evaluating linguistic, probabilistic, and factual generalizability of large-scale transformer models. Our findings demonstrate that: i) out-of-the-box transformer models underperform on the new scholarly domain, ii) prompt-based training improves performance by up to 40% in relaxed evaluation, and iii) tests of the models in a distinct domain reveals a gap in capturing domain knowledge, highlighting the need for increased attention and resources in the scholarly domain for transformer models

Complex matching of RDF datatype properties

Property mapping is a fundamental component of ontology matching, and yet there is little support that goes beyond the identification of single property matches. Real data often requires some degree of composition, trivially exemplified by the mapping of "first name" and "last name" to "full name" on one end, to complex matchings, such as parsing and pairing symbol/digit strings to SSN numbers, at the other end of the spectrum. In this paper, we propose a two-phase instance-based technique for complex datatype property matching. Phase 1 computes the Estimate Mutual Information matrix of the property values to (1) find simple, 1:1 matches, and (2) compute a list of possible complex matches. Phase 2 applies Genetic Programming to the much reduced search space of candidate matches to find complex matches. We conclude with experimental results that illustrate how the technique works. Furthermore, we show that the proposed technique greatly improves results over those obtained if the Estimate Mutual Information matrix or the Genetic Programming techniques were to be used independently. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40285-2_18

The Digitalization of Bioassays in the Open Research Knowledge Graph

Background: Recent years are seeing a growing impetus in the semantification of scholarly knowledge at the fine-grained level of scientific entities in knowledge graphs. The Open Research Knowledge Graph (ORKG, orkg.org) represents an important step in this direction, with thousands of scholarly contributions as structured, fine-grained, machine-readable data. There is a need, however, to engender change in traditional community practices of recording contributions as unstructured, non-machine-readable text. For this in turn, there is a strong need for AI tools designed for scientists that permit easy and accurate semantification of their scholarly contributions. We present one such tool, ORKG-assays. Implementation: ORKG-assays is a freely available AI micro-service in ORKG written in Python designed to assist scientists obtain semantified bioassays as a set of triples. It uses an AI-based clustering algorithm which on gold-standard evaluations over 900 bioassays with 5,514 unique property-value pairs for 103 predicates shows competitive performance. Results and Discussion: As a result, semantified assay collections can be surveyed on the ORKG platform via tabulation or chart-based visualizations of key property values of the chemicals and compounds offering smart knowledge access to biochemists and pharmaceutical researchers in the advancement of drug development

UbiqLog: a generic mobile phone based life-log framework

Smart phones are conquering the mobile phone market; they are not just phones they also act as media players, gaming consoles, personal calendars, storage, etc. They are portable computers with fewer computing capabilities than personal computers. However unlike personal computers users can carry their smartphone with them at all times. The ubiquity of mobile phones and their computing capabilities provide an opportunity of using them as a life logging device. Life-logs (personal e-memories) are used to record users' daily life events and assist them in memory augmentation. In a more technical sense, life-logs sense and store users' contextual information from their environment through sensors, which are core components of life-logs. Spatio-temporal aggregation of sensor information can be mapped to users' life events. We propose UbiqLog, a lightweight, configurable and extendable life-log framework that uses mobile phone as a device for life logging. The proposed framework extends previous research in this field, which investigated mobile phones as life-log tool through continuous sensing. Its openness in terms of sensor configuration allows developers to create exible, multipurpose life-log tools. In addition to that this framework contains a data model and an architecture, which can be used as reference model for further life-log development, including its extension to other devices, such as ebook readers, T.V.s, etc