Semantic and Syntactic Matching of Heterogeneous e-Catalogues

In e-procurement, companies use e-catalogues to exchange product infor-mation with business partners. Matching e-catalogues with product requests helps the suppliers to identify the best business opportunities in B2B e-Marketplaces. But various ways to specify products and the large variety of e-catalogue formats used by different business actors makes it difficult. This Ph.D. thesis aims to discover potential syntactic and semantic rela-tionships among product data in procurement documents and exploit it to find similar e-catalogues. Using a Concept-based Vector Space Model, product data and its semantic interpretation is used to find the correlation of product data. In order to identify important terms in procurement documents, standard e-catalogues and e-tenders are used as a resource to train a Product Named Entity Recognizer to find B2B product mentions in e-catalogues. The proposed approach makes it possible to use the benefits of all availa-ble semantic resources and schemas but not to be dependent on any specific as-sumption. The solution can serve as a B2B product search system in e-Procurement platforms and e-Marketplaces

Building a Semantic Tendering System

In the new B2B e-commerce arena, applications such as auctions and data exchange are growing rapidly. However, Web content is currently designed for human consumption rather than computer manipulation. This limits the possibility of Web automation. Fortunately, the new development of the Semantic Web that allows Web pages to provide information not only in terms of their content, but also in terms of the properties of that content, can be used for automation. Electronic tendering systems are among the successfully commercial systems that can tremendously benefit from the availability of Semantic Web. This study proposes an e-tendering system that uses the Semantic Web to investigate the automatic negotiation process. The system is built in a P2P environment to simulate a two-player negotiation. It is found that the ontology of semantic information can be used to locate qualified suppliers and precede negotiation. The bargaining power of each party is then determined by the relative magnitude of the negotiators’ respective costs of haggling and the utility that varies with the degree of risk preference. Our experiments showed that applying automatic negotiation strategies to e-tendering system in semantic web can reflect the risk preference of the participants

Methodologies, tools and languages for building ontologies. Where is their meeting point?

In this paper we review and compare the main methodologies, tools and languages for building ontologies that have been reported in the literature, as well as the main relationships among them. Ontology technology is nowadays mature enough: many methodologies, tools and languages are already available. The future work in this field should be driven towards the creation of a common integrated workbench for ontology developers to facilitate ontology development, exchange, evaluation, evolution and management, to provide methodological support for these tasks, and translations to and from different ontology languages. This workbench should not be created from scratch, but instead integrating the technology components that are currently available

A PROCESS-BASED APPROACH TO KNOWLEDGE MANAGEMENT

This paper analyses the relationship between business process modelling, knowledge management and information systems development projects. The paper’s main objective is to present business rules as the encoded knowledge of corporate business practices. Further, it introduces a rule-based business activity meta-model as a repository in which business knowledge can be captured and traced from their origin in the business environment through to their implementation in information systems. The case study of the Croatian Ministry of Finance is presented, discussing the practical experience in integrating business process repository and organisational knowledge as the foundation for information system development

The Management of Debris Flow in Disaster Prevention using an Ontology-based Knowledge Management System

In recently years, the government, academia and business have applied different information technologies to disaster prevention and diverse web sites have been developed. Although these web sites provide a large number of data about disaster-prevention, they are knowledge poor in nature. Furthermore, disaster-prevention is a knowledge-intensive task and a potential knowledge management system can overcome the shortcoming of knowledge poor. On the other hand, ontology design plays the key role toward designing a successful knowledge management system. In this paper, we introduce a three-stage life cycle for ontology design for supporting the service of disaster prevention of debris flow and propose a framework of an ontology-based knowledge management system with the KAON API environment. In addition, by appealing to the technology of component reuse, the system is developed at lower cost thus knowledge workers can focus on the design of ontology and knowledge objects. The objectives of the proposed system is to facilitate knowledge accumulation, knowledge reuse and dissemination for the management of disaster prevention. This work is expected to enable the promotion of the traditional disaster management of debris flow towards the so-called knowledge-driven decision support services

Ontologies as a Set to Describe Legal Information

The article discusses the features of legal knowledge ontology creation. It is determined that ontology is the most appropriate way to describe legal knowledge. The particular qualities of legal information and the features of the language of a right were investigated. A review of legal knowledge ontologies that are used in various branches of law was made. The properties of legal information and the requirements for regulatory documentation in Ukraine were described. The formalization of the structure of the ontology database was presented, taking into account the required attributes of the concepts. The methodology of the work with the knowledge base was proposed to use the independent work of many users. The legal knowledge ontology at the law university was filled by all users of the software package, but experts checked the quality of this content. Crowdsourcing was considered as the main technique of the ontology filling process. Several branches of the ontology of legal knowledge were filled. The results of the experimental operation of this ontology by university students were analyzed

Towards an AEC-AI Industry Optimization Algorithmic Knowledge Mapping: An Adaptive Methodology for Macroscopic Conceptual Analysis

[EN] The Architecture, Engineering, and Construction (AEC) Industry is one of the most important productive sectors, hence also produce a high impact on the economic balances, societal stability, and global challenges in climate change. Regarding its adoption of technologies, applications and processes is also recognized by its status-quo, its slow innovation pace, and the conservative approaches. However, a new technological era - Industry 4.0 fueled by AI- is driving productive sectors in a highly pressurized global technological competition and sociopolitical landscape. In this paper, we develop an adaptive approach to mining text content in the literature research corpus related to the AEC and AI (AEC-AI) industries, in particular on its relation to technological processes and applications. We present a rst stage approach to an adaptive assessment of AI algorithms, to form an integrative AI platform in the AEC industry, the AEC-AI industry 4.0. At this stage, a macroscopic adaptive method is deployed to characterize ``Optimization,'' a key term in AEC-AI industry, using a mixed methodology incorporating machine learning and classical evaluation process. Our results show that effective use of metadata, constrained search queries, and domain knowledge allows getting a macroscopic assessment of the target concept. This allows the extraction of a high-level mapping and conceptual structure characterization of the literature corpus. The results are comparable, at this level, to classical methodologies for the literature review. In addition, our method is designed for an adaptive assessment to incorporate further stages.This work was supported by the CONICYT/FONDECYT/INICIACION under Grant 11180056 to Jose Garcia and the Spanish Ministry of Science and Innovation through the FEDER Funding under Project PID2020-117056RB-I00 to Victor Yepes.Maureira, C.; Pinto, H.; Yepes, V.; García, J. (2021). Towards an AEC-AI Industry Optimization Algorithmic Knowledge Mapping: An Adaptive Methodology for Macroscopic Conceptual Analysis. IEEE Access. 9:110842-110879. https://doi.org/10.1109/ACCESS.2021.3102215S110842110879