A Semantic-Based Information Management System to Support Innovative Product Design

International competition and the rapidly global economy, unified by improved communication and transportation, offer to the consumers an enormous choice of goods and services. The result is that companies now require quality, value, time to market and innovation to be successful in order to win the increasing competition. In the engineering sector this is traduced in need of optimization of the design process and in maximization of re-use of data and knowledge already existing in the company. The “SIMI-Pro” (Semantic Information Management system for Innovative Product design) system addresses specific deficiencies in the conceptual phase of product design when knowledge management, if applied, is often sectorial. Its main contribution is in allowing easy, fast and centralized collection of data from multiple sources and in supporting the retrieval and re-use of a wide range of data that will help stylists and engineers shortening the production cycle. SIMI-Pro will be one of the first prototypes to base its information management and its knowledge sharing system on process ontology and it will demonstrate how the use of centralized network systems, coupled with Semantic Web technologies, can improve inter-working activities and interdisciplinary knowledge sharing

Mapping Between RDBMS And Ontology: A Review

Abstract: Today Semantic web is playing a key role in the intelligent retrieval of information. It is the new-generation Web that tries to represent information such that it can be used by machines not just for display purposes, but for automation, integration, and reuse across applications. It allows the representation and exchange of information in a meaningful way. Ontologies form the backbone of the Semantic Web; they allow machine understanding of information through the links between the information resources and the terms in the ontologies. Ontology describes basic concepts in a domain and defines relations among them. An ontology together with a set of individual instances of classes constitutes a knowledge base. An effort has been made by the Semantic Web community to apply its semantic techniques in open, distributed and heterogeneous Web environments, and for sharing the knowledge in the semantic web. For sharing the knowledge ontologies were introduced, and have grown considerably in number. Building ontology for a specific domain may be start from scratch or by modifying or using an existing ontology. The term Semantic Web (SW) given by Tim Berners Lee is considered as vast concept within itself. Semantic Web (SW) is defined as collection of information linked in a way so that it can be easily processed by machines. It is information in machine form. It contains Semantic Web Documents (SWD's) that are written in RDF or OWL languages. They contain relevant information regarding user's query. Crawlers play vital role in accessing information from SWD's.

Domain ontology usage analysis framework

The Semantic Web (also known as Web of Data) is growing fast and becoming a decentralized knowledge platform for publishing and sharing information. The web ontologies promote the establishment of a shared understanding between data providers and data consumers, allowing for automated information processing and effective and efficient information retrieval. The majority of existing research efforts is focused around ontology engineering, ontology evaluation and ontology evolution. This work goes a step further and evaluates theontology usage. In this paper, we present an Ontology Usage Analysis Framework (OUSAF) and a set of metrics used to measure the ontology usage. The implementation of the proposed framework is illustrated using the example of GoodRelations ontology (GRO). GRO has been well adopted by the semantic ecommerce community, and the OUSAF approach has been used to analyse GRO usage in the dataset comprised of RDF data collected from the web

Intelligent Knowledge Retrieval from Industrial Repositories

Actually, a large amount of information is stored in the industrial repositories. Accessing this information is complicated, and the techniques currently used in metadata and the material chosen by the user do not scale efficiently in large collections. The semantic Web provides a frame of reference that allows sharing and reusing knowledge efficiently. In our work, we present a focus for discovering information in digital repositories based on the application of expert system technologies, and we show a conceptual architecture for a semantic search engine. We used case-based reasoning methodology to create a prototype that supports efficient retrieval knowledge from digital repositories. OntoEnter is a collaborative effort that proposes a new form of interaction between users and digital enterprise repositories, where the latter are adapted to users and their surroundings

Knowledge-based methods for automatic extraction of domain-specific ontologies

Semantic web technology aims at developing methodologies for representing large amount of knowledge in web accessible form. The semantics of knowledge should be easy to interpret and understand by computer programs, so that sharing and utilizing knowledge across the Web would be possible. Domain specific ontologies form the basis for knowledge representation in the semantic web. Research on automated development of ontologies from texts has become increasingly important because manual construction of ontologies is labor intensive and costly, and, at the same time, large amount of texts for individual domains is already available in electronic form. However, automatic extraction of domain specific ontologies is challenging due to the unstructured nature of texts and inherent semantic ambiguities in natural language. Moreover, the large size of texts to be processed renders full-fledged natural language processing methods infeasible. In this dissertation, we develop a set of knowledge-based techniques for automatic extraction of ontological components (concepts, taxonomic and non-taxonomic relations) from domain texts. The proposed methods combine information retrieval metrics, lexical knowledge-base(like WordNet), machine learning techniques, heuristics, and statistical approaches to meet the challenge of the task. These methods are domain-independent and automatic approaches. For extraction of concepts, the proposed WNSCA+{PE, POP} method utilizes the lexical knowledge base WordNet to improve precision and recall over the traditional information retrieval metrics. A WordNet-based approach, the compound term heuristic, and a supervised learning approach are developed for taxonomy extraction. We also developed a weighted word-sense disambiguation method for use with the WordNet-based approach. An unsupervised approach using log-likelihood ratios is proposed for extracting non-taxonomic relations. Further more, a supervised approach is investigated to learn the semantic constraints for identifying relations from prepositional phrases. The proposed methods are validated by experiments with the Electronic Voting and the Tender Offers, Mergers, and Acquisitions domain corpus. Experimental results and comparisons with some existing approaches clearly indicate the superiority of our methods. In summary, a good combination of information retrieval, lexical knowledge base, statistics and machine learning methods in this study has led to the techniques efficient and effective for extracting ontological components automatically

Semantic Web Modelling: Challenges and Opportunities in Small and Large Museum Collections

Semantic Web technologies foster connection and contextualization. They can benefit museum collections by disclosing information in a scalable and interoperable way, aggregating previously heterogeneous and siloed data. Based on formal languages such as RDF, RDFS or OWL they can describe the meaning and the connections among disparate data to define concepts, entities, and relationships and to facilitate multifaceted retrieval, reasoning, data integration and knowledge reuse. Benefits of Semantic Web technologies to the broader DH domain include but not limited to harmonised views of distributed sources, semantic-based content aggregation, enrichment, search, browsing and recommendation. Over the last decades we have witnessed a proliferation of semantic web projects in the broader cultural heritage domain at a national and European level. Infrastructure programmes, such as EUROPEANA, DARIAH, PARTHENOS and ARIADNEplus, to name but a few, have delivered rich interoperable structures and innovations that advanced the tasks of data integration, sharing, analysis, retrieval, and visualisation. As conceptual models mature and expand, and CIDOC-CRM is becoming an undeniable standard in the domain, we reflect on the challenges and opportunities encountered when semantic web technologies are applied both to regional small and large, globally renowned museum collections. The role and application of semantic modelling is examined through two distinct case studies; a) the regional Archaeological Museum of Tripolis (Greece) of limited digital presence, but with a unique collection of regional antiquities that employed semantic methods to enrich and share their digitised collections holdings and b) the Sloane Lab (UK) that aims to aggregate a multitude of catalogue records (both historic and current, from multiple disciplines) dispersed across the British Museum, Natural History Museum and British Library. The presentation delivers useful insight and highlights the opportunities and challenges both for small heritage organisations and large global institutions when applying high-level semantics to withdraw silo barriers of museum items and enable interoperable and multi-layered representations

Structuring and extracting knowledge for the support of hypothesis generation in molecular biology

Background: Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes. Results: We describe progress towards automated support for the generation of biomolecular hypotheses. Semantic Web technologies are used to structure and store knowledge, while a workflow extracts knowledge from text. We designed minimal proto-ontologies in OWL for capturing different aspects of a text mining experiment: the biological hypothesis, text and documents, text mining, and workflow provenance. The models fit a methodology that allows focus on the requirements of a single experiment while supporting reuse and posterior analysis of extracted knowledge from multiple experiments. Our workflow is composed of services from the 'Adaptive Information Disclosure Application' (AIDA) toolkit as well as a few others. The output is a semantic model with putative biological relations, with each relation linked to the corresponding evidence. Conclusion: We demonstrated a 'do-it-yourself' approach for structuring and extracting knowledge in the context of experimental research on biomolecular mechanisms. The methodology can be used to bootstrap the construction of semantically rich biological models using the results of knowledge extraction processes. Models specific to particular experiments can be constructed that, in turn, link with other semantic models, creating a web of knowledge that spans experiments. Mapping mechanisms can link to other knowledge resources such as OBO ontologies or SKOS vocabularies. AIDA Web Services can be used to design personalized knowledge extraction procedures. In our example experiment, we found three proteins (NF-Kappa B, p21, and Bax) potentially playing a role in the interplay between nutrients and epigenetic gene regulation

Applying semantic web technologies to knowledge sharing in aerospace engineering

This paper details an integrated methodology to optimise Knowledge reuse and sharing, illustrated with a use case in the aeronautics domain. It uses Ontologies as a central modelling strategy for the Capture of Knowledge from legacy docu-ments via automated means, or directly in systems interfacing with Knowledge workers, via user-defined, web-based forms. The domain ontologies used for Knowledge Capture also guide the retrieval of the Knowledge extracted from the data using a Semantic Search System that provides support for multiple modalities during search. This approach has been applied and evaluated successfully within the aerospace domain, and is currently being extended for use in other domains on an increasingly large scale