Ontology Enrichment by Discovering Multi-Relational Association Rules from Ontological Knowledge Bases

International audienceIn the Semantic Web context, OWL ontologies represent the con-ceptualization of domains of interest while the corresponding as-sertional knowledge is given by the heterogeneous Web resources referring to them. Being strongly decoupled, ontologies and assertion can be out-of-sync. An ontology can be incomplete, noisy and sometimes inconsistent with regard to the actual usage of its conceptual vocabulary in the assertions. Data mining can support the discovery of hidden knowledge patterns in the data, to enrich the ontologies. We present a method for discovering multi-relational association rules, coded in SWRL, from ontological knowledge bases. Unlike state-of-the-art approaches, the method is able to take the intensional knowledge into account. Furthermore, since discovered rules are represented in SWRL, they can be straightforwardly integrated within the ontology, thus (i) enriching its expressive power and (ii) augmenting the assertional knowledge that can be derived. Discovered rules may also suggest new axioms to be added to the ontology. We performed experiments on publicly available ontologies validating the performances of our approach

A Semantic Web of Know-How: Linked Data for Community-Centric Tasks

This paper proposes a novel framework for representing community know-how on the Semantic Web. Procedural knowledge generated by web communities typically takes the form of natural language instructions or videos and is largely unstructured. The absence of semantic structure impedes the deployment of many useful applications, in particular the ability to discover and integrate know-how automatically. We discuss the characteristics of community know-how and argue that existing knowledge representation frameworks fail to represent it adequately. We present a novel framework for representing the semantic structure of community know-how and demonstrate the feasibility of our approach by providing a concrete implementation which includes a method for automatically acquiring procedural knowledge for real-world tasks.Comment: 6th International Workshop on Web Intelligence & Communities (WIC14), Proceedings of the companion publication of the 23rd International Conference on World Wide Web (WWW 2014

Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

Integrating Distributed Sources of Information for Construction Cost Estimating using Semantic Web and Semantic Web Service technologies

A construction project requires collaboration of several organizations such as owner, designer, contractor, and material supplier organizations. These organizations need to exchange information to enhance their teamwork. Understanding the information received from other organizations requires specialized human resources. Construction cost estimating is one of the processes that requires information from several sources including a building information model (BIM) created by designers, estimating assembly and work item information maintained by contractors, and construction material cost data provided by material suppliers. Currently, it is not easy to integrate the information necessary for cost estimating over the Internet. This paper discusses a new approach to construction cost estimating that uses Semantic Web technology. Semantic Web technology provides an infrastructure and a data modeling format that enables accessing, combining, and sharing information over the Internet in a machine processable format. The estimating approach presented in this paper relies on BIM, estimating knowledge, and construction material cost data expressed in a web ontology language. The approach presented in this paper makes the various sources of estimating data accessible as Simple Protocol and Resource Description Framework Query Language (SPARQL) endpoints or Semantic Web Services. We present an estimating application that integrates distributed information provided by project designers, contractors, and material suppliers for preparing cost estimates. The purpose of this paper is not to fully automate the estimating process but to streamline it by reducing human involvement in repetitive cost estimating activities

Representing and coding the knowledge embedded in texts of Health Science Web published articles

Despite the fact that electronic publishing is a common activity to scholars electronic journals are still based in the print model and do not take full advantage of the facilities offered by the Semantic Web environment. This is a report of the results of a research project with the aim of investigating the possibilities of electronic publishing journal articles both as text for human reading and in machine readable format recording the new knowledge contained in the article. This knowledge is identified with the scientific methodology elements such as problem, methodology, hypothesis, results, and conclusions. A model integrating all those elements is proposed which makes explicit and records the knowledge embedded in the text of scientific articles as an ontology. Knowledge thus represented enables its processing by intelligent software agents The proposed model aims to take advantage of these facilities enabling semantic retrieval and validation of the knowledge contained in articles. To validate and enhance the model a set of electronic journal articles were analyzed

Database marketing intelligence methodology supported by ontologies and knowlegde discovery in databases

Tese de doutoramento em Tecnologias e Sistemas de InformaçãoActualmente as organizações actuam em ambientes caracterizados pela inconstância, elevada competitividade e pressão no desenvolvimento de novas abordagens ao mercado e aos clientes. Nesse contexto, o acesso à informação, o suporte à tomada de decisão e a partilha de conhecimento tornam-se essenciais para o desempenho organizativo. No domínio do marketing têm surgido diversas abordagens para a exploração do conteúdo das suas bases de dados. Uma das abordagens, utilizadas com maior sucesso, tem sido o processo para a descoberta de conhecimento em bases de dados. Por outro lado, a necessidade de representação e partilha de conhecimento tem contribuído para um crescente desenvolvimento das ontologias em áreas diversas como sejam medicina, aviação ou segurança. O presente trabalho cruza diversas áreas: tecnologias e sistemas de informação (em particular a descoberta de conhecimento), o marketing (especificamente o database marketing) e as ontologias. O objectivo principal desta investigação foca o papel das ontologias em termos de suporte e assistência ao processo de descoberta de conhecimento em bases de dados num contexto de database marketing. Através de abordagens distintas foram formuladas duas ontologias: ontologia para o processo de descoberta de conhecimento em bases de dados e, a ontologia para o processo database marketing suportado na extracção de conhecimento em bases de dados (com reutilização da ontologia anterior). O processo para licitação e validação de conhecimento, baseou-se no método de Delphi (ontologia de database marketing) e no processo de investigação baseada na revisão de literatura (ontologia de descoberta de conhecimento). A concretização das ontologias suportou-se em duas metodologias: metodologia methontology, para a ontologia de descoberta de conhecimento e metodologia 101 para a ontologia de database marketing. A última, evidencia a reutilização de ontologias, viabilizando assim a reutilização da ontologia de descoberta de conhecimento na ontologia de database marketing. Ambas ontologias foram desenvolvidas sobre a ferramenta Protege-OWL permitindo não só a criação de toda a hierarquia de classes, propriedades e relações, como também, a realização de métodos de inferência através de linguagens baseadas em regras de Web semântica. Posteriormente, procedeu-se à experimentação da ontologia em casos práticos de extracção de conhecimento a partir de bases de dados de marketing. O emprego das ontologias neste contexto de investigação, representa uma abordagem pioneira e inovadora, uma vez que são propostas para assistirem em cada uma das fases do processo de extracção de conhecimento em bases de dados através de métodos de inferência. È assim possível assistir o utilizador em cada fase do processo de database marketing em acções tais como de selecção de actividades de marketing em função dos objectivos de marketing (e.g., perfil de cliente), em acções de selecção dados (e.g., tipos de dados a utilizar em função da actividade a desenvolver) ou mesmo no processo de selecção de algoritmos (e.g. inferir sobre o tipo de algoritmo a usar em função do objectivo definido). A integração das duas ontologias num contexto mais lato permite, propor uma metodologia com vista ao efectivo suporte do processo de database marketing baseado no processo de descoberta de conhecimento em bases de dados, denominado nesta dissertação como: Database Marketing Intelligence. Para a demonstração da viabilidade da metodologia proposta foi seguido o método action-research com o qual se observou e testou o papel das ontologias no suporte à descoberta de conhecimento em bases de dados (através de um caso prático) num contexto de database marketing. O trabalho de aplicação prática decorreu sobre uma base de dados real relativa a um cartão de fidelização de uma companhia petrolífera a operar em Portugal. Os resultados obtidos serviram para demonstrar em duas vertente o sucesso da abordagem proposta: por um lado foi possível formalizar e acompanhar todo o processo de descoberta de conhecimento em bases de dados; por outro lado, foi possível perspectivar uma metodologia para um domínio concreto suportado por ontologias (suporte á decisão na selecção de métodos e tarefas) e na descoberta de conhecimento em bases de dados.Nowadays, the environment in which companies work is turbulent, very competitive and pressure in the development of new approaches to the market and clients. In this context, the access to information, the decision support and knowledge sharing become essential for the organization performance. In the marketing domain several approaches for the exploration of database exploration have emerged. One of the most successfully used approaches has been the knowledge discovery process in databases. On the other hand, the necessity of knowledge representation and sharing and contributed to a growing development of ontologies in several areas such as in the medical, the aviation or safety areas. This work crosses several areas: technology and information systems (specifically knowledge discovery in databases), marketing (specifically database marketing) and ontologies in general. The main goal of this investigation is to focus on the role of ontologies in terms of support and aid to the knowledge discovery process in databases in a database marketing context. Through distinct approaches two ontologies were created: ontology for the knowledge discovery process in databases, and the ontology for the database marketing process supported on the knowledge extraction in databases (reusing the former ontology). The elicitation and validation of knowledge process was based on the Delphi method (database marketing ontology) and the investigation process was based on literature review (knowledge discovery ontology). The carrying out of both ontologies was based on two methodologies: methontology methodology, for the knowledge discovery process and 101 methodology for the database marketing ontology. The former methodology, stresses the reusing of ontologies, allowing the reusing of the knowledge discovery ontology in the database marketing ontology. Both ontologies were developed with the Protege-OWL tool. This tool allows not only the creation of all the hierarchic classes, properties and relationships, but also the carrying out of inference methods through web semantics based languages. Then, the ontology was tested in practical cases of knowledge extraction from marketing databases. The application of ontologies in this investigation represents a pioneer and innovative approach, once they are proposed to aid and execute an effective support in each phase of the knowledge extraction from databases in the database marketing context process. Through inference processes on the knowledge base created it was possible to assist the user in each phase of the database marketing process such as, in marketing activity selection actions according to the marketing objectives (e.g., client profile) or in data selection actions (e.g., type of data to use according to the activity to be preformed. In relation to aid in the knowledge discovery process in databases, it was also possible to infer on the type of algorithm to use according to the defined objective or even according to the type of data pre-processing activities to develop regarding the type of data and type of attribute information. The integration of both ontologies in a more general context allows proposing a methodology aiming to the effective support of the database marketing process based on the knowledge discovery process in databases, named in this dissertation as: Database Marketing Intelligence. To demonstrate the viability of the proposed methodology the action-research method was followed with which the role of ontologies in assisting knowledge discovery in databases (through a practical case) in the database marketing context was observed and tested. For the practical application work a real database about a customer loyalty card from a Portuguese oil company was used. The results achieved demonstrated the success of the proposed approach in two ways: on one hand, it was possible to formalize and follow the whole knowledge discovery in databases process; on the other hand, it was possible to perceive a methodology for a concrete domain supported by ontologies (support of the decision in the selection of methods and tasks) and in the knowledge discovery in databases.Fundação para a Ciência e a Tecnologia (FCT) - SFRH/BD/36541/200

Applications and Uses of Dental Ontologies

The development of a number of large-scale semantically-rich ontologies for biomedicine attests to the interest of life science researchers and clinicians in Semantic Web technologies. To date, however, the dental profession has lagged behind other areas of biomedicine in developing a commonly accepted, standardized ontology to support the representation of dental knowledge and information. This paper attempts to identify some of the potential uses of dental ontologies as part of an effort to motivate the development of ontologies for the dental domain. The identified uses of dental ontologies include support for advanced data analysis and knowledge discovery capabilities, the implementation of novel education and training technologies, the development of information exchange and interoperability solutions, the better integration of scientific and clinical evidence into clinical decision-making, and the development of better clinical decision support systems. Some of the social issues raised by these uses include the ethics of using patient data without consent, the role played by ontologies in enforcing compliance with regulatory criteria and legislative constraints, and the extent to which the advent of the Semantic Web introduces new training requirements for dental students. Some of the technological issues relate to the need to extract information from a variety of resources (for example, natural language texts), the need to automatically annotate information resources with ontology elements, and the need to establish mappings between a variety of existing dental terminologies

PowerAqua: fishing the semantic web

The Semantic Web (SW) offers an opportunity to develop novel, sophisticated forms of question answering (QA). Specifically, the availability of distributed semantic markup on a large scale opens the way to QA systems which can make use of such semantic information to provide precise, formally derived answers to questions. At the same time the distributed, heterogeneous, large-scale nature of the semantic information introduces significant challenges. In this paper we describe the design of a QA system, PowerAqua, designed to exploit semantic markup on the web to provide answers to questions posed in natural language. PowerAqua does not assume that the user has any prior information about the semantic resources. The system takes as input a natural language query, translates it into a set of logical queries, which are then answered by consulting and aggregating information derived from multiple heterogeneous semantic sources