Ontology methodology building criteria for crowdsourcing innovation intermediaries

Crowdsourcing innovation intermediaries are organizations that mediate the communication and relationship between companies that aspire to solve some problem or to take advantage of any business opportunity with a crowd that is prone to give ideas based on their knowledge, experience and wisdom. A significant part of the activity of these intermediaries is carried out by using a web platform that takes advantage of web 2.0 tools to implement its capabilities. Thus, ontologies are presented as an appropriate strategy to represent the knowledge inherent to this activity and therefore the accomplishment of interoperability between machines and systems. In this paper we present an ontology roadmap for developing crowdsourcing innovation ontology of the intermediation process. We start making a literature review on ontology building, analyze and compare ontologies that propose the development from scratch with the ones that propose reusing other ontologies, and present the criteria for selecting the methodology. We also review enterprise and innovation ontologies known in literature. Finally, are taken some conclusions and presented the roadmap for building crowdsourcing innovation intermediary ontology.

Essentials in Ontology Engineering: Methodologies, Languages, and Tools

In the beginning of the 90s, ontology development was similar to an art: ontology developers did not have clear guidelines on how to build ontologies but only some design criteria to be followed. Work on principles, methods and methodologies, together with supporting technologies and languages, made ontology development become an engineering discipline, the so-called Ontology Engineering. Ontology Engineering refers to the set of activities that concern the ontology development process and the ontology life cycle, the methods and methodologies for building ontologies, and the tool suites and languages that support them. Thanks to the work done in the Ontology Engineering field, the development of ontologies within and between teams has increased and improved, as well as the possibility of reusing ontologies in other developments and in final applications. Currently, ontologies are widely used in (a) Knowledge Engineering, Artificial Intelligence and Computer Science, (b) applications related to knowledge management, natural language processing, e-commerce, intelligent information integration, information retrieval, database design and integration, bio-informatics, education, and (c) the Semantic Web, the Semantic Grid, and the Linked Data initiative. In this paper, we provide an overview of Ontology Engineering, mentioning the most outstanding and used methodologies, languages, and tools for building ontologies. In addition, we include some words on how all these elements can be used in the Linked Data initiative.

An ontology roadmap for crowdsourcing innovation intermediaries

Ontologies have proliferated in the last years, essentially justified by the need of achieving a consensus in the multiple representations of reality inside computers, and therefore the accomplishment of interoperability between machines and systems. Ontologies provide an explicit conceptualization that describes the semantics of the data. Crowdsourcing innovation intermediaries are organizations that mediate the communication and relationship between companies that aspire to solve some problem or to take advantage of any business opportunity with a crowd that is prone to give ideas based on their knowledge, experience and wisdom, taking advantage of web 2.0 tools. Various ontologies have emerged, but at the best of our knowledge, there isn’t any ontology that represents the entire process of intermediation of crowdsourcing innovation. In this paper we present an ontology roadmap for developing crowdsourcing innovation ontology of the intermediation process. Over the years, several authors have proposed some distinct methodologies, by different proposals of combining practices, activities, languages, according to the project they were involved in. We start making a literature review on ontology building, and analyse and compare ontologies that propose the development from scratch with the ones that propose reusing other ontologies. We also review enterprise and innovation ontologies known in literature. Finally, are presented the criteria for selecting the methodology and the roadmap for building crowdsourcing innovation intermediary ontology.

Methodology for collaborative enterprise reference ontology building

Dissertation presented at Faculdade de Ciências e Tecnologia of Universidade Nova de Lisboa to obtain the Master degree in Electrical Engineering and Computer Science

Annotating multimedia for community folksonomy and ontology building

This is an audio recording which introduces and summarises this project.

An architecture for scaling ontology networks

Constructing ontology networks typically occurs at design time at the hands of knowledge engineers who assemble their components statically. There are, however, use cases where ontology networks need to be assembled upon request and processed at runtime, without altering the stored ontologies and without tampering with one another. These are what we call "virtual [ontology] networks", and keeping track of how an ontology changes in each virtual network is called "multiplexing". Issues may arise from the connectivity of ontology networks. In many cases, simple flat import schemes will not work, because many ontology managers can cause property assertions to be erroneously interpreted as annotations and ignored by reasoners. Also, multiple virtual networks should optimize their cumulative memory footprint, and where they cannot, this should occur for very limited periods of time. We claim that these problems should be handled by the software that serves these ontology networks, rather than by ontology engineering methodologies. We propose a method that spreads multiple virtual networks across a 3-tier structure, and can reduce the amount of erroneously interpreted axioms, under certain raw statement distributions across the ontologies. We assumed OWL as the core language handled by semantic applications in the framework at hand, due to the greater availability of reasoners and rule engines. We also verified that, in common OWL ontology management software, OWL axiom interpretation occurs in the worst case scenario of pre-order visit. To measure the effectiveness and space-efficiency of our solution, a Java and RESTful implementation was produced within an Apache project. We verified that a 3-tier structure can accommodate reasonably complex ontology networks better, in terms of the expressivity OWL axiom interpretation, than flat-tree import schemes can. We measured both the memory overhead of the additional components we put on top of traditional ontology networks, and the framework's caching capabilities.

A Method for Developing Ontologies from User-Generated Classication Systems

Abstract Web 2.0 applications enabled users to classify information resources using their own vocabularies. The bottom-up nature of these user-generated classification systems have turned them into interesting knowledge sources, since they provide a rich terminology generated by potentially large user communities. Previous research has shown that it is possible to elicit some emergent semantics from the aggregation of individual classifications in these systems. However the generation of ontologies from them is still an open research problem. In this thesis we address the problem of how to tap into user-generated classification systems for building domain ontologies. Our objective is to design a method to develop domain ontologies from user-generated classifications systems. To do so, we rely on ontologies in the Web of Data to formalize the semantics of the knowledge collected from the classification system. Current ontology development methodologies have recognized the importance of reusing knowledge from existing resources. Thus, our work is framed within the NeOn methodology scenario for building ontologies by reusing and reengineering non-ontological resources. The main contributions of this work are: An integrated method to develop ontologies from user-generated classification systems. With this method we extract a domain terminology from the classification system and then we formalize the semantics of this terminology by reusing ontologies in the Web of Data. Identification and adaptation of existing techniques for implementing the activities in the method so that they can fulfill the requirements of each activity. A novel study about emerging semantics in user-generated lists. Resumen La web 2.0 permitió a los usuarios clasificar recursos de información usando su propio vocabulario. Estos sistemas de clasificación generados por usuarios son recursos interesantes para la extracción de conocimiento debido principalmente a que proveen una extensa terminología generada por grandes comunidades de usuarios. Se ha demostrado en investigaciones previas que es posible obtener una semántica emergente de estos sistemas. Sin embargo la generación de ontologías a partir de ellos es todavía un problema de investigación abierto. Esta tesis trata el problema de cómo aprovechar los sistemas de clasificación generados por usuarios en la construcción de ontologías de dominio. Así el objetivo de la tesis es diseñar un método para desarrollar ontologías de dominio a partir de sistemas de clasificación generados por usuarios. El método propuesto reutiliza conceptualizaciones existentes en ontologías publicadas en la Web de Datos para formalizar la semántica del conocimiento que se extrae del sistema de clasificación. Por tanto, este trabajo está enmarcado dentro del escenario para desarrollar ontologías mediante la reutilización y reingeniería de recursos no ontológicos que se ha definido en la Metodología NeOn. Las principales contribuciones de este trabajo son: Un método integrado para desarrollar una ontología de dominio a partir de sistemas de clasificación generados por usuarios. En este método se extrae una terminología de dominio del sistema de clasificación y posteriormente se formaliza su semántica reutilizando ontologías en la Web de Datos. La identificación y adaptación de un conjunto de técnicas para implementar las actividades propuestas en el método de tal manera que puedan cumplir automáticamente los requerimientos de cada actividad. Un novedoso estudio acerca de la semántica emergente en las listas generadas por usuarios en la Web.

Combining data mining and ontology engineering to enrich ontologies and linked data

In this position paper, we claim that the need for time consuming data preparation and result interpretation tasks in knowledge discovery, as well as for costly expert consultation and consensus building activities required for ontology building can be reduced through exploiting the interplay of data mining and ontology engineering. The aim is to obtain in a semi-automatic way new knowledge from distributed data sources that can be used for inference and reasoning, as well as to guide the extraction of further knowledge from these data sources. The proposed approach is based on the creation of a novel knowledge discovery method relying on the combination, through an iterative ?feedbackloop?, of (a) data mining techniques to make emerge implicit models from data and (b) pattern-based ontology engineering to capture these models in reusable, conceptual and inferable artefacts.

User-centred ontology learning for knowledge management

Automatic ontology building is a vital issue in many fields where they are currently built manually. This paper presents a user-centred methodology for ontology construction based on the use of Machine Learning and Natural Language Processing. In our approach, the user selects a corpus of texts and sketches a preliminary ontology (or selects an existing one) for a domain with a preliminary vocabulary associated to the elements in the ontology (lexicalisations). Examples of sentences involving such lexicalisation (e.g. ISA relation) in the corpus are automatically retrieved by the system. Retrieved examples are validated by the user and used by an adaptive Information Extraction system to generate patterns that discover other lexicalisations of the same objects in the ontology, possibly identifying new concepts or relations. New instances are added to the existing ontology or used to tune it. This process is repeated until a satisfactory ontology is obtained. The methodology largely automates the ontology construction process and the output is an ontology with an associated trained leaner to be used for further ontology modifications.

Mídia-educação: da criatividade à livre expressão na escola

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Ontologies, taxonomies, thesauri:learning from texts

The use of ontologies as representations of knowledge is widespread but their construction, until recently, has been entirely manual. We argue in this paper for the use of text corpora and automated natural language processing methods for the construction of ontologies. We delineate the challenges and present criteria for the selection of appropriate methods. We distinguish three ma jor steps in ontology building: associating terms, constructing hierarchies and labelling relations. A number of methods are presented for these purposes but we conclude that the issue of data-sparsity still is a ma jor challenge. We argue for the use of resources external tot he domain specific corpus.

Methodologies for the generation of design summer years for building energy simulation using UKCP09 probabilitic climate projections

Design summer years representing near-extreme hot summers have been used in the United Kingdom for the evaluation of thermal comfort and overheating risk. The years have been selected from measured weather data basically representative of an assumed stationary climate. Recent developments have made available ‘morphed’ equivalents of these years by shifting and stretching the measured variables using change factors produced by the UKCIP02 climate projections. The release of the latest, probabilistic, climate projections of UKCP09 together with the availability of a weather generator that can produce plausible daily or hourly sequences of weather variables has opened up the opportunity for generating new design summer years which can be used in risk-based decision-making. There are many possible methods for the production of design summer years from UKCP09 output: in this article, the original concept of the design summer year is largely retained, but a number of alternative methodologies for generating the years are explored. An alternative, more robust measure of warmth (weighted cooling degree hours) is also employed. It is demonstrated that the UKCP09 weather generator is capable of producing years for the baseline period, which are comparable with those in current use. Four methodologies for the generation of future years are described, and their output related to the future (deterministic) years that are currently available. It is concluded that, in general, years produced from the UKCP09 projections are warmer than those generated previously. Practical applications: The methodologies described in this article will facilitate designers who have access to the output of the UKCP09 weather generator (WG) to generate Design Summer Year hourly files tailored to their needs. The files produced will differ according to the methodology selected, in addition to location, emissions scenario and timeslice.

Did you validate your ontology? OOPS!

The application of methodologies for building ontologies can im-prove ontology quality. However, such quality is not guaranteed because of the difficulties involved in ontology modelling. These difficulties are related to the inclusion of anomalies or bad practices within the ontology development. Sev-eral authors have provided lists of typical anomalies detected in ontologies dur-ing the last decade. In this context, our aim in this paper is to describe OOPS! (OntOlogy Pitfall Scanner!), a tool for detecting pitfalls in ontologies.

Did you evaluate your ontology? OOPS! (OntOlogy Pitfall Scanner!)

The application of methodologies for building ontologies can improve ontology quality. However, such quality is not guaranteed because of the difficulties involved in ontology modelling. These difficulties are related to the inclusion of anomalies or bad practices within the ontology development. In this context, our aim is to describe OOPS!(OntOlogy Pitfall Scanner!), a tool for detecting pitfalls in ontologies.

Building an ontology catalogue for smart cities

Apart from providing semantics and reasoning power to data, ontologies enable and facilitate interoperability across heterogeneous systems or environments. A good practice when developing ontologies is to reuse as much knowledge as possible in order to increase interoperability by reducing heterogeneity across models and to reduce development effort. Ontology registries, indexes and catalogues facilitate the task of finding, exploring and reusing ontologies by collecting them from different sources. This paper presents an ontology catalogue for the smart cities and related domains. This catalogue is based on curated metadata and incorporates ontology evaluation features. Such catalogue represents the first approach within this community and it would be highly useful for new ontology developments or for describing and annotating existing ontologies.