The use of ontologies for effective knowledge modelling and information retrieval

© 2017 The dramatic increase in the use of knowledge discovery applications requires end users to write complex database search requests to retrieve information. Such users are not only expected to grasp the structural complexity of complex databases but also the semantic relationships between data stored in databases. In order to overcome such difficulties, researchers have been focusing on knowledge representation and interactive query generation through ontologies, with particular emphasis on improving the interface between data and search requests in order to bring the result sets closer to users research requirements. This paper discusses ontology-based information retrieval approaches and techniques by taking into consideration the aspects of ontology modelling, processing and the translation of ontological knowledge into database search requests. It also extensively compares the existing ontology-to-database transformation and mapping approaches in terms of loss of data and semantics, structural mapping and domain knowledge applicability. The research outcomes, recommendations and future challenges presented in this paper can bridge the gap between ontology and relational models to generate precise search requests using ontologies. Moreover, the comparison presented between various ontology-based information retrieval, database-to-ontology transformations and ontology-to-database mappings approaches provides a reference for enhancing the searching capabilities of massively loaded information management systems

Semantic component selection - SemaCS

In component based software development, project success or failure largely depends on correct software component evaluation. All available evaluation methods require time to analyse components. Due to the black box nature of components, preliminary judgments are made based on vendor descriptions. As there is no standard way of describing components, descriptions have to be interpreted using semantics and domain knowledge. This paper presents a semi-automated generic method for component identification and classification based on generic domain taxonomy and user generated semantic input. Every query is semantically tailored to what is being looked for, arriving at better results then it is currently possible using available automated categorisation systems

A Conceptual Representation of Documents and Queries for Information Retrieval Systems by Using Light Ontologies

International audienceThis article presents a vector space model approach to representing documents and queries, based on concepts instead of terms and using WordNet as a light ontology. Such representation reduces information overlap with respect to classic semantic expansion techniques. Experiments carried out on the MuchMore benchmark and on the TREC-7 and TREC-8 Ad-hoc collections demonstrate the effectiveness of the proposed approach

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

Innovative approaches to urban data management using emerging technologies

Many characteristics of Smart cities rely on a sufficient quantity and quality of urban data. Local industry and developers can use this data for application development that improves life of all citizens. Therefore, the handling and usability of this data is a big challenge for smart cities. In this paper we investigate new approaches to urban data management using emerging technologies and give an insight on further research conducted within the EC-funded smarticipate project. Geospatial data cannot be handled well in classical relational database environments. Either they are just put in as binary large objects or have to be broken down into elementary types which can be handled by the database, in many cases resulting in a slow system, since the database technology is not really tuned for delivery on mass data as classical relational databases are optimized for online transaction processing and not analytic processing. Document-based databases provide a better performance, but still struggle with the challenge of large binary objects. Also the heterogeneity of data requires a lot of mapping and data cleansing, in some cases replication can’t be avoided. Another approach is to use Semantic Web technologies to enhance the data and build up relations and connections between entities. However, data formats such as RDF use a different approach and are not suitable for geospatial data leading to a lack on usability. Search engines are a good example of web applications with a high usability. The users must be able to find the right data and get information of related or close matches. This allows information retrieval in an easy to use fashion. The same principles should be applied to geospatial data, which would improve the usability of open data. Combined with data mining and big data technologies those principles would improve the usability of open geospatial data and even lead to new ways to use it. By helping with the interpretation of data in a certain context data is transformed into useful information. In this paper we analyse key features of open geodata portals such as linked data and machine learning in order to show ways of improving the user experience. Based on the Smarticipate projects we show afterwards as open data and geo data online and see the practical application. We also give an outlook on piloting cases where we want to evaluate, how the technologies presented in this paper can be combined to a usefull open data portal. In contrast to the previous EC-funded project urbanapi, where participative processes in smart cities where created with urban data, we go one step further with semantic web and open data. Thereby we achieve a more general approach on open data portals for spatial data and how to improve their usability. The envisioned architecture of the smarticipate project relies on file based storage and a no-copy strategy, which means that data is mostly kept in its original format, a conversion to another format is only done if necessary (e.g. the current format has limitations on domain specific attributes or the user requests a specific format). A strictly functional approach and architecture is envisioned which allows a massively parallel execution and therefore is predestined to be deployed in a cloud environment. The actual search interface uses a domain specific vocabulary which can be customised for special purposes or for users that consider their context and expertise, which should abstract from technology specific peculiarities. Also application programmers will benefit form this architecture as linked data principles will be followed extensively. For example, the JSON and JSON-LD standards will be used, so that web developers can use results of the data store directly without the need for conversion. Also links to further information will be provided within the data, so that a drill down is possible for more details. The remainder of this paper is structured as follows. After the introduction about open data and data in general we look at related work and existing open data portals. This leads to the main chapter about the key technology aspects for an easy-to-use open data portal. This is followed by Chapter five, an introduction of the EC-funded project smarticipate, in which the key technology aspects of chapter four will be included

Data Integration for Open Data on the Web

In this lecture we will discuss and introduce challenges of integrating openly available Web data and how to solve them. Firstly, while we will address this topic from the viewpoint of Semantic Web research, not all data is readily available as RDF or Linked Data, so we will give an introduction to different data formats prevalent on the Web, namely, standard formats for publishing and exchanging tabular, tree-shaped, and graph data. Secondly, not all Open Data is really completely open, so we will discuss and address issues around licences, terms of usage associated with Open Data, as well as documentation of data provenance. Thirdly, we will discuss issues connected with (meta-)data quality issues associated with Open Data on the Web and how Semantic Web techniques and vocabularies can be used to describe and remedy them. Fourth, we will address issues about searchability and integration of Open Data and discuss in how far semantic search can help to overcome these. We close with briefly summarizing further issues not covered explicitly herein, such as multi-linguality, temporal aspects (archiving, evolution, temporal querying), as well as how/whether OWL and RDFS reasoning on top of integrated open data could be help