An Idea Ontology for Innovation Management

Exchanging and analyzing ideas across different software tools and repositories is needed to implement the concepts of open innovation and holistic innovation management. However, a precise and formal definition for the concept of an idea is hard to obtain. In this paper, the authors introduce an ontology to represent ideas. This ontology provides a common language to foster interoperability between tools and to support the idea life cycle. Through the use of an ontology, additional benefits like semantic reasoning and automatic analysis become available. Our proposed ontology captures both a core idea concept that covers the ‘heart of the idea’ and further concepts to support collaborative idea development, including rating, discussing, tagging, and grouping ideas. This modular approach allows the idea ontology to be complemented by additional concepts like customized evaluation methods. The authors present a case study that demonstrates how the ontology can be used to achieve interoperability between innovation tools and to answer questions relevant for innovation managers that demonstrate the advantages of semantic reasoning.

Searching Linked Objects with Falcons

The rapid development of the data Web is accompanied by increasing information needs from ordinary Web users for searching objects and their relations. To meet the challenge, this chapter presents Falcons Object Search, a keyword-based search engine for linked objects. To support various user needs expressed via keyword queries, for each object an extensive virtual document is indexed, which consists of not only associated literals but also the textual descriptions of associated links and linked objects. The resulting objects are ranked according to a combination of their relevance to the query and their popularity. For each resulting object, a query-relevant structured snippet is provided to show the associated literals and linked objects matched with the query for reflecting query relevance and even directly answering the question behind the query. To exploit ontological semantics for more precise search results, the type information of objects is leveraged to support class-based query refinement, and Web-scale class-inclusion reasoning is performed to discover implicit type information. Further, a subclass recommendation technique is proposed to allow users navigate class hierarchies for incremental results filtering. A task-based experiment demonstrates the promising features of the system.

Solving Semantic Interoperability Conflicts in Cross-Border E-Government Services

Interoperability is one of the most challenging problems in modern cross-organizational information systems, which rely on heterogeneous information and process models. Interoperability becomes very important for e-Government information systems that support cross-organizational communication especially in a cross-border setting. The main goal in this context is to seamlessly provide integrated services to the user (citizen). In this paper we focus on Pan European e-Services and issues related with their integration. Our analysis uses basic concepts of the generic public service model of the Governance Enterprise Architecture (GEA) and of the Web Service Modeling Ontology (WSMO), to express the semantic description of the e-services. Based on the above, we present a mediation infrastructure capable of resolving semantic interoperability conflicts at a pan-European level. We provide several examples to illustrate both the need to solve such semantic conflicts and the actual solutions we propose.

Enabling Scalable Semantic Reasoning for Mobile Services

With the emergence of high-end smart phones/PDAs there is a growing opportunity to enrich mobile/pervasive services with semantic reasoning. This paper presents novel strategies for optimising semantic reasoning for realising semantic applications and services on mobile devices. We have developed the mTableaux algorithm which optimises the reasoning process to facilitate service selection. We present comparative experimental results which show that mTableaux improves the performance and scalability of semantic reasoning for mobile devices.

Inductive Classification of Semantically Annotated Resources through Reduced Coulomb Energy Networks

The tasks of resource classification and retrieval from knowledge bases in the Semantic Web are the basis for a lot of important applications. In order to overcome the limitations of purely deductive approaches to deal with these tasks, inductive (instance-based) methods have been introduced as efficient and noise-tolerant alternatives. In this paper we propose an original method based on a non-parametric learning scheme: the Reduced Coulomb Energy (RCE) Network. The method requires a limited training effort but it turns out to be very effective during the classification phase. Casting retrieval as the problem of assessing the class-membership of individuals w.r.t. the query concepts, we propose an extension of a classification algorithm using RCE networks based on an entropic similarity measure for OWL. Experimentally we show that the performance of the resulting inductive classifier is comparable with the one of a standard reasoner and often more efficient than with other inductive approaches. Moreover, we show that new knowledge (not logically derivable) is induced and the likelihood of the answers may be provided.

A URI is Worth a Thousand Tags

Although tagging is a widely accepted practice on the Social Web, it raises various issues like tags ambiguity and heterogeneity, as well as the lack of organization between tags. We believe that Semantic Web technologies can help solve many of these issues, especially considering the use of formal resources from the Web of Data in support of existing tagging systems and practices. In this article, we present the MOAT—Meaning Of A Tag—ontology and framework, which aims to achieve this goal. We will detail some motivations and benefits of the approach, both in an Enterprise 2.0 ecosystem and on the Web. As we will detail, our proposal is twofold: It helps solve the problems mentioned previously, and weaves user-generated content into the Web of Data, making it more efficiently interoperable and retrievable.

Community-driven Consolidated Linked Data

User-generated content can help the growth of linked data. However, there are a lack of interfaces enabling ordinary people to author linked data. Secondly, people have multiple perspectives on the same concept and different contexts. Thirdly, there are not enough ontologies to model various data. Therefore, the authors of this chapter propose an approach to enable people to share various data through an easy-to-use social platform. Users define their own concepts and multiple conceptualizations are allowed. These are consolidated using semi-automatic schema alignment techniques supported by the community. Further, concepts are grouped semi-automatically by similarity. As a result of consolidation and grouping, informal lightweight ontologies emerge gradually. The authors have implemented a social software system, called StYLiD, to realize the approach. It can serve as a platform motivating people to bookmark and share different things. It may also drive vertical portals for specific communities with integrated data from multiple sources. Some experimental observations support the validity of the approach.

A New Similarity Measure for Automatic Construction of the Unknown Word Lexical Dictionary

This paper deals with research that automatically constructs a lexical dictionary of unknown words as an automatic lexical dictionary expansion. The lexical dictionary has been usefully applied to various fields for semantic information processing. It has limitations in which it only processes terms defined in the dictionary. Under this circumstance, the concept of “Unknown Word (UW)” is defined. UW is considered a word, not defined in WordNet, that is an existing representative lexical dictionary. Here is where a new method to construct UW lexical dictionary through inputting various document collections that are scattered on the WebWeb is proposed. The authors grasp related terms of UW and measure semantic relatedness (similarity) between an UW and a related term(s). The relatedness is obtained by calculating both probabilistic relationship and semantic relationship. This research can extend UW lexical dictionary with an abundant number of UW. It is also possible to prepare a foundation for semantic retrieval by simultaneously using the UW lexical dictionary and WordNet.

Linked Data

The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward.

Learning of OWL Class Expressions on Very Large Knowledge Bases and its Applications

The vision of the Semantic Web aims to make use of semantic representations on the largest possible scale - the Web. Large knowledge bases such as DBpedia, OpenCyc, and GovTrack are emerging and freely available as Linked Data and SPARQL endpoints. Exploring and analysing such knowledge bases is a significant hurdle for Semantic Web research and practice. As one possible direction for tackling this problem, the authors present an approach for obtaining complex class expressions from objects in knowledge bases by using Machine Learning techniques. The chapter describes in detail how to leverage existing techniques to achieve scalability on large knowledge bases available as SPARQL endpoints or Linked Data. The algorithms are made available in the open source DL-Learner project and this chapter presents several real-life scenarios in which they can be used by Semantic Web applications.