BOWL:augmenting the semantic web with beliefs

As the Semantic Web is an open, complex and constantly evolving medium, it is the norm, but not exception that information at different sites is incomplete or inconsistent. This poses challenges for the engineering and development of agent systems on the Semantic Web, since autonomous software agents need to understand, process and aggregate this information. Ontology language OWL provides core language constructs to semantically markup resources on the Semantic Web, on which software agents interact and cooperate to accomplish complex tasks. However, as OWL was designed on top of (a subset of) classic predicate logic, it lacks the ability to reason about inconsistent or incomplete information. Belief-augmented Frames (BAF) is a frame-based logic system that associates with each frame a supporting and a refuting belief value. In this paper, we propose a new ontology language Belief-augmented OWL (BOWL) by integrating OWL DL and BAF to incorporate the notion of confidence. BOWL is paraconsistent, hence it can perform useful reasoning services in the presence of inconsistencies and incompleteness. We define the abstract syntax and semantics of BOWL by extending those of OWL. We have proposed reasoning algorithms for various reasoning tasks in the BOWL framework and we have implemented the algorithms using the constraint logic programming framework. One example in the sensor fusion domain is presented to demonstrate the application of BOWL.

Museum Collections and the Semantic Web

The paper discusses some current trends in the area of development and use of semantic portals for accessing heterogeneous museum collections on the Semantic Web. The presentation is focused on some issues concerning metadata standards for museums, museum collections ontologies and semantic search engines. A number of design considerations and recommendations are formulated.

Reconciling OWL and non-monotonic rules for the semantic web

We propose a description logic extending SROIQ (the description logic underlying OWL 2 DL) and at the same time encompassing some of the most prominent monotonic and nonmonotonic rule languages, in particular Datalog extended with the answer set semantics. Our proposal could be considered a substantial contribution towards fulfilling the quest for a unifying logic for the Semantic Web. As a case in point, two non-monotonic extensions of description logics considered to be of distinct expressiveness until now are covered in our proposal. In contrast to earlier such proposals, our language has the "look and feel" of a description logic and avoids hybrid or first-order syntaxes. © 2012 The Author(s).

GraniteNights - A Multi-Agent Visit Scheduler Utilising Semantic Web Technology

Postprint

An Empirical Investigation of Learning From the Semantic Web

Peer reviewed

Innovazione nel Semantic Web: Evoluzione della base di conoscenza semantica YAGO

La presente ricerca tratta lo studio delle basi di conoscenza, volto a facilitare la raccolta, l'organizzazione e la distribuzione della conoscenza. La scelta dell’oggetto è dovuta all'importanza sempre maggiore acquisita da questo ambito di ricerca e all'innovazione che esso è in grado di apportare nel campo del Web semantico. Viene analizzata la base di conoscenza YAGO: se ne descrivono lo stato dell’arte, le applicazioni e i progetti per sviluppi futuri. Il lavoro è stato condotto esaminando le pubblicazioni relative al tema e rappresenta una risorsa in lingua italiana sull'argomento.

URIs and Intertextuality: Incumbent Philosophical Commitments in the Development of the Semantic Web

Examines two commitments inherent in Resource Description Framework (RDF): intertextuality and rationalism. After introducing how rationalism has been studied in knowledge organization, this paper then introduces the concept of bracketed-rationalism. This paper closes with a discussion of ramifications of intertextuality and bracketed rationalism on evaluation of RDF.

Ontologies and the Semantic Web: Problems and Perspectives for LIS Professionals

Many years have passed since Berners-Lee envi- sioned the Web as it should be (1999), but still many information professionals do not know their precise role in its development, especially con- cerning ontologies –considered one of its main elements. Why? May it still be a lack of under- standing between the different academic commu- nities involved (namely, Computer Science, Lin- guistics and Library and Information Science), as reported by Soergel (1999)? The idea behind the Semantic Web is that of several technologies working together to get optimum information re- trieval performance, which is based on proper resource description in a machine-understandable way, by means of metadata and vocabularies (Greenberg, Sutton and Campbell, 2003). This is obviously something that Library and Information Science professionals can do very well, but, are we doing enough? When computer scientists put on stage the ontology paradigm they were asking for semantically richer vocabularies that could support logical inferences in artificial intelligence as a way to improve information retrieval systems. Which direction should vocabulary development take to contribute better to that common goal? The main objective of this paper is twofold: 1) to identify main trends, issues and problems con- cerning ontology research and 2) to identify pos- sible contributions from the Library and Information Science area to the development of ontologies for the semantic web. To do so, our paper has been structured in the following manner. First, the methodology followed in the paper is reported, which is based on a thorough literature review, where main contributions are analysed. Then, the paper presents a discussion of the main trends, issues and problems concerning ontology re- search identified in the literature review. Recom- mendations of possible contributions from the Library and Information Science area to the devel- opment of ontologies for the semantic web are finally presented.

Scheme Versioning in the Semantic Web

This paper describes a conceptual framework and meth- odology for managing scheme versioning for the Semantic Web. The first part of the paper introduces the concept of vocabulary encoding schemes, distinguished from metadata schemas, and discusses the characteristics of changes in schemes. The paper then presents a proposal to use a value record–similar to a term record in thesaurus management techniques–to manage scheme versioning challenges for the Semantic Web. The con-clusion identifies future research directions.

Augmenting web service discovery by cognitive semantics and abduction

We argue that web service discovery technology should help the user navigate a complex problem space by providing suggestions for services which they may not be able to formulate themselves as (s)he lacks the epistemic resources to do so. Free text documents in service environments provide an untapped source of information for augmenting the epistemic state of the user and hence their ability to search effectively for services. A quantitative approach to semantic knowledge representation is adopted in the form of semantic space models computed from these free text documents. Knowledge of the user’s agenda is promoted by associational inferences computed from the semantic space. The inferences are suggestive and aim to promote human abductive reasoning to guide the user from fuzzy search goals into a better understanding of the problem space surrounding the given agenda. Experimental results are discussed based on a complex and realistic planning activity.

Publishing Engineering Education Resources: A Collaborative Web Project.

Web is a powerful hypermedia-based information retrieval mechanism that provides a user-friendly access across all major computer platforms connected over Internet. This paper demonstrates the application of Web technology when used as an educational delivery tool. It also reports on the development of a prototype electronic publishing project where Web technology was used to deliver power engineering educational resources. The resulting hyperbook will contain diverse teaching resources such as hypermedia-based modular educational units and computer simulation programs that are linked in a meaningful and structured way. The use of Web for disseminating information of this nature has many advantages that cannot possibly be achieved otherwise. PREAMBLE The continual increase of low-cost functionality available in desktop computing has opened up a new possibility in learning within a wider educational framework. This technology also is supported by enhanced features offered by new and ...

A Personalized Ontology Model for Web Information Gathering

As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or a user local information. In this paper, a personalized ontology model is proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering. The results show that this ontology model is successful.

Classifying the user intent of web queries using k-means clustering

Purpose: Web search engines are frequently used by people to locate information on the Internet. However, not all queries have an informational goal. Instead of information, some people may be looking for specific web sites or may wish to conduct transactions with web services. This paper aims to focus on automatically classifying the different user intents behind web queries. Design/methodology/approach: For the research reported in this paper, 130,000 web search engine queries are categorized as informational, navigational, or transactional using a k-means clustering approach based on a variety of query traits. Findings: The research findings show that more than 75 percent of web queries (clustered into eight classifications) are informational in nature, with about 12 percent each for navigational and transactional. Results also show that web queries fall into eight clusters, six primarily informational, and one each of primarily transactional and navigational. Research limitations/implications: This study provides an important contribution to web search literature because it provides information about the goals of searchers and a method for automatically classifying the intents of the user queries. Automatic classification of user intent can lead to improved web search engines by tailoring results to specific user needs. Practical implications: The paper discusses how web search engines can use automatically classified user queries to provide more targeted and relevant results in web searching by implementing a real time classification method as presented in this research. Originality/value: This research investigates a new application of a method for automatically classifying the intent of user queries. There has been limited research to date on automatically classifying the user intent of web queries, even though the pay-off for web search engines can be quite beneficial. © Emerald Group Publishing Limited.

Semantic labelling for document feature patterns using ontological subjects

Finding and labelling semantic features patterns of documents in a large, spatial corpus is a challenging problem. Text documents have characteristics that make semantic labelling difficult; the rapidly increasing volume of online documents makes a bottleneck in finding meaningful textual patterns. Aiming to deal with these issues, we propose an unsupervised documnent labelling approach based on semantic content and feature patterns. A world ontology with extensive topic coverage is exploited to supply controlled, structured subjects for labelling. An algorithm is also introduced to reduce dimensionality based on the study of ontological structure. The proposed approach was promisingly evaluated by compared with typical machine learning methods including SVMs, Rocchio, and kNN.

Towards modelling web search behaviour : integrating users’ cognitive styles

With the rapid growth of information on the Web, the study of information searching has let to an increased interest. Information behaviour (IB) researchers and information systems (IS) developers are continuously exploring user - Web search interactions to understand and to help users to provide assistance with their information searching. In attempting to develop models of IB, several studies have identified various factors that govern user's information searching and information retrieval (IR), such as age, gender, prior knowledge and task complexity. However, how users' contextual factors, such as cognitive styles, affect Web search interactions has not been clearly explained by the current models of Web Searching and IR. This study explores the influence of users' cognitive styles on their Web search behaviour. The main goal of the study is to enhance Web search models with a better understanding of how these cognitive styles affect Web searching. Modelling Web search behaviour with a greater understanding of user's cognitive styles can help information science researchers and IS designers to bridge the semantic gap between the user and the IS. To achieve the aims of the study, a user study with 50 participants was conducted. The study adopted a mixed method approach incorporating several data collection strategies to gather a range of qualitative and quantitative data. The study utilised pre-search and post-search questionnaires to collect the participants' demographic information and their level of satisfaction about the search interactions. Riding's (1991) Cognitive Style Analysis (CSA) test was used to assess the participants' cognitive styles. Participants completed three predesigned search tasks and the whole user - web search interactions, including thinkaloud, were captured using a monitoring program. Data analysis involved several qualitative and quantitative techniques: the quantitative data gave raise to detailed findings about users' Web searching and cognitive styles, the qualitative data enriched the findings with illustrative examples. The study results provide valuable insights into Web searching behaviour among different cognitive style users. The findings of the study extend our understanding of Web search behaviour and how users search information on the Web. Three key study findings emerged: • Users' Web search behaviour was demonstrated through information searching strategies, Web navigation styles, query reformulation behaviour and information processing approaches while performing Web searches. The manner in which these Web search patterns were demonstrated varied among the users with different cognitive style groups. • Users' cognitive styles influenced their information searching strategies, query reformulation behaviour, Web navigational styles and information processing approaches. Users with particular cognitive styles followed certain Web search patterns. • Fundamental relationships were evident between users' cognitive styles and their Web search behaviours; and these relationships can be illustrated through modelling Web search behaviour. Two models that depict the associations between Web search interactions, user characteristics and users' cognitive styles were developed. These models provide a greater understanding of Web search behaviour from the user perspective, particularly how users' cognitive styles influence their Web search behaviour. The significance of this research is twofold: it will provide insights for information science researchers, information system designers, academics, educators, trainers and librarians who want to better understand how users with different cognitive styles perform information searching on the Web; at the same time, it will provide assistance and support to the users. The major outcomes of this study are 1) a comprehensive analysis of how users search the Web; 2) extensive discussion on the implications of the models developed in this study for future work; and 3) a theoretical framework to bridge high-level search models and cognitive models.