Reasoning Web 2008 Papers

Rules and ontologies play a key role in the layered architecture of the Semantic Web, as they are used to ascribe meaning to, and to reason about, data on the Web. While the Ontology Layer of the Semantic Web is quite developed, and the Web Ontology Language (OWL) is a W3C recommendation since a cou- ple of years already, the rules layer is far less developed and an active area of research; a number of initiatives and proposals have been made so far, but no standard as been released yet. Many implementations of rule engines are around which deal with Semantic Web data in one or another way. This article gives a comprehensive, although not exhaustive, overview of such systems, describes their supported languages, and sets them in relation with theoretical approaches for combining rules and ontologies as foreseen in the Semantic Web architec- ture. In the course of this, we identify desired properties and common features of rule languages and evaluate existing systems against their support. Furthermore, we review technical problems underlying the integration of rules and ontologies, and classify representative proposals for theoretical integration approaches into different categories.

Managing uncertainty and/or vagueness is starting to play an impor- tant role in Semantic Web representation languages. Our aim is to overview basic concepts on representing uncertain and vague knowledge in current Semantic Web ontology and rule languages (and their combination).

Attempto Controlled English (ACE) is a controlled natural language, i.e. a precisely defined subset of English that can automatically and unambiguously be translated into first-order logic. ACE may seem to be completely natural, but is actually a formal language, concretely it is a first-order logic language with an English syntax. Thus ACE is human and machine understandable. ACE was originally intended to specify software, but has since been used as a general knowledge repre- sentation language in several application domains, most recently for the semantic web. ACE is supported by a number of tools, predominantly by the Attempto Parsing Engine (APE) that translates ACE texts into Discourse Representation Structures (DRS), a variant of first-order logic. Other tools include the Attempto Reasoner RACE, the AceRules system, the ACE View plug-in for the Prot ́eg ́e ontology editor, AceWiki, and the OWL verbaliser.

Multimedia constitutes an interesting field of application for Semantic Web and Semantic Web reasoning, as the access and man- agement of multimedia content and context depends strongly on the semantic descriptions of both. At the same time, multimedia resources constitute complex objects, the descriptions of which are involved and require the foundation on sound modeling practice in order to represent findings of low- and high level multimedia analysis and to make them accessible via Semantic Web querying of resources. This tutorial aims to provide a red thread through these different issues and to give an outline of where Semantic Web modeling and reasoning needs to further contribute to the area of semantic multimedia for the fruitful interaction between these two fields of computer science.

One of the most visible trends on the Web is the emergence of “Social Web” sites which facilitate the creation and gathering of knowledge through the simplification of user contributions via blogs, tagging and folksonomies, wikis, podcasts, and the deployment of online social networks. The Social Web has enabled community-based knowledge acquisition with efforts like the Wikipedia demonstrating the “wisdom of the crowds” in creating the world’s largest online encyclopaedia. Although it is difficult to define the exact boundaries of what structures or abstractions belong to the Social Web, a common property of such sites is that they facilitate collaboration and sharing between users with low tech- nical barriers, although usually on single sites. As more social websites form around the connections between people and their objects of interest, and as these “object-centred networks” grow bigger and more diverse, more intuitive methods are needed for representing and navigating the content items in these sites: both within and across social websites. Also, to better enable user access to multiple sites, interoperability among social websites is required in terms of both the con- tent objects and the person-to-person networks expressed on each site. This re- quires representation mechanisms to interconnect people and objects on the Social Web in an interoperable and extensible way. The Semantic Web provides such representation mechanisms: it can be used to link people and objects by represent- ing the heterogeneous ties that bind us all to each other (either directly or indirectly). In this paper, we will describe methods that build on agreed-upon Semantic Web formats to describe people, content objects, and the connections that bind them together explicitly or implicitly, enabling social websites to interoperate by appealing to some common semantics. We will also focus on how developers can use the Semantic Web to augment the ways in which they cre- ate,reuse, and link content on social networking sites and social websites.

Semantic Web technologies are appealing for biomedical researchers since they promise to solve many of the daily problems they face while access- ing and integrating biological information that is distributed over the Internet and managed by using tools which are extremely heterogeneous and largely not compatible. On the other hand, the complexity of biomedical information and its heterogeneity, together with the need of keeping current production services steadily up and running, make the transition from current semantic-less to fu- ture semantic-aware services a huge problem.

In this paper, authors present the characteristics of biomedical information that make adoption of semantic web technologies both desirable and complex at the same time. They then present the tools and the applications that have been developed so far, including biomedical ontologies, RDF/OWL data stores, query systems and semantic-aware tools and browsers. Finally, they present community efforts and the perspectives that can be sought for short- and mid- term developments in the field.

To make semantic Web services accessible to users, providers use registries to publish them. Unfortunately, the current registries use discovery mechanisms which are inefficient, as they do not support discovery based on the semantics of the services and thus lead to a considerable number of irrele- vant matches. Semantic discovery and matching of services is a promising ap- proach to address this challenge. This paper presents an algorithm to match a semantic Web service request described with SAWSDL against semantic Web service advertisements. The algorithm is novel in three fundamental aspects. First, the similarity among semantic Web service properties, such as inputs and outputs, is evaluated using Tversky’s model which is based on concepts (classes), their semantic relationships, and their common and distinguishing fea- tures (properties). Second, the algorithm, not only takes into account services’ inputs and outputs, but it also considers the functionality of services. Finally, the algorithm is able to match a semantic Web service request against adver- tisements that are annotated with concepts that are with or without a common ontological commitment. In other words, it can evaluate the similarity of con- cepts defined in the context of different ontologies.

The next article, Reasoning Web 2009, will focus on the use of semantic technologies to enhance data access on the Web.