Abstract. Semantic Web languages are being used to represent, encode and exchange semantic data in many contexts beyond the Web – in databases, multiagent systems, mobile computing, and ad hoc networking environments. The core paradigm, however, remains what we call the Web aspect of the Semantic Web – its use by independent and distributed agents who publish and consume data on the World Wide Web. To better understand this central use case, we have harvested and analyzed a collection of Semantic Web documents from an estimated ten million available on the Web. Using a corpus of more than 1.7 million documents comprising over 300 million RDF triples, we describe a number of global metrics, properties and usage patterns. Most of the metrics, such as the size of Semantic Web documents and the use frequency of Semantic Web terms, were found to follow a power law distribution. 1

High throughput glycoproteomics, similar to genomics and proteomics, involves extremely large volumes of distributed, heterogeneous data as a basis for identification and quantification of a structurally diverse collection of biomolecules. The ability to share, compare, query for and most critically correlate datasets using the native biological relationships are some of the challenges being faced by glycobiology researchers. As a solution for these challenges, we are building a semantic structure, using a suite of ontologies, which supports management of data and information at each step of the experimental lifecycle. This framework will enable researchers to leverage the large scale of glycoproteomics data to their benefit. In this paper, we focus on the design of these biological ontology schemas with an emphasis on relationships between biological concepts, on the use of novel approaches to populate these complex ontologies including integrating extremely large datasets (~500MB) as part of the instance base and on the evaluation of ontologies using OntoQA [38] metrics. The application of these ontologies in providing informatics solutions, for high throughput glycoproteomics experimental domain, is also discussed. We present our experience as a use case of developing two ontologies in one domain, to be part of a set of use cases, which are used in the development of an emergent framework for building and deploying biological ontologies.

Ontology evaluation is a maturing discipline with methodologies and measures being developed and proposed. However, evaluation methods that have been proposed have not been applied to specific examples. In this paper, we present the state-of-the-art in ontology evaluation- current methodologies, criteria and measures, analyse appropriate evaluations that are important to our application- browsing in Wikipedia, and apply these evaluations in the context of ontologies with varied properties. Specifically, we seek to evaluate ontologies based on categories found in Wikipedia. Categories and Subject Descriptors

The Semantic Web was invented by Tim Berners-Lee in 1998 as a web of data for machine consumption. Its applicability in supporting real world applications on the World Wide Web, however, remains unclear to this day because most existing works treat the Semantic Web as one universal RDF graph and ignore the Web aspect. In fact, the Semantic Web is distributed on the Web as a web of belief: each piece of Semantic Web data is independently published on the Web as a certain agent’s belief instead of the universal truth. Therefore, we enhance the current conceptual model of the Semantic Web to characterize both the content and the context of Semantic Web data. A significant sample dataset is harvested to demonstrate the non-trivial presence and the global properties of the Semantic Web on the Web. Based on the enhanced conceptual model, we introduce a novel search and navigation model for the unique behaviors in Web-scale Semantic Web data access, and develop an enabling tool – the Swoogle Semantic Web search engine. To evaluate the data quality of Semantic Web data, we also (i) develop an explainable ranking schema that orders the popularity of Semantic Web documents and terms, and (ii) introduce a new level of granularity of Semantic Web data – RDF molecule that supports lossless RDF graph decomposition and effective provenance tracking. This dissertation systematically investigates the Web aspect of the Semantic Web. Its primary contribu-tions are the enhanced conceptual model of the Semantic Web, the novel Semantic Web search and navigation model, and the Swoogle Semantic Web search engine.

In today’s web search technologies, the link structure of the web plays a critical role. In this work, the goal is to use semantic relationships for ranking documents without relying on the existence of any specific structure in a document or links between documents. Instead, named/real-world entities are identified and the relevance of documents is determined using relationships that are known to exist between the entities in a populated ontology, that is, by “connecting-the-dots. ” We introduce a measure of relevance that is based on traversal and the semantics of relationships that link entities in an ontology. The implementation of the methods described here builds upon an existing architecture for processing unstructured information that solves some of the scalability aspects for text processing, indexing and basic keyword/entity document retrieval. The contributions of this thesis are in demonstrating the role and benefits of using relationships for ranking documents when a user types a traditional keyword query. The research components that make this possible are as follows. First, a flexible semantic discovery and ranking component takes user-defined criteria for identification of the most interesting semantic associations between entities in an ontology. Second, semantic analytics techniques

The art and science of building ontologies have been developed to the point where it is not sufficient anymore to design and implement a new ontology. Rather, one needs to follow the process of building an ontology by evaluating its quality in absolute numeric terms. If another ontology in the same domain exists, then the two ontologies should be compared in a quantitative manner to determine which one of them is better. Furthermore, the quality scoring mechanism should provide clues concerning the sections of the ontology (one or both) that need improvement. Ontologies are complex structures which exist in many different variations. Even after imposing a basic structural framework and choosing a domain, two ontologies may be evaluated with respect to a number of different features. In this paper we will concentrate on one single ontology feature and assume that all other features are fixed. We have developed a mechanism to measure the quality of this ontology feature, preferred term(s) based on the concept of naturalness, and show that it agrees very well with human judgments. Thus we provide an approach towards the principled selection of the preferred terms in an ontology.

Abstract. Domain ontology as an instrument for knowledge representation, sharing, reuse and interoperability takes an increasingly important role in the approaches for personalised intelligent e-learning architectures and systems. However, wider practical acceptance of domain ontology as an engineering product and as a part of web-based learning systems is still needed. We believe that one of the barriers for wider spreading of domain ontologies in different fields, including e-learning, is the problem of the design and maintenance of high quality ontologies. As the importance of the quality of learning resources is obvious, the quality of domain ontology for e-learning is even more important, because ontology is intended to be re-used in design and implementation of various learning resources. In this paper, we analyse the quality-related characteristics of domain ontology. We propose a framework for evaluation of the quality of domain ontology for web-based learning. Further, we propose a model for ontology evaluation, based on its technical and complexity-related characteristics. We identify main conceptual (semantic) quality characteristics, and analyse the relationship between both types of ontology quality characteristics. Also we present an application of proposed framework to the evaluation of ontologies for web based learning.

In this deliverable we present results obtained from the work described in the first version of this deliverable (D1.2.10) about ontology repositories. In particular, we provide an overview of the sustainability of the Ontology Metadata Vocabulary (OMV), the proposal of a hierarchy for the classification of ontology domains and evaluation results for the distributed ontology metadata repository (Oyster). Furthermore, we discuss the flow of knowledge between Oyster and the ontology metadata portal Onthology that rely on the content evaluation metrics described at the end of the deliverable. Therefore, the second part of the deliverable discusses how to properly define metrics for ontologies, and introduce the notions of ontology normalization for measuring, and of stable metrics. This will help to properly define better ontology metrics in subsequent research in this area. The normalization has been implemented in a prototype as part of the KAON2 OWL Tools (source code available for download at OntoWare.org). Parts of this work have been already accepted for publication at the European Semantic Web Conference (ESWC2007).

Abstract. As the sheer volume of new knowledge increases, there is a need to find effective ways to convey and correlate emerging knowledge in machine-readable form. The success of the Semantic Web hinges on the ability to formalize distributed knowledge in terms of a varied set of ontologies. We present Pan-Onto-Eval, a comprehensive approach to evaluating an ontology by considering its structure, semantics, and domain. We provide formal definitions of the individual metrics that constitute Pan-Onto-Eval, and synthesize them into an integrated metric. We illustrate its effectiveness by presenting an example based on multiple ontologies for a University.

Abstract — In this paper, we measure and analyze the graph features of Semantic Web (SW) schemas with focus on powerlaw degree distributions. Our main finding is that the majority of SW schemas with a significant number of properties (resp. classes) approximate a power-law for total-degree (resp. number of subsumed classes) distribution. Moreover, our analysis revealed some emerging conceptual modeling practices of SW schema developers, namely: a) each schema has a few focal classes that have been analyzed in detail (i.e., they have numerous properties and subclasses) which are further connected with focal classes defined in other schemas, b) class subsumption hierarchies are mostly unbalanced (i.e., some branches are deep and heavy, while others are shallow and light), c) most properties have as domain/range classes that are located high at the class subsumption hierarchies and d) the number of recursive/multiple properties is significant. The knowledge of these features is essential for guiding synthetic SW schema generation, which is an important step towards benchmarking SW repositories and query languages implementations. Index Terms — Semantic Web, power-laws, conceptual schemas morphology