This paper presents two algorithms based on the horizontal and vertical pattern discovery paradigms that find the connected subgraphs that have a sufficient number of edge-disjoint embeddings in a single large undirected labeled sparse graph. These algorithms use three different methods to determine the number of the edge-disjoint embeddings of a subgraph that are based on approximate and exact maximum independent set computations and use it to prune infrequent subgraphs. Experimental evaluation on real datasets from various domains show that both algorithms achieve good performance, scale well to sparse input graphs with more than 100,000 vertices, and significantly outperform a previously developed algorithm.

This paper presents the notion of Semantic Associations as complex relationships between resource entities. These relationships capture both a connectivity of entities as well as similarity of entities based on a specific notion of similarity called ρ-isomorphism. It formalizes these notions for the RDF data model, by introducing a notion of a Property Sequence as a type. In the context of a graph model such as that for RDF, Semantic Associations amount to specific certain graph signatures. Specifically, they refer to sequences (i.e. directed paths) here called Property Sequences, between entities, networks of Property Sequences (i.e. undirected paths), or subgraphs of ρ-isomorphic Property Sequences. The ability to query about the existence of such relationships is fundamental to tasks in analytical domains such as national security and business intelligence, where tasks often focus on finding complex yet meaningful and obscured relationships between entities. However, support for such queries is lacking in contemporary query systems, including those for RDF. This paper discusses how querying for Semantic Associations might be enabled on the Semantic Web, through the use of an operator ρ. It also discusses two approaches for processing ρqueries on available persistent RDF stores and memory resident RDF data graphs, thereby building on current RDF query languages.

This paper presents a framework for enhancing Web usage records with formal semantics based on an ontology underlying the site. Besides, it elicits automated methods of mapping URLs to application events. Using the ontology's taxonomy, we describe user actions at different levels of abstractions. Using the ontology's concepts and relations, we capture the multitude of user interests expressed by a visit to one page. We employ our ideas in an application of SEAL, a framework for semantic portals that uses Semantic Web technologies to support communities of interest. Different realizations of semantically enriched user tracking are discussed and related to other approaches. We describe first results from a prototypical system, and discuss benefits of Conceptual User Tracking for Web usage mining

Item-based Collaborative Filtering (CF) algorithms have been designed to deal with the scalability problems associated with traditional user-based CF approaches without sacrificing recommendation or prediction accuracy. Item-based algorithms avoid the bottleneck in computing user-user correlations by first considering the relationships among items and performing similarity computations in a reduced space. Because the computation of item similarities is independent of the methods used for generating predictions, multiple knowledge sources, including structured semantic information about items, can be brought to bear in determining similarities among items. The integration of semantic similarities for items with rating- or usage-based similarities allows the system to make inferences based on the underlying reasons for which a user may or may not be interested in a particular item. Furthermore, in cases where little or no rating (or usage) information is available (such as in the case of newly added items, or in very sparse data sets), the system can still use the semantic similarities to provide reasonable recommendations for users. In this paper, we introduce an approach for semantically enhanced collaborative filtering in which structured semantic knowledge about items, extracted automatically from the Web based on domain-specific reference ontologies, is used in conjunction with user-item mappings to create a combined similarity measure and generate predictions. Our experimental results demonstrate that the integrated approach yields significant advantages both in terms of improving accuracy, as well as in dealing with very sparse data sets or new items.

We argue that in a distributed context, such as the Semantic Web, ontology engineers and data creators often cannot control (or even imagine) the possible uses their data or ontologies might have. Therefore ontologies are unlikely to identify every useful or interesting classification possible in a problem domain, for example these might be of a personalised nature and only appropriate for a certain user in a certain context, or they might be of a different granularity than the initial scope of the ontology. We argue that machine learning techniques will be essential within the Semantic Web context to allow these unspecified classifications to be identified. In this paper we explore the application of machine learning methods to FOAF, highlighting the challenges posed by the characteristics of such data. Specifically, we use clustering to identify classes of people and inductive logic programming (ILP) to learn descriptions of these groups. We argue that these descriptions constitute re-usable, first class knowledge that is neither explicitly stated nor deducible from the input data. These new descriptions can be represented as simple OWL class restrictions or more sophisticated descriptions using SWRL. These are then suitable either for incorporation into future versions of ontologies or for on-the-fly use for personalisation tasks.

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of data Web mining operates on, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web resources and navigation behavior are increasingly being used. This fits exactly with the aims of the Semantic Web: the Semantic Web enriches the WWW by machineprocessable information which supports the user in his tasks. In this paper, we discuss the interplay of the Semantic Web with Web Mining, with a specific focus on usage mining.

Personalization based on Web usage mining can enhance the effectiveness and scalability of collaborative filtering. However, without semantic knowledge about the underlying domain, such systems cannot recommend different types of complex objects based in their underlying properties and attributes. This paper provides an overview of approaches for incorporating semantic knowledge into Web usage mining and personalization processes. We present two general approaches to integrate semantic knowledge extracted from the content features of pages into the usage-based personalization process. Next, we present a general framework of integrating domain ontologies with Web Usage Mining and Personalization. In each case, we discuss how semantic knowledge is leveraged and represented in the preprocessing and pattern discovery phases, as well as how it is used to enhance usage-based personalization.

We present a case study about the application of the inductive database approach to the analysis of Web logs. We consider rich XML Web logs – called conceptual logs – that are generated by Web applications designed with the WebML conceptual model and developed with the WebRatio CASE tool. Conceptual logs integrate the usual information about user requests with meta-data concerning the structure of the content and the hypertext of a Web application. We apply a data mining language (MINE RULE) to conceptual logs in order to identify different types of patterns, such as: recurrent navigation paths, most frequently visited page contents, and anomalies (e.g., intrusion attempts or harmful usages of resources). We show that the exploitation of the nuggets of information embedded in the logs and of the specialized mining constructs provided by the query languages enables the rapid customization of the mining procedures following to the Web developers ’ need. Given our on-field experience, we also suggest that the use of queries in advanced languages, as opposed to ad-hoc heuristics, eases the specification and the discovery of large spectrum of patterns.

With more than two billion pages created by millions of Web page authors and organizations, the World Wide Web is a tremendously rich

A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, beside consistency checking, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as (labeled, directed) graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures in general currently receive high attention in the Semantic Web community, there are only very few SNA applications up to now, and virtually none for analyzing the structure of ontologies. We illustrate in this paper the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality based on Hermitian matrices, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.