Eyal Oren

WSMX - a semantic service-oriented architecture

Web services offer an interoperability model that abstracts from the idiosyncrasies of specific implementations; they were introduced to address the increasing need for seamless interoperability between systems in the business-to-business domain. We analyse the requirements from this domain and show that to fully address interoperability demands we need to make use of semantic descriptions of Web services. We therefore introduce the Web service execution environment (WSMX), at software system that enables the creation and execution of semantic Web services based on the Web service modelling ontology. Providers can use it to register and offer their services and requesters can use it to dynamically discover and invoke relevant services. WSMX allows a requester to discover, mediate and invoke Web services in order to carry out its tasks, based on services available on the Internet.

Scalable Distributed Reasoning Using MapReduce

We address the problem of scalable distributed reasoning, proposing a technique for materialising the closure of an RDF graph based on MapReduce. We have implemented our approach on top of Hadoop and deployed it on a compute cluster of up to 64 commodity machines. We show that a naive implementation on top of MapReduce is straightforward but performs badly and we present several non-trivial optimisations. Our algorithm is scalable and allows us to compute the RDFS closure of 865M triples from the Web (producing 30B triples) in less than two hours, faster than any other published approach.

Marvin: Distributed reasoning over large-scale Semantic Web data

Many Semantic Web problems are difficult to solve through common divide-and-conquer strategies, since they are hard to partition. We present Marvin, a parallel and distributed platform for processing large amounts of RDF data, on a network of loosely coupled peers. We present our divide-conquer-swap strategy and show that this model converges towards completeness. Within this strategy, we address the problem of making distributed reasoning scalable and load-balanced. We present SpeedDate, a routing strategy that combines data clustering with random exchanges. The random exchanges ensure load balancing, while the data clustering attempts to maximise efficiency. SpeedDate is compared against random and deterministic (DHT-like) approaches, on performance and load-balancing. We simulate parameters such as system size, data distribution, churn rate, and network topology. The results indicate that SpeedDate is near-optimally balanced, performs in the same order of magnitude as a DHT-like approach, and has an average throughput per node that scales with i for i items in the system. We evaluate our overall Marvin system for performance, scalability, load balancing and efficiency.

ActiveRDF: object-oriented semantic web programming

Object-oriented programming is the current mainstream programming paradigm but existing RDF APIs are mostly triple-oriented. Traditional techniques for bridging a similar gap between relational databases and object-oriented programs cannot be applied directly given the different nature of Semantic Web data, for example in the semantics of class membership, inheritance relations, and object conformance to schemas. We present ActiveRDF, an object-oriented API for managing RDF data that offers full manipulation and querying of RDF data, does not rely on a schema and fully conforms to RDF(S) semantics. ActiveRDF can be used with different RDF data stores: adapters have been implemented to generic SPARQL endpoints, Sesame, Jena, Redland and YARS and new adapters can be added easily. In addition, integration with the popular Ruby on Rails framework enables rapid development of Semantic Web applications.

Semantic wikis for personal knowledge management

Wikis are becoming popular knowledge management tools. Analysing knowledge management requirements, we observe that wikis do not fully support structured search and knowledge reuse. We show how Semantic wikis address the requirements and present a general architecture. We introduce our SemperWiki prototype which offers advanced information access and knowledge reuse.

ActiveRDF: Embedding Semantic Web data into object-oriented languages

Semantic Web applications share a large portion of development effort with database-driven Web applications. Existing approaches for development of these database-driven applications cannot be directly applied to Semantic Web data due to differences in the underlying data model. We develop a mapping approach that embeds Semantic Web data into object-oriented languages and thereby enables reuse of existing Web application frameworks. We analyse the relation between the Semantic Web and the Web, and survey the typical data access patterns in Semantic Web applications. We discuss the mismatch between object-oriented programming languages and Semantic Web data, for example in the semantics of class membership, inheritance relations, and object conformance to schemas. We present ActiveRDF, an object-oriented API for managing RDF data that offers full manipulation and querying of RDF data, does not rely on a schema and fully conforms to RDF(S) semantics. ActiveRDF can be used with different RDF data stores: adapters have been implemented to generic SPARQL endpoints, Sesame, Jena, Redland and YARS and new adapters can be added easily. We demonstrate the usage of ActiveRDF and its integration with the popular Ruby on Rails framework which enables rapid development of Semantic Web applications.

How semantics make better wikis

Wikis are popular collaborative hypertext authoring environments, but they neither support structured access nor information reuse. Adding semantic annotations helps to address these limitations. We present an architecture for Semantic Wikis and discuss design decisions including structured access, views, and annotation language. We present our prototype SemperWiki that implements this architecture.

A Flexible Integration Framework for Semantic Web 2.0 Applications

The Semantic Web application framework extends Ruby on Rails to enable rapid development of integrated Semantic Web mash-ups. Web applications are mostly database driven. Developers design a database schema and then construct the application logic (which generates Web pages for user interaction) on top of the schema. These applications are centralized and rely on their own relational database, limiting the possibilities for data integration. Mash-ups (often called Web 2.0 applications) are an emerging Web development paradigm that combines functionality from different Web applications.

m3po: An Ontology to Relate Choreographies to Workflow Models

External business processes (choreography models) are currently disconnected from internal processes (workflow models), which leads to synchronisation and verification problems. Connecting these by directly mapping internal to external processes requires a quadratic amount of mappings; an intermediate ontology reduces the amount of necessary mappings but is not trivial to construct due to the variety in workflow meta-models. We introduce our multi meta-model process ontology (m3po) which is based on various existing reference models and languages from the workflow and choreography domain. The m3po ontology relates workflow models to choreography models and allows choreography extraction from internal workflow models. An initial validation is given by translating an IBM Websphere MQ workflow model into the m3po ontology and subsequently extracting an abstract BPEL model from the ontology

Simple Algorithms for Predicate Suggestions Using Similarity and Co-occurrence

When creating Semantic Web data, users have to make a critical choice for a vocabulary: only through shared vocabularies can meaning be established. A centralised policy prevents terminology divergence but would restrict users needlessly. As seen in collaborative tagging environments, suggestion mechanisms help terminology convergence without forcing users. We introduce two domain-independent algorithms for recommending predicates (RDF statements) about resources, based on statistical dataset analysis. The first algorithm is based on similarity between resources, the second one is based on co-occurrence of predicates. Experimental evaluation shows very promising results: a high precision with relatively high recall in linear runtime performance.