Ontology-based Adaptive Reasoning Service for ScienceWeb

2005

The Semantic Web aims to extend the current Web with well-dened semantics, represented in a machine interpretable form, to support better and greater automation of tasks over the Web. In this context, ontologies are regarded as a cornerstone, given that they are intended to be sharable formal conceptualisations of a domain of interest.

ABSTRACT Ontologies, in the OWL language, form the basis of the Semantic Web: they define concepts and relationships, and are thus the fundamental model for encoding information. As the Semantic Web has been more widely adopted, extremely large ontologies have begun to emerge, particularly in the life sciences. Reasoning about such ontologies requires scalability beyond that of current Description Logic-based reasoning tools.

2009

Semantic Web reasoning systems are confronted with the task to process growing amounts of distributed, dynamic resources. This paper presents a novel way of approaching the challenge by RDF graph traversal, exploiting the advantages of swarm intelligence. The natureinspired and index-free methodology is realised by self-organising swarms of autonomous, light-weight entities that traverse RDF graphs by following paths, aiming to instantiate pattern-based inference rules. The method is evaluated on the basis of a series of simulation experiments with regard to desirable properties of Semantic Web reasoning, focussing on anytime behaviour, adaptiveness and scalability.

ACM International Conference Proceeding Series, 2011

Recent advances in the Semantic Web community have yielded a variety of reasoning methods used to process and exploit semantically annotated data. However, most of those methods have only been approved for small, closed, trustworthy, consistent, and static domains. Still, there is a deep mismatch between the requirements for reasoning on a Web scale and the existing efficient reasoning algorithms over restricted subsets. This paper describes the pilot implementation of LarKC-the Large Knowledge Collider, a platform, which focuses on supporting large-scale reasoning over billions of structured data in heterogeneous data sets. The architecture of LarKC allows for an effective combination of techniques coming from different Semantic Web domains by following a serviceoriented approach, supplied by sustainable infrastructure solutions.

2010

As more data is being semantically annotated, it is getting more common that researchers in multiple disciplines rely on semantic repositories that contain large amounts of data in the form of ontologies as a source of information. One of the main issues currently facing these researchers is the lack of easy-to-use interfaces for data retrieval, due to the need to use special query languages or applications. In addition, the knowledge in these repositories might not be comprehensive or up-to-date due to several reasons, such as the discovery of new knowledge in the eld after the repositories were created. In this paper, we present SemanticQA system that allows users to query semantic data repositories using natural language questions. If a user question cannot be answered solely from the populated ontology, SemanticQA detects the failing parts and attempts to answer these parts from web documents and glues the partial answers to reply to the whole questions, which might involve a repetition of the same process if other parts fail.

In this paper we consider knowledge bases that organize information using ontologies. Specifically, we investigate reasoning over a semantic web where the underlying knowledge base covers linked data about science research that are being harvested from the Web and are supplemented and edited by community members. In the semantic web over which we want to reason, frequent changes occur in the underlying knowledge base, and less frequent changes occur in the underlying ontology or the rule set that governs the reasoning. Interposing a backward chaining reasoner between a knowledge base and a query manager yields an architecture that can support reasoning in the face of frequent changes. However, such an interposition of the reasoning introduces uncertainty regarding the size and effort measurements typically exploited during query optimization. We present an algorithm for dynamic query optimization in such an architecture. We also introduce new optimization techniques to the backward-chaining algorithm. We show that these techniques together with the query-optimization reported on earlier, will allow us to actually outperform forward-chaining reasoners in scenarios where the knowledge base is subject to frequent change. Finally, we analyze the impact of these techniques on a large knowledge base that requires external storage.

Proceedings of the AAAI Workshop on Semantic eScience. doi, 2007

We present a set of four web services provided as a result of our work in developing a semantic data framework in the setting of virtual observatories. These web services allow a client service to search for data using three primary selections: choose parameter, choose date-time range and choose instrument, and also to return appropriate service links to the actual data (the fourth service). These services are built using a shared and common understanding of the inputs, outputs and preconditions as defined by a formal ontology, encoded in OWL-DL and running in an internet accessible environment with Web Service Description Language (WSDL) bindings. Upon invocation the service can utilize reasoning services just as a user of the web portal is able to. The service client can optionally utilize the ontology when it consumes the service for additional knowledge or may be used purely syntactically (as most existing web services are now). We present these services, and how they are developed within a specific domain context for the Virtual Solar-Terrestrial Observatory.

Lecture Notes in Computer Science, 2005

Several applications require reasoning over autonomously developed ontologies. Initially conceived to explicit the semantics of a certain domain, these ontologies become a powerful tool for supporting business interactions, once heterogeneities have been solved and inconsistencies eliminated. Unfortunately, a stable coherent logical state is hard to maintain in such an environment, due to normal evolution carried out independently over individual ontologies. As a result, reasoning over autonomously developed ontologies has to face with both heterogeneity and inconsistency, in order to assure correct answering. In this paper we study the problem arising in these settings. We propose an incremental reasoning approach based on a virtual reasoning space that is filled with relevant ontology entities as query answering progress. We show how to identify the set of relevant entities with respect to a user query using a set theory approach and illustrate the solution with a use case exploring the web service discovery scenario.

European Semantic Web Symposium / Conference, 2010

The current state of the art regarding scalable reasoning consists of programs that run on a single machine. When the amount of data is too large, or the logic is too complex, the computational resources of a single machine are not enough. We propose a distributed approach that overcomes these limitations and we sketch a research methodology. A distributed approach is challenging because of the skew in data distribution and the difficulty in partitioning Semantic Web data. We present initial results which are promising and suggest that the approach may be successful.