Module extraction via query inseparability in OWL 2 QL

We show that deciding conjunctive query inseparability for OWL 2 QL ontologies is PSpace-hard and in ExpTime. We give polynomial-time (incomplete) algorithms and demonstrate by experiments that they can be used for practical module extraction

Extracting ontologies from software documentation: a semi-automatic method and its evaluation

Rich and generic ontologies about web service functionalities are a prerequisite for performing complex reasoning tasks with web service descriptions. However, their acquisition is timeconsuming and conditioned by the small number of web services available in certain domains. As a solution, we describe a semiautomatic method to extract such ontologies from software documentation, motivated by the observation that web services reflect the functionality of their underlying implementation. Further, we report on fine-tuning the extraction process by using a multi-stage evaluation method

Modularization for the Cell Ontology

One of the premises of the OBO Foundry is that development of an orthogonal set of ontologies will increase domain expert contributions and logical interoperability, and decrease maintenance workload. For these reasons, the Cell Ontology (CL) is being re-engineered. This process requires the extraction of sub-modules from existing OBO ontologies, which presents a number of practical engineering challenges. These extracted modules may be intended to cover a narrow or a broad set of species. In addition, applications and resources that make use of the Cell Ontology have particular modularization requirements, such as the ability to extract custom subsets or unions of the Cell Ontology with other OBO ontologies. These extracted modules may be intended to cover a narrow or a broad set of species, which presents unique complications.

We discuss some of these requirements, and present our progress towards a customizable simple-to-use modularization tool that leverages existing OWL-based tools and opens up their use for the CL and other ontologies

Syntactic vs. Semantic Locality: How Good Is a Cheap Approximation?

Extracting a subset of a given OWL ontology that captures all the ontology's knowledge about a specified set of terms is a well-understood task. This task can be based, for instance, on locality-based modules (LBMs). These come in two flavours, syntactic and semantic, and a syntactic LBM is known to contain the corresponding semantic LBM. For syntactic LBMs, polynomial extraction algorithms are known, implemented in the OWL API, and being used. In contrast, extracting semantic LBMs involves reasoning, which is intractable for OWL 2 DL, and these algorithms had not been implemented yet for expressive ontology languages. We present the first implementation of semantic LBMs and report on experiments that compare them with syntactic LBMs extracted from real-life ontologies. Our study reveals whether semantic LBMs are worth the additional extraction effort, compared with syntactic LBMs

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201