Using TREC for cross-comparison between classic IR and ontology-based search models at a Web scale

The construction of standard datasets and benchmarks to evaluate ontology-based search approaches and to compare then against baseline IR models is a major open problem in the semantic technologies community. In this paper we propose a novel evaluation benchmark for ontology-based IR models based on an adaptation of the well-known Cranfield paradigm (Cleverdon, 1967) traditionally used by the IR community. The proposed benchmark comprises: 1) a text document collection, 2) a set of queries and their corresponding document relevance judgments and 3) a set of ontologies and Knowledge Bases covering the query topics. The document collection and the set of queries and judgments are taken from one of the most widely used datasets in the IR community, the TREC Web track. As a use case example we apply the proposed benchmark to compare a real ontology-based search model (Fernandez, et al., 2008) against the best IR systems of TREC 9 and TREC 2001 competitions. A deep analysis of the strengths and weaknesses of this benchmark and a discussion of how it can be used to evaluate other ontology-based search systems is also included at the end of the paper

OUC's Participation in the 2011 INEX Book Track

In this article we describe the Oslo University College’s participation in the INEX 2011 Book track. In 2010, the OUC submitted retrieval results for the “Prove It” task with traditional relevance detection combined with some rudimental detection of confirmation. In line with our belief that proving or refuting facts are different semantic aware actions of speech, we have this year attempted to incorporate some rudimentary semantic support based on the WordNet database

Modelling text-fact-integration in digital libraries

Digital Libraries currently face the challenge of integrating many different types of research information (e.g. publications, primary data, expert‘s profiles, institutional profiles, project information etc.) according to their scientific users‘ needs. To date no general, integrated model for knowledge organization and retrieval in Digital Libraries exists. This causes the problem of structural and semantic heterogeneity due to the wide range of metadata standards, indexing vocabularies and indexing approaches used for different types of information. The research presented in this paper focuses on areas in which activities are being undertaken in the field of Digital Libraries in order to treat semantic interoperability problems. We present a model for the integrated retrieval of factual and textual data which combines multiple approaches to semantic interoperability und sets them into context. Embedded in the research cycle, traditional content indexing methods for publications meet the newer, but rarely used ontology-based approaches which seem to be better suited for representing complex information like the one contained in survey data. The benefits of our model are (1) easy re-use of available knowledge organisation systems and (2) reduced efforts for domain modelling with ontologies. (author's abstract

Deductive and Inductive Stream Reasoning for Semantic Social Media Analytics

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A Linked-Data Model for Semantic Sensor Streams

This paper describes a semantic modelling scheme, a naming convention and a data distribution mechanism for sensor streams. The proposed solutions address important challenges to deal with large-scale sensor data emerging from the Internet of Things resources. While there are significant numbers of recent work on semantic sensor networks, semantic annotation and representation frameworks, there has been less focus on creating efficient and flexible schemes to describe the sensor streams and the observation and measurement data provided via these streams and to name and resolve the requests to these data. We present our semantic model to describe the sensor streams, demonstrate an annotation and data distribution framework and evaluate our solutions with a set of sample datasets. The results show that our proposed solutions can scale for large number of sensor streams with different types of data and various attributes