Evaluation of information retrieval systems using structural equation modeling

The interpretation of the experimental data collected by testing systems across input datasets and model parameters is of strategic importance for system design and implementation. In particular, finding relationships between variables and detecting the latent variables affecting retrieval performance can provide designers, engineers and experimenters with useful if not necessary information about how a system is performing. This paper discusses the use of Structural Equation Modeling (SEM) in providing an in-depth explanation of evaluation results and an explanation of failures and successes of a system; in particular, we focus on the case of evaluation of Information Retrieval systems

Business Process Retrieval Based on Behavioral Semantics

This paper develops a framework for retrieving business processes considering search requirements based on behavioral semantics properties; it presents a framework called "BeMantics" for retrieving business processes based on structural, linguistics, and behavioral semantics properties. The relevance of the framework is evaluated retrieving business processes from a repository, and collecting a set of relevant business processes manually issued by human judges. The "BeMantics" framework scored high precision values (0.717) but low recall values (0.558), which implies that even when the framework avoided false negatives, it prone to false positives. The highest pre- cision value was scored in the linguistic criterion showing that using semantic inference in the tasks comparison allowed to reduce around 23.6 % the number of false positives. Using semantic inference to compare tasks of business processes can improve the precision; but if the ontologies are from narrow and specific domains, they limit the semantic expressiveness obtained with ontologies from more general domains. Regarding the perform- ance, it can be improved by using a filter phase which indexes business processes taking into account behavioral semantics propertie

Exploiting Query Structure and Document Structure to Improve Document Retrieval Effectiveness

In this paper we present a systematic analysis of document retrieval using unstructured and structured queries within the score region algebra (SRA) structured retrieval framework. The behavior of di®erent retrieval models, namely Boolean, tf.idf, GPX, language models, and Okapi, is tested using the transparent SRA framework in our three-level structured retrieval system called TIJAH. The retrieval models are implemented along four elementary retrieval aspects: element and term selection, element score computation, score combination, and score propagation. The analysis is performed on a numerous experiments evaluated on TREC and CLEF collections, using manually generated unstructured and structured queries. Unstructured queries range from the short title queries to long title + description + narrative queries. For generating structured queries we exploit the knowledge of the document structure and the content used to semantically describe or classify documents. We show that such structured information can be utilized in retrieval engines to give more precise answers to user queries then when using unstructured queries

An information retrieval approach to ontology mapping

In this paper, we present a heuristic mapping method and a prototype mapping system that support the process of semi-automatic ontology mapping for the purpose of improving semantic interoperability in heterogeneous systems. The approach is based on the idea of semantic enrichment, i.e., using instance information of the ontology to enrich the original ontology and calculate similarities between concepts in two ontologies. The functional settings for the mapping system are discussed and the evaluation of the prototype implementation of the approach is reported. \ud \u