A structured model metametadata technique to enhance semantic searching in metadata repository

This paper discusses on a novel technique for semantic searching and retrieval of information about learning materials. A novel structured metametadata model has been created to provide the foundation for a semantic search engine to extract, match and map queries to retrieve relevant results. Metametadata encapsulate metadata instances by using the properties and attributes provided by ontologies rather than describing learning objects. The use of ontological views assists the pedagogical content of metadata extracted from learning objects by using the control vocabularies as identified from the metametadata taxonomy. The use of metametadata (based on the metametadata taxonomy) supported by the ontologies have contributed towards a novel semantic searching mechanism. This research has presented a metametadata model for identifying semantics and describing learning objects in finer-grain detail that allows for intelligent and smart retrieval by automated search and retrieval software

Using multiple related ontologies in a fuzzy information retrieval model.

With the Semantic Web progress many independently developed distinct domain ontologies have to be shared and reused by a variety of applications. The use of ontologies in information retrieval applications allows the retrieval of semantically related documents to an initial users´ query. This work presents a fuzzy information retrieval model for improving the document retrieval process considering a knowledge base composed of multiple domain ontologies that are fuzzy related. Each ontology can be represented independently as well as their relationships. This knowledge organization is used in a novel method to expand the user initial query and to index the documents in the collection. Experimental results show that the proposed model presents better overall performance when compared with another fuzzy-based approach for information retrieval.SBIA 2008

An experiment with ontology mapping using concept similarity

This paper describes a system for automatically mapping between concepts in different ontologies. The motivation for the research stems from the Diogene project, in which the project's own ontology covering the ICT domain is mapped to external ontologies, in order that their associated content can automatically be included in the Diogene system. An approach involving measuring the similarity of concepts is introduced, in which standard Information Retrieval indexing techniques are applied to concept descriptions. A matrix representing the similarity of concepts in two ontologies is generated, and a mapping is performed based on two parameters: the domain coverage of the ontologies, and their levels of granularity. Finally, some initial experimentation is presented which suggests that our approach meets the project's unique set of requirements

How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval

The knowledge representation community has built general-purpose ontologies which contain large amounts of commonsense knowledge over relevant aspects of the world, including useful visual information, e.g.: "a ball is used by a football player", "a tennis player is located at a tennis court". Current state-of-the-art approaches for visual recognition do not exploit these rule-based knowledge sources. Instead, they learn recognition models directly from training examples. In this paper, we study how general-purpose ontologies---specifically, MIT's ConceptNet ontology---can improve the performance of state-of-the-art vision systems. As a testbed, we tackle the problem of sentence-based image retrieval. Our retrieval approach incorporates knowledge from ConceptNet on top of a large pool of object detectors derived from a deep learning technique. In our experiments, we show that ConceptNet can improve performance on a common benchmark dataset. Key to our performance is the use of the ESPGAME dataset to select visually relevant relations from ConceptNet. Consequently, a main conclusion of this work is that general-purpose commonsense ontologies improve performance on visual reasoning tasks when properly filtered to select meaningful visual relations.Comment: Accepted in IJCAI-1

GeoCLEF 2006: the CLEF 2006 Ccross-language geographic information retrieval track overview

After being a pilot track in 2005, GeoCLEF advanced to be a regular track within CLEF 2006. The purpose of GeoCLEF is to test and evaluate cross-language geographic information retrieval (GIR): retrieval for topics with a geographic specification. For GeoCLEF 2006, twenty-five search topics were defined by the organizing groups for searching English, German, Portuguese and Spanish document collections. Topics were translated into English, German, Portuguese, Spanish and Japanese. Several topics in 2006 were significantly more geographically challenging than in 2005. Seventeen groups submitted 149 runs (up from eleven groups and 117 runs in GeoCLEF 2005). The groups used a variety of approaches, including geographic bounding boxes, named entity extraction and external knowledge bases (geographic thesauri and ontologies and gazetteers)

Restaurant Multi-Context-Based Information Retrieval System Ontological Model

This paper aims to improve information retrieval results by considering multi-context-based information that can be associated with retrieval. Traditional Information Retrieval has been termed inefficient because of its lack of consideration for individual user preference and contexts. An example domain where user preference and context consideration are expedient is the restaurant and food information retrieval domain. Current food-based ontologies do not provide sufficient information to tackle this challenge. We analysed existing food-based ontologies, developed and evaluated a restaurant-food-based ontology that provides application developers with a formalised restaurant-food ontology that will foster interoperability and information sharing within the domain. The ontology was developed using the methontology methodology for ontology development. Our restaurant-food ontology is based on ontology web language (OWL) and implemented in Protégé ontology editor. Using standard ontology evaluation measures of competency (in terms of precision and recall) and consistency, our results show that our ontology is 100% competent and can be used to build a range of applications that require answering a wide range of queries correctly that are general, detailed, context-based (location and environmental) and preference-based. This is currently, beyond what traditional Information retrieval and location-based systems can answer with accurac

Applying Biomedical Ontologies on Semantic Query Expansion

*1- Introduction*

The interpretation of a question (or information need) depends, among other things, of a series of lexicalsemantic relations that complement and help the cognitive process of answering that information need. Despite this fact, currently used information retrieval mechanisms take few advantages of the semantic interpretation of users’ information needs (usually specified through keywords). In most of the cases, those mechanisms are based on keyword matching, and thus are excessively dependant on the query and document terms.

There are several past results showing that, in general, information retrieval based on domain knowledge decreases the accuracy of keyword based search engines. We believe this approach deserves further discussion and experimentation, looking for more strong evidences that these negative results can really be generalized. Moreover, there are some questions left unanswered by previous work that our experiment is addressing:

(_i_) Using a scientific ontology, with formal construction and maintenance processes, such as the OBO ontologies, would produce better results? 

(_ii_) Are there more efficient query expansion techniques using available domain knowledge?

(_iii_) Is a scientific ontology complete enough to fulfill the information retrieval researchers’ needs, in general?

*2- Semantic Query Expansion*

To try to answer some of these questions, we run a query expansion experiment using the Gene Ontology (GO) as domain knowledge. As the document repository, we used an extraction of 10 years of PubMed publications (from 1994 to 2004), which contains approximately 4.6 Million documents. This dataset is a test collection used by the information retrieval community, called Genomic TREC.

*3- Results*
To evaluate our ontology-based semantic query expansion technique, we measured the effectiveness of the information retrieval mechanism with and without expansion. In a nutshell, the average result showed an increase of 28% on synonyms relations and a small decrease on other relations.

Our results show a lot of consistence with past related work. In fact, if the expansion strategy does not selectively choose when and how to expand, only synonym relations are worth to be used. However, looking further, it is possible to find several opportunities to try other expansion strategies. For example, the problem with query expansion using generalization/specialization relationships is that, if it is always applied, the bad results are more frequent than the good ones. But, if the strategy is to be selective on when to use these relations for expansion, the increasing on accuracy can be outstanding. As shown by our experiment, there was a query with 98% increment on effectiveness. 

*4- Conclusion*
We strongly believe that it is premature to assume that semantics-based query expansion is, in general, a recall-enhancing, precision-degrading technique. Our experiments suggest that by using scientific based ontologies (like OBO ontologies) with formal relations, it is possible to increase both recall and precision. Our group is currently revising this first experiment towards a better semantic query expansion strategy.

*5- Acknowledgements*
This work was partially funded by CAPES and CNPq research grants 311454/2006-2, 306889/2007-2 and 484713/2007-8.

*References*
_Fox E. Lexical relations enhancing effectiveness of information retrieval systems. SIGIR Forum, New York, v.15, n.3, p.5-3._

_Voorhees E. Query expansion using lexicalsemantic relations. In: ACM SIGIR conference on research and development in information retrieval, Proceedings, Dublin:17, p.61–69, 1994