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

Investigation on Applying Modular Ontology to Statistical Language Model for Information Retrieval

The objective of this research is to provide a novel approach to improving retrieval performance by exploiting Ontology with the statistical language model (SLM). The proposed methods consist of two major processes, namely ontology-based query expansion (OQE) and ontology-based document classification (ODC). Research experiments have required development of an independent search tool that can combine the OQE and ODC in a traditional SLM-based information retrieval (IR) process using a Web document collection. This research considers the ongoing challenges of modular ontology enhanced SLM-based search and addresses three contribution aspects. The first concerns how to apply modular ontology to query expansion, in a bespoke language model search tool (LMST). The second considers how to incorporate OQE with the language model to improve the search performance. The third examines how to manipulate such semantic-based document classification to improve the smoothing accuracy. The role of ontology in the research is to provide formally described domains of interest that serve as context, to enhance system query effectiveness

Exploiting semantics for improving clinical information retrieval

Clinical information retrieval (IR) presents several challenges including terminology mismatch and granularity mismatch. One of the main objectives in clinical IR is to fill the semantic gap among the queries and documents and going beyond keywords matching. To address these issues, in this study we attempt to use semantic information to improve the performance of clinical IR systems by representing queries in an expressive and meaningful context. In this study we propose query context modeling to improve the effectiveness of clinical IR systems. To model query contexts we propose two novel approaches to modeling medical query contexts. The first approach concerns modeling medical query contexts based on mining semantic-based AR for improving clinical text retrieval. The query context is derived from the rules that cover the query and then weighted according to their semantic relatedness to the query concepts. In our second approach we model a representative query context by developing query domain ontology. To develop query domain ontology we extract all the concepts that have semantic relationship with the query concept(s) in UMLS ontologies. Query context represents concepts extracted from query domain ontology and weighted according to their semantic relatedness to the query concept(s). The query context is then exploited in the patient records query expansion and re-ranking for improving clinical retrieval performance. We evaluate this approach on the TREC Medical Records dataset. Results show that our proposed approach significantly improves the retrieval performance compare to classic keyword-based IR model

CROEQS: Contemporaneous Role Ontology-based Expanded Query Search: implementation and evaluation

Searching annotated items in multimedia databases becomes increasingly important. The traditional approach is to build a search engine based on textual metadata. However, in manually annotated multimedia databases, the conceptual level of what is searched for might differ from the high-levelness of the annotations of the items. To address this problem, we present CROEQS, a semantically enhanced search engine. It allows the user to query the annotated persons not only on their name, but also on their roles at the time the multimedia item was broadcast. We also present the ontology used to expand such queries: it allows us to semantically represent the domain knowledge on people fulfilling a role during a temporal interval in general, and politicians holding a political office specifically. The evaluation results show that query expansion using data retrieved from an ontology considerably filters the result set, although there is a performance penalty

Hybrid Query Expansion on Ontology Graph in Biomedical Information Retrieval

Nowadays, biomedical researchers publish thousands of papers and journals every day. Searching through biomedical literature to keep up with the state of the art is a task of increasing difficulty for many individual researchers. The continuously increasing amount of biomedical text data has resulted in high demands for an efficient and effective biomedical information retrieval (BIR) system. Though many existing information retrieval techniques can be directly applied in BIR, BIR distinguishes itself in the extensive use of biomedical terms and abbreviations which present high ambiguity. First of all, we studied a fundamental yet simpler problem of word semantic similarity. We proposed a novel semantic word similarity algorithm and related tools called Weighted Edge Similarity Tools (WEST). WEST was motivated by our discovery that humans are more sensitive to the semantic difference due to the categorization than that due to the generalization/specification. Unlike most existing methods which model the semantic similarity of words based on either the depth of their Lowest Common Ancestor (LCA) or the traversal distance of between the word pair in WordNet, WEST also considers the joint contribution of the weighted distance between two words and the weighted depth of their LCA in WordNet. Experiments show that weighted edge based word similarity method has achieved 83.5% accuracy to human judgments. Query expansion problem can be viewed as selecting top k words which have the maximum accumulated similarity to a given word set. It has been proved as an effective method in BIR and has been studied for over two decades. However, most of the previous researches focus on only one controlled vocabulary: MeSH. In addition, early studies find that applying ontology won\u27t necessarily improve searching performance. In this dissertation, we propose a novel graph based query expansion approach which is able to take advantage of the global information from multiple controlled vocabularies via building a biomedical ontology graph from selected vocabularies in Metathesaurus. We apply Personalized PageRank algorithm on the ontology graph to rank and identify top terms which are highly relevant to the original user query, yet not presented in that query. Those new terms are reordered by a weighted scheme to prioritize specialized concepts. We multiply a scaling factor to those final selected terms to prevent query drifting and append them to the original query in the search. Experiments show that our approach achieves 17.7% improvement in 11 points average precision and recall value against Lucene\u27s default indexing and searching strategy and by 24.8% better against all the other strategies on average. Furthermore, we observe that expanding with specialized concepts rather than generalized concepts can substantially improve the recall-precision performance. Furthermore, we have successfully applied WEST from the underlying WordNet graph to biomedical ontology graph constructed by multiple controlled vocabularies in Metathesaurus. Experiments indicate that WEST further improve the recall-precision performance. Finally, we have developed a Graph-based Biomedical Search Engine (G-Bean) for retrieving and visualizing information from literature using our proposed query expansion algorithm. G-Bean accepts any medical related user query and processes them with expanded medical query to search for the MEDLINE database

Searching Ontologies Based on Content: Experiments in the Biomedical Domain

As more ontologies become publicly available, finding the "right" ontologies becomes much harder. In this paper, we address the problem of ontology search: finding a collection of ontologies from an ontology repository that are relevant to the user's query. In particular, we look at the case when users search for ontologies relevant to a particular topic (e.g., an ontology about anatomy). Ontologies that are most relevant to such query often do not have the query term in the names of their concepts (e.g., the Foundational Model of Anatomy ontology does not have the term "anatomy" in any of its concepts' names). Thus, we present a new ontology-search technique that helps users in these types of searches. When looking for ontologies on a particular topic (e.g., anatomy), we retrieve from the Web a collection of terms that represent the given domain (e.g., terms such as body, brain, skin, etc. for anatomy). We then use these terms to expand the user query. We evaluate our algorithm on queries for topics in the biomedical domain against a repository of biomedical ontologies. We use the results obtained from experts in the biomedical-ontology domain as the gold standard. Our experiments demonstrate that using our method for query expansion improves retrieval results by a 113%, compared to the tools that search only for the user query terms and consider only class and property names (like Swoogle). We show 43% improvement for the case where not only class and property names but also property values are taken into account

Utilising semantic technologies for intelligent indexing and retrieval of digital images

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion