A Query Expansion Method Based on Evolving Source Code

Proksch has proved the changed terms of source code negatively affect code search quality. However, current query expansion (QE) methods always ignore it. In this paper we propose a novel QE method based on the semantics of change sequences (QESC). It not only captures which changes occurred by ext racting change sequences from Github commits, but also understands why changes occurred by learning sequence semantics with Deep Belief Network (DBN). Thus it could extract relevant terms to expand or irrelevant terms to exclude from the changes semantically similar to a query. Our experimental results show QESC outperforms the existing QE methods by 15–23% in terms of precision on inspecting the first query result.

Queries to search engines on the Web are usually short. They do not provide sufficient information for an effective selection of relevant documents. Previous research has proposed the utilization of query expansion to deal with this problem. However, expansion terms are usually determined on term co-occurrences within documents. In this study, we propose a new method for query expansion based on user interactions recorded in user logs. The central idea is to extract correlations between query terms and document terms by analyzing user logs. These correlations are then used to select high-quality expansion terms for new queries. Compared to previous query expansion methods, ours takes advantage of the user judgments implied in user logs. The experimental results show that the log-based query expansion method can produce much better results than both the classical search method and the other query expansion methods.

Manifold ranking has been successfully applied in query-oriented multi-document summarization. It not only makes use of the relationships among the sentences, but also the relationships between the given query and the sentences. However, the information of original query is often insufficient. So we present a query expansion method, which is combined in the manifold ranking to resolve this problem. Our method not only utilizes the information of the query term itself and the knowledge base WordNet to expand it by synonyms, but also uses the information of the document set itself to expand the query in various ways (mean expansion, variance expansion and TextRank expansion). Compared with the previous query expansion methods, our method combines multiple query expansion methods to better represent query information, and at the same time, it makes a useful attempt on manifold ranking. In addition, we use the degree of word overlap and the proximity between words to calculate the similarity between sentences. We performed experiments on the datasets of DUC 2006 and DUC2007, and the evaluation results show that the proposed query expansion method can significantly improve the system performance and make our system comparable to the state-of-the-art systems.

Accepted: 24.11.2015 In this paper, a hybrid approach is developed for geospatial information retrieval which combines query expansion (QE) and latent semantic indexing (LSI) methods. The hybrid method uses the advantages of Query Expansion and Semantic indexing methods for improving search results. LSI establishes relations depending on the similarities between queries and the documents where QE helps by adding extra similar terms to the queries. The dataset is populated using data extracted from Wikipedia and USGS (The United States Geological Survey). Automation is programmed to get all the information from the web and to extract the meaningful words. The significant terms are the name of the places, directions, and nearby locations. The dataset includes mountains and places with their latitudes, longitudes, locations and directions. The extracted data is also taken into Protege for semantically querying. The approach is applied to geographical data (popular search items on search engines), and by querying the data with the combination of LSI and QE more related results are handled than the results of methods when they are applied alone. Many queries are run, and the results are listed and compared. At the end of the study it is understood that the hybrid method considerably improves the search results.

Information retrieval systems utilize user feedback for generating optimal queries with respect to a particular information need. However, the methods that have been developed in IR for generating these queries do not memorize information gathered from previous search processes, and hence cannot use such information in new search processes. Thus, a new search process cannot profit from the results of the previous processes. Web Information Retrieval systems should be able to maintain results from previous search processes, thus learning from previous queries and improving overall retrieval quality. In this chapter, we are using the similarity of a new query to previously learned queries. We then expand the new query by extracting terms from documents, which have been judged as relevant to these previously learned queries. Thus, the new method uses global feedback information for query expansion in contrast to local feedback information, which has been widely used in previous work in query expansion methods. Experimentally, we compared a new query expansion method with two conventional information retrieval methods in local and global query expansion to enhance the traditional information system. From the results gathered it can be concluded that although the traditional IR system performance is high, but we notice that PRF method increases the average recall and decreases the fallout measure.

This paper study about a review of the literature on Query Expansion (QE) methods. The main aspects of the review study are the methods, limitations of the QE methods, and datasets used in QE. As the results of this study, we can conclude several methods used on QE i.e., Ontology, Association Rules, Wordnet, Methathesaurus, Synonym mapping, Concept-based, Local Co-occurrence, and Latent Semantic Indexing (LSI). Each of the methods still has limitations. For datasets used in QE, many researchers use public datasets.

With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

Query expansion methods have been proven to be effective to improve the average performance of patent retrieval, and most of query expansion methods use single source of information for query expansion term selection. In this paper, we propose a method which exploits external resources for improving patent retrieval. Google search engine and Derwent World Patents Index were used as external resources to enhance the performance of query expansion methods. LambdaRank was employed to improve patent retrieval performance by combining different query expansion methods with different text fields weighting strategies of different resources. Experiments on TREC data sets showed that our combination of multiple information sources for query formulation was more effective than using any single source to improve patent retrieval performance.

In this paper we propose the schema of 3-layer domain ontology. The entity relation, entity-attribute pair and textual description are distributed in layers respectively. Based on the 3-layer ontology we proposed 4 query expansion methods: top-down expansion, synonyms expansion, bottom-up expansion and backtracking expansion. A set of experiment1 is carried out to prove the expansion methods. Different result shows the expansion methods is effective as a whole, and we analysis the shortage of our methods.

Entity-based ranking is a popular research area in document ranking as knowledge graphs can introduce additional evidence for ranking systems. In most entity-based ranking framework, the original query is transformed into a set of entities and candidate documents are ranked according to their relevance to the entity set. However, mistakes can easily occur during the transformation process, which introduce noise to the ranking system. This paper represents an expansion-based ranking framework, which expand the original query with high-quality candidate entities to mitigate the impact of transformation mistakes and enrich the semantics of the query in the meanwhile. Compared with existing query expansion methods, our expansion method is lightweight as it reduces much computational burden and requests little extra information. However, the core concerns of the users might be covered up after query expansion. To alleviate this dilemma, a core concern capturing method is pro-posed, wherein the attention weight of each inter-entity relation is measured to capture the core entity pairs in the expanded query. Experiments on the S2-CS dataset demonstrate the effectiveness of our lightweight query expansion method and the superiority of our core concern capturing method. Compared with a classical retrieval model, our method provides 11% improvements in terms of NDCG@15.

Increasing size of web leads to relevant information extraction by traditional and last query expansion methods not to be efficient and encounter an essential challenge. Therefore, to improve query expansion, in this paper, a novel hybrid method called HQEBSKG is introduced. In the first part, a new and efficient technique is proposed based on semantic knowledgebase to expand users’ queries. In the following part, a useful method based on semantic relations between words is also suggested for query expansion. In the current work, to achieve the semantic relations, FarsNet and WordNet ontologies are used simultaneously to enrich knowledge resources. The experimental results show performance improvement.

We present a method for automatic query expansion for cross-lingual information retrieval in the medical domain. The method employs machine translation of source-language queries into a document language and linear regression to predict the retrieval performance for each translated query when expanded with a candidate term. Candidate terms (in the document language) come from multiple sources: query translation hypotheses obtained from the machine translation system, Wikipedia articles and PubMed abstracts. Query expansion is applied only when the model predicts a score for a candidate term that exceeds a tuned threshold which allows to expand queries with strongly related terms only. Our experiments are conducted using the CLEF eHealth 2013–2015 test collection and show significant improvements in both cross-lingual and monolingual settings.

In this paper, we address a novel method of Web query expansion by using WordNet and TSN. WordNet is an online lexical dictionary which describes word relationships in three dimensions of Hypernym, Hyponym and Synonym. And their impacts to expansions are different. We provide quantitative descriptions of the query expansion impact along each dimension. However, WordNet may bring many noises for the expansion due to its collection independent characteristic. Furthermore, it may not catch current state of words and their relationships because of the explosive increase of the Web. To overcome those problems, collection-based TSN (Term Semantic Network) is created with respect to word co-occurrence in the collection. We use TSN both as a filter and a supplement for WordNet. We also provide a quantitatively study as what is the best way for the expansion with TSN. In our system, we combine the query expansions along each semantic dimension as our overall solution. Our experiments reveal that the combined expansion can provide a satisfied result for the Web query performance. The methodologies in this paper have been already employed in our Web image search engine system.

We will present a new algorithm for improving the retrieval performance using query expansion, based on a hierarchy of clusters. In order to create this hierarchical data structure, a clustering algorithm is executed multiple times with different initial conditions. With the aid of this hierarchical data structure, we have achieved significant improvement in retrieval performance over previously known methods in terms of both recall and precision. In our experiments with Japanese patent data, we have employed a co-clustering algorithm as a clustering method.

Searching answers to complex questions is a challenging IR task. In this paper, we examine the use of query templates with semantic slots to formulate slot-based queries. These queries have query terms assigned to entity and relationship slots. We develop several query expansion methods for slot-based queries so as to improve their retrieval effectiveness on a document collection. Each method consists of a combination of term scoring scheme, term scoring formula, and term assignment scheme. Our preliminary experiments evaluate these different slot-based query expansion methods on a collection of news documents,and conclude that:(1) slot-based queries yield better retrieval accuracy compared to keyword-based queries in the complex question problems; and (2)directly applying traditional query expansion on the query terms of each slot does not always work well.