Wikipedia-based query phrase expansion in patent class search

Al-Shboul, Bashar; Myaeng, Sung-Hyon

doi:10.1007/s10791-013-9233-4

Cited by 28 publications

(13 citation statements)

References 37 publications

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“…Then, candidate phrases having low conceptual distance with respect to the query phrase are considered in the set of most appropriate phrases. Recently, Al-Shboul and Myaeng [7] presented a query phrase expansion approach using semantic annotations in Wikipedia pages. It tries to enrich the user query with the phrases that disambiguate the original query word.…”

Section: One-to-many Associationmentioning

confidence: 99%

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Query expansion techniques for information retrieval: A survey

Azad

Deepak

2019

Information Processing & Management

220

104

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With the ever increasing size of the web, relevant information extraction on the Internet with a query formed by a few keywords has become a big challenge. Query Expansion (QE) plays a crucial role in improving searches on the Internet. Here, the user's initial query is reformulated by adding additional meaningful terms with similar significance. QE -as part of information retrieval (IR) -has long attracted researchers' attention. It has become very influential in the field of personalized social document, question answering, cross-language IR, information filtering and multimedia IR. Research in QE has gained further prominence because of IR dedicated conferences such as TREC (Text Information Retrieval Conference) and CLEF (Conference and Labs of the Evaluation Forum). This paper surveys QE techniques in IR from 1960 to 2017 with respect to core techniques, data sources used, weighting and ranking methodologies, user participation and applications -bringing out similarities and differences.There is a huge amount of data available on the Internet, and it is growing exponentially. This unconstrained information-growth has not been accompanied by a corresponding technical advancement in the approaches for extracting relevant information [191]. Often, a web-search does not yield relevant results. There are multiple reasons for this. First, the keywords submitted by the user can be related to multiple topics; as a result, the search results are not focused on the topic of interest. Second, the query can be too short to capture appropriately what the user is looking for. This can happen just as a matter of habit (e.g., the average size of a web search is 2.4 words [257,255]). Third, the user is often not sure about what he is looking for until he sees the results. Even if the user knows what he is searching for, he does not know how to formulate an appropriate query (navigational queries are exceptions to this [51]). QE plays an important part in fetching relevant results in the above cases. Most web queries fall under the following three fundamental categories [51, 139] :-Informational Queries: Queries that cover a broad topic (e.g., India or journals) for which there may be thousands of relevant results. -Navigational Queries: Queries that are looking for specific website or URL (e.g., ISRO).-Transactional Queries: Queries that demonstrate the user's intent to execute a specific activity (e.g., downloading papers or buying books).2 Hiteshwar Kumar Azad, Akshay DeepakCurrently, user-queries are mostly processed using indexes and ontologies, which work on exact matches and are hidden from the users. This leads to the problem of term mismatch: user queries and search index are not based on the same set of terms. This is also known as the vocabulary problem [99]; it results from a combination of synonymy and polysemy. Synonymy refers to multiple words with common meaning, e.g., "buy" and "purchase". Polysemy refers to words with multiple meanings, e.g., "mouse" (a computer device or an animal). Synonymous and polysemou...

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Section: One-to-many Associationmentioning

confidence: 99%

“…Recently, quite a few research works have used it for QE (e.g., [167,14,286,3,9]). Al-Shboul and Myaeng [7] attempt to enrich initial queries using semantic annotations in Wikipedia articles combined with phrase-disambiguation. Experimental results show better results in comparison to the relevance based language model.…”

Section: Web-based Approachesmentioning

confidence: 99%

Query expansion techniques for information retrieval: A survey

Azad

Deepak

2019

Information Processing & Management

220

104

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“…It also proposed a phrase-scoring function. Reference [46] utilized Wikipedia to categorize the original query into three types: (1) ambiguous queries (queries with terms having more than one potential meaning), (2) entity queries (queries having a specific meaning that covers a narrow topic), and (3) broader queries (queries having neither an ambiguous nor specific meaning). They consolidated the expansion terms into the original query and evaluated these techniques using language modelling IR.…”

Section: Use Of Wikipedia As Data Source For Qementioning

confidence: 99%

A new approach for query expansion using Wikipedia and WordNet

Azad

Deepak

2019

Information Sciences

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Query expansion (QE) is a well-known technique used to enhance the effectiveness of information retrieval. QE reformulates the initial query by adding similar terms that help in retrieving more relevant results. Several approaches have been proposed in literature producing quite favorable results, but they are not evenly favorable for all types of queries (individual and phrase queries). One of the main reasons for this is the use of the same kind of data sources and weighting scheme while expanding both the individual and the phrase query terms. As a result, the holistic relationship among the query terms is not well captured or scored. To address this issue, we have presented a new approach for QE using Wikipedia and WordNet as data sources. Specifically, Wikipedia gives rich expansion terms for phrase terms, while WordNet does the same for individual terms.We have also proposed novel weighting schemes for expansion terms: in-link score (for terms extracted from Wikipedia) and a tf-idf based scheme (for terms extracted from WordNet). In the proposed Wikipedia-WordNet-based QE technique (WWQE), we weigh the expansion terms twice: first, they are scored by the weighting scheme individually, and then, the weighting scheme scores the selected expansion terms concerning the entire query using correlation score. The proposed approach gains improvements of 24% on the MAP score and 48% on the GMAP score over unexpanded queries on the FIRE dataset. Experimental results achieve a significant improvement over individual expansion and other related state-of-the-art approaches. We also analyzed the effect on retrieval effectiveness of the proposed technique by varying the number of expansion terms. makes information processing a big challenge and creates a vocabulary gap between user queries and indexed documents. It is common for a user's query Q and its set of relevant documents D to use different vocabulary and language styles while referring to the same concept. For example, terms 'buy' and 'purchase' have the same meaning; however, only one of these can be present in the documents' index while the other one can be the user's query term. This makes it difficult to retrieve the information actually wanted by the user [21,28]. An effective strategy to fill this gap is to use the Query Expansion (QE) technique, which enhances the retrieval effectiveness by adding expansion terms to the initial query. Selection of the expansion terms plays a crucial role in QE because only a small subset of the expanded terms are actually relevant to the query [26].In this sense, the approach for selection of expansion terms is equally important in comparison to what we do further with the expanded terms in order to retrieve the desired information. QE has a long research history in information retrieval (IR) [31,39]. It has the potential to enhance IR's effectiveness by adding relevant terms that can help to discriminate the relevant documents from irrelevant ones. The source of expansion terms plays a significant role in QE. A variety of ...

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“…Inevitably, CDS search based on PRF also suffers from a well-known problem of PRF called topic drifting, which degrades retrieval performance as the intention of the query topic could change in an unexpected direction due to erroneous extraction of the concepts to be expanded [34]. In order to reduce the effect of query drift, word embedding approach was utilized for term expansion [35] and search diversification [36].…”

Section: Related Workmentioning

confidence: 99%

A New Biomedical Passage Retrieval Framework for Laboratory Medicine: Leveraging Domain-specific Ontology, Multilevel PRF, and Negation Differential Weighting

Han

Shim

2018

Journal of Healthcare Engineering

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Clinical decision support (CDS) search is performed to retrieve key medical literature that can assist the practice of medical experts by offering appropriate medical information relevant to the medical case in hand. In this paper, we present a novel CDS search framework designed for passage retrieval from biomedical textbooks in order to support clinical decision-making using laboratory test results. The framework utilizes two unique characteristics of the textual reports derived from the test results, which are syntax variation and negation information. The proposed framework consists of three components: domain ontology, index repository, and query processing engine. We first created a domain ontology to resolve syntax variation by applying the ontology to detect medical concepts from the test results with language translation. We then preprocessed and performed indexing of biomedical textbooks recommended by clinicians for passage retrieval. We finally built the query-processing engine tailored for CDS, including translation, concept detection, query expansion, pseudo-relevance feedback at the local and global levels, and ranking with differential weighting of negation information. To evaluate the effectiveness of the proposed framework, we followed the standard information retrieval evaluation procedure. An evaluation dataset was created, including 28,581 textual reports for 30 laboratory test results and 56,228 passages from widely used biomedical textbooks, recommended by clinicians. Overall, our proposed passage retrieval framework, GPRF-NEG, outperforms the baseline by 36.2, 100.5, and 69.7 percent for MRR, R-precision, and Precision at 5, respectively. Our study results indicate that the proposed CDS search framework specifically designed for passage retrieval of biomedical literature represents a practically viable choice for clinicians as it supports their decision-making processes by providing relevant passages extracted from the sources that they prefer to refer to, with improved performances.

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Wikipedia-based query phrase expansion in patent class search

Cited by 28 publications

References 37 publications

Query expansion techniques for information retrieval: A survey

Query expansion techniques for information retrieval: A survey

A new approach for query expansion using Wikipedia and WordNet

A New Biomedical Passage Retrieval Framework for Laboratory Medicine: Leveraging Domain-specific Ontology, Multilevel PRF, and Negation Differential Weighting

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