Exploiting entity relationship for query expansion in enterprise search

Liu, Xitong; Chen, Fei; Fang, Hui; Wang, Min

doi:10.1007/s10791-013-9237-0

Cited by 20 publications

(9 citation statements)

References 46 publications

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“…Some other recent applications of QE are plagiarism detection [203], event search [89,15,43], text classification [269], patent retrieval [180,181,268], dynamic process in IoT [122,123], classification of e-commerce [128], biomedical IR [1], enterprise search [174], code search [205], parallel computing in IR [179] and twitter search [151,304]. Table 7 summarizes some of the prominent and recent applications of QE in literature based on the above discussion.…”

Section: Other Applicationsmentioning

confidence: 99%

Query expansion techniques for information retrieval: A survey

Azad

Deepak

2019

Information Processing & Management

229

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: Other Applicationsmentioning

confidence: 99%

Query expansion techniques for information retrieval: A survey

Azad

Deepak

2019

Information Processing & Management

229

104

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“…The task of Relation Extraction (RE) consists of detecting and classifying the semantic relations present in text. RE has been shown to benefit a wide range of NLP tasks, such as information retrieval (Liu et al, 2014), question answering (Ravichandran and Hovy, 2002) and textual entailment (Szpektor et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations

Marcheggiani

Titov

2016

TACL

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We present a method for unsupervised open-domain relation discovery. In contrast to previous (mostly generative and agglomerative clustering) approaches, our model relies on rich contextual features and makes minimal independence assumptions. The model is composed of two parts: a feature-rich relation extractor, which predicts a semantic relation between two entities, and a factorization model, which reconstructs arguments (i.e., the entities) relying on the predicted relation. The two components are estimated jointly so as to minimize errors in recovering arguments. We study factorization models inspired by previous work in relation factorization and selectional preference modeling. Our models substantially outperform the generative and agglomerative-clustering counterparts and achieve state-of-the-art performance.

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“…where γ is a score threshold parameter, and Z is a normalization factor, such that 0 ≤ P (e|q) ≤ 1. Additionally, entities relevant to those mentioned in the query may also be considered [34].…”

Section: Mapping Queries To Entitiesmentioning

confidence: 99%

“…Another approach is to use the various surface forms (aliases) of the query entities as expansion terms [18,34]. The main intuition behind doing so is that by including the different ways an entity can be referred to (i.e., its "synonyms"), documents relevant to that entity can be retrieved more effectively.…”

Section: Surface Formsmentioning

confidence: 99%

Leveraging Entities in Document Retrieval

Balog

2018

Entity-Oriented Search

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This chapter focuses on the classic IR task of ad hoc document retrieval and discusses how entities may be leveraged to improve retrieval performance. At their core, all document retrieval methods compare query and document representations. Traditionally, these representations are based on terms (words). Entities facilitate a semantic understanding of both the user's information need, as expressed by the keyword query, and of the document's content. Entities thus may be used to improve query and/or document representations. As a first step of that process, entities that are related to the query need to be identified, thereby establishing a mapping between queries and entities. As we shall explain in Sect. 8.1, one may go beyond considering only those entities that are explicitly mentioned in the query. We next present three different families of approaches, which are illustrated in Fig. 8.1. • Expansion-based methods utilize entities as a source of expansion terms to enrich the representation of the query (Sect. 8.2). • Projection-based methods treat entities as a latent layer, while leaving the original document/query representations intact (Sect. 8.3). • Entity-based methods consider explicitly the entities that are recognized in documents, as first-class citizens, and embrace entity-based representations in "duet" with traditional term-based representations in the retrieval model (Sect. 8.4). This particular order corresponds to the temporal evolution of research in this area, where the tendency toward more and more explicit entity semantics is clearly reflected. Throughout this chapter, we shall assume that both queries and documents have been annotated with entities, using entity linking techniques we have discussed before (see Chap. 5 for documents and Sect. 7.3 for queries). A particular challenge involved here is how to deal with the uncertainty of these automatic annotations. In practice, necessitated by efficiency considerations, all methods described in this chapter are implemented as re-ranking mechanisms. The details are found in Sect. 8.5. Finally, we present standard datasets and useful resources in Sect. 8.6. We refer to Table 8.1 for the notation used throughout this chapter.

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Exploiting entity relationship for query expansion in enterprise search

Cited by 20 publications

References 46 publications

Query expansion techniques for information retrieval: A survey

Query expansion techniques for information retrieval: A survey

Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations

Leveraging Entities in Document Retrieval

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