Reexamining the MKM Value Proposition: From Math Web Search to Math Web ReSearch

Kohlhase, Andrea; Kohlhase, Michael

doi:10.1007/978-3-540-73086-6_25

Cited by 8 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, MIR research has been motivated primarily by developing new search techniques based on query-by-expression [75,171]. Better response to information needs will allow MIR to mirror the advances in internet search interfaces over the last two decades [61].…”

Section: Mathematical Information Retrievalmentioning

confidence: 99%

“…[75,171]) or constructed task scenarios is observed, with assessments provided by participants regarding the satisfaction of their information needs [22]. For off-line experiments such as those done for TREC, relevance assessments are usually binary (relevant/non-relevant) and produced before an experiment is run.…”

Section: Evaluation Of Math Retrieval Systemsmentioning

confidence: 99%

“…Kohlhase and Kohlhase suggest pen-based entry may be a more natural expression input modality [75]. We propose that pen-based entry will be most effective when paired with keyboard and mouse input.…”

mentioning

confidence: 97%

“…Despite the efforts to add expressions into query languages, their addition may not always add value for users [75]. Zhao et al studied a small group of profes-sors, graduate students and librarians affiliated with the Math Department at the National University of Singapore, and found that most of their participants could not identify a situation where they would want to search using an expression [171].…”

mentioning

confidence: 99%

See 3 more Smart Citations

Recognition and retrieval of mathematical expressions

Zanibbi

Blostein

2011

IJDAR

229

112

View full text Add to dashboard Cite

Document recognition and retrieval technologies complement one another, providing improved access to increasingly large document collections. While recognition and retrieval of textual information is fairly mature, with wide-spread availability of Optical Character Recognition (OCR) and text-based search engines, recognition and retrieval of graphics such as images, figures, tables, diagrams, and mathematical expressions are in comparatively early stages of research. This paper surveys the state of the art in recognition and retrieval of mathematical expressions, organized around four key problems in math retrieval (query construction, normalization, indexing, and relevance feedback), and four key problems in math recognition (detecting expressions, detecting and classifying symbols, analyzing symbol layout, and constructing a representation of meaning). Of special interest is the machine learning problem of jointly optimizing the component algorithms in a math recognition system, and developing effective indexing, retrieval and relevance feedback algorithms for math retrieval. Another important open problem is developing user interfaces that seamlessly integrate recognition and retrieval. Activity in these important research areas is increasing, in part because math notation provides an excellent domain for studying problems common to many document and graphics recognition and retrieval applications, and also because mature applications will likely provide substantial benefits for education, research, and mathematical literacy.

show abstract

Section: Mathematical Information Retrievalmentioning

confidence: 99%

Section: Evaluation Of Math Retrieval Systemsmentioning

confidence: 99%

mentioning

confidence: 97%

mentioning

confidence: 99%

See 2 more Smart Citations

Recognition and retrieval of mathematical expressions

Zanibbi

Blostein

2011

IJDAR

229

112

View full text Add to dashboard Cite

show abstract

“…There are two general approaches to searching mathematical formulas, i.e., employing string representations of mathematical formulas, which uses conventional information retrieval methods, and leveraging the structure inherent in content representations [1], [16].…”

Section: B Mathematical Search Systemsmentioning

confidence: 99%

Exploiting textual descriptions and dependency graph for searching mathematical expressions in scientific papers

Kristianto

Topić

Aizawa

2014

Ninth International Conference on Digital Information Management (ICDIM 2014)

View full text Add to dashboard Cite

Mathematical expressions are important for communication of scientific information, for instance, to explain or define concepts written in natural language. Despite their importance, current conventional search systems can not establish access to the mathematical expressions contained in a scientific paper. The major focus of current development of mathematical search systems is mathematical tree structure indexing, but utilizing textual information surrounding the expressions in these systems is also important. We examine how textual information contributes to a mathematical search system, primarily in the ranking process. We investigate the impact of two types of textual information in the ranking performances of a mathematical search system: words in context windows (baseline), which is easily extracted from sentence tokenization result, and descriptions, which are extracted using a machine learning method. We also examine the improvement in ranking obtained by utilizing the dependency graph of mathematical expressions. The experiment results show that the use of description and dependency graph together deliver better ranking performance than the use of context or when no textual information is used. The results also show that the dependency graph is crucial for increasing the number of mathematical expressions being assigned descriptions, and thus its use with descriptions together presented higher ranking performance than the use of descriptions only. This study suggests that descriptions represent mathematical expressions better (more precisely) than context windows, and even descriptions from child (indirect) expressions still represent the target expression better than the context from the target expression itself.

show abstract

A Survey on Retrieval of Mathematical Knowledge

Guidi

Coen

2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. We present a short survey of the literature on indexing and retrieval of mathematical knowledge, with pointers to 72 papers and tentative taxonomies of both retrieval problems and recurring techniques. Purpose Driven Taxonomy of Retrieval ProblemsRetrieval of mathematical knowledge is always presented as the low hanging fruit of Mathematical Knowledge Management, and it has been addressed in several papers by people coming either from the formal methods or from the information retrieval community. The problem being resistant to classical content search techniques [LRG13], it is usually addressed combining a small set of new ideas and techniques that are recurrent in the literature. Despite the amount of work, however, there is not a single solution that is the clearly winning on the others, nor convincing unbiased benchmarks to compare solutions. Some authors like [KK07] also suggest that the community should first better understand the actual needs of mathematicians from an unbiased perspective to improve the MKM technology as a whole. In this paper we collect a hopefully comprehensive bibliography, and we roughly classify the papers according to novel taxonomies both for the problems and the techniques employed. The only other surveys on the same topic are [AZ04], now outdated and focused mostly on (European) research projects that contributed to the topic in the 6th Framework Programme, [ZB12], which covers less literature in much greater detail without attempting a classification, [L13], which is focused on evaluation of mathematics retrieval, and [L10], which is written in Slovak.We begin our discussion with a purpose driven taxonomy made of three different retrieval problems that deal with mathematical knowledge. Each problem is characterised by its own set of expectations and constraints, and adopting a solution to another problem may be infeasible or yield poor results. In the next sections we classify the papers according to an encoding based taxonomy (presentation vs content vs semantics) and to a taxonomy of techniques employed. Finally we point to the rich literature relative to the problem of ranking, and we touch the problem of evaluation of systems. We conclude with some notes on the availability of math retrieval systems.⋆ The final publication is available at http://link.springer.com.

show abstract

Reexamining the MKM Value Proposition: From Math Web Search to Math Web ReSearch

Cited by 8 publications

References 14 publications

Recognition and retrieval of mathematical expressions

Recognition and retrieval of mathematical expressions

Exploiting textual descriptions and dependency graph for searching mathematical expressions in scientific papers

A Survey on Retrieval of Mathematical Knowledge

Contact Info

Product

Resources

About