CSFCube: A Test Collection of Computer Science Research Articles for Faceted Query by Example

Published on [DATE]

Introduction

In the field of computer science, the ability to effectively search and retrieve relevant research articles is crucial for researchers and practitioners. However, traditional keyword-based search methods often fail to capture the nuances and complexities of research topics. To address this issue, a team of researchers, including Sheshera Mysore, Tim O’Gorman, Andrew McCallum, and Hamed Zamani, have developed CSFCube, a test collection of computer science research articles specifically designed for faceted query by example.

Faceted Query by Example

Faceted query by example is a search method that allows users to specify their information needs using a combination of example documents and facets. Facets are predefined categories or attributes that can be used to refine search results. By using example documents, users can provide specific instances of what they are looking for, and the system can then retrieve similar documents based on the provided examples and facets.

The Need for CSFCube

Existing test collections for information retrieval often focus on general-purpose documents, such as news articles or web pages. However, computer science research articles have unique characteristics that require specialized test collections. CSFCube aims to fill this gap by providing a test collection specifically tailored for computer science research articles.

Construction of CSFCube

To construct CSFCube, the researchers collected a large corpus of computer science research articles from various sources, including conferences and journals. They then annotated these articles with facets, such as research topics, methodologies, and datasets used. The annotations were done by domain experts to ensure accuracy and relevance.

Evaluation and Use Cases

CSFCube can be used for various evaluation tasks, such as faceted search, recommendation systems, and query understanding. Researchers and developers can use CSFCube to benchmark their algorithms and systems, compare different approaches, and measure performance. The test collection provides a standardized and reproducible evaluation framework for the development and evaluation of faceted query by example systems.

Conclusion

CSFCube is a valuable resource for the computer science community, providing a test collection specifically designed for faceted query by example. By using CSFCube, researchers and practitioners can improve the effectiveness and efficiency of their information retrieval systems in the domain of computer science research articles.

Example-based Search: A New Frontier for Exploratory Search

Published on July 15, 2019

For more information, please contact us at examplesearch@domain.com.

Introduction

Exploratory search is a process that involves searching for information when the user's needs are not well-defined. Traditional keyword-based search engines may not be effective in such scenarios. However, example-based search offers a new frontier for exploratory search, allowing users to provide examples or prototypes of the desired information. In this blog, we will explore the concept of example-based search and its potential in revolutionizing the way we search for information.

Background

Traditional search engines rely on keyword-based queries, which may not capture the user's intent accurately in exploratory search scenarios. Example-based search, on the other hand, allows users to provide examples or prototypes of the desired information, making the search process more intuitive and effective. This approach has gained attention in recent years due to its potential to improve the search experience.

Example-based Search Techniques

Various example-based search techniques have been developed to enhance the exploratory search process. These techniques include query-by-example, content-based image retrieval, and similarity search. Each technique has its own strengths and limitations, and the choice of technique depends on the specific application and user requirements. By leveraging these techniques, users can search for information based on examples rather than relying solely on keywords.

Challenges in Example-based Search

While example-based search offers promising benefits, there are several challenges that need to be addressed. One of the challenges is the representation of examples and their matching with the target information. Finding effective ways to represent examples and accurately match them with relevant information is crucial for the success of example-based search systems. Additionally, scalability is another challenge, especially when dealing with large datasets. Efficient algorithms and techniques are required to handle the computational complexity of example-based search. Furthermore, there is a need for effective evaluation metrics to assess the performance of example-based search systems and compare them with traditional keyword-based search engines.

Example-based Search in Neural Information Retrieval

The integration of example-based search with neural information retrieval techniques has shown promising results. The paper discusses the concept of entity-duet neural ranking, which leverages knowledge graph semantics to improve the relevance and effectiveness of example-based search. By incorporating knowledge graph semantics, the search results can be more accurate and aligned with the user's intent. Experimental results on a large dataset demonstrate the potential of this approach in enhancing example-based search.

The Semantic Scholar Open Research Corpus

The Semantic Scholar Open Research Corpus (S2ORC) is a valuable dataset that provides access to a large collection of scholarly articles. This dataset can be utilized for various research purposes, including example-based search. Researchers can leverage the S2ORC dataset to develop and evaluate example-based search techniques, further advancing the field.

Conclusion

Example-based search has the potential to revolutionize exploratory search by providing a more intuitive and effective way of searching for information. By allowing users to provide examples or prototypes, the search process becomes more user-centric and aligned with the user's intent. However, there are still challenges that need to be addressed, such as the representation of examples, scalability, and evaluation metrics. Further research and development are required to fully exploit the benefits of example-based search. The integration of knowledge graph semantics and the availability of large datasets like S2ORC can contribute to the advancement of example-based search techniques.

For more information, please contact us at examplesearch@domain.com.

Publication source

See the PDF from which this article has been generated:

PDF source url: https://aclanthology.org/2022.naacl-main.331.pdf