Search for: All records

he advancement of web programming techniques, such as Ajax and jQuery, and datastores, such as Apache Solr and Elasticsearch, have made it much easier to deploy small to medium scale web- based search engines. However, developing a sustainable search engine that supports scholarly big data services is still challenging often because of limited human resources and financial support. Such scenarios are typical in academic settings or small businesses. Here, we showcase how four key design decisions were made by trading-off competing factors such as performance, cost, and effi- ciency, when developing the Next Generation CiteSeerX (NGX), the successor of CiteSeerX, which was a pioneering digital library search engine that has been serving academic communities for more than two decades. This work extends our previous work in Wu et al. (2021) and discusses design considerations of infrastruc- ture, web applications, indexing, and document filtering. These design considerations can be generalized to other web-based search engines with a similar scale that are deployed in small business or academic settings with limited resources. 

Recently, the Allen Institute for Artificial Intelligence released the Semantic Scholar Open Research Corpus (S2ORC), one of the largest open-access scholarly big datasets with more than 130 million schol- arly paper records. S2ORC contains a significant portion of automat- ically generated metadata. The metadata quality could impact down- stream tasks such as citation analysis, citation prediction, and link analysis. In this project, we assess the document linking quality and estimate the document conflation rate for the S2ORC dataset. Using semi-automatically curated ground truth corpora, we estimated that the overall document linking quality is high, with 92.6% of documents correctly linking to six major databases, but the linking quality varies depending on subject domains. The document confla- tion rate is around 2.6%, meaning that about 97.4% of documents are unique. We further quantitatively compared three near-duplicate detection methods using the ground truth created from S2ORC. The experiments indicated that locality-sensitive hashing was the best method in terms of effectiveness and scalability, achieving high performance (F1=0.960) and a much reduced runtime. Our code and data are available at https://github.com/lamps-lab/docconflation. 

Subject categories of scholarly papers generally refer to the knowledge domain(s) to which the papers belong, examples being computer science or physics. Subject category classification is a prerequisite for bibliometric studies, organizing scientific publications for domain knowledge extraction, and facilitating faceted searches for digital library search engines. Unfortunately, many academic papers do not have such information as part of their metadata. Most existing methods for solving this task focus on unsupervised learning that often relies on citation networks. However, a complete list of papers citing the current paper may not be readily available. In particular, new papers that have few or no citations cannot be classified using such methods. Here, we propose a deep attentive neural network (DANN) that classifies scholarly papers using only their abstracts. The network is trained using nine million abstracts from Web of Science (WoS). We also use the WoS schema that covers 104 subject categories. The proposed network consists of two bi-directional recurrent neural networks followed by an attention layer. We compare our model against baselines by varying the architecture and text representation. Our best model achieves micro- F 1 measure of 0.76 with F 1 of individual subject categories ranging from 0.50 to 0.95. The results showed the importance of retraining word embedding models to maximize the vocabulary overlap and the effectiveness of the attention mechanism. The combination of word vectors with TFIDF outperforms character and sentence level embedding models. We discuss imbalanced samples and overlapping categories and suggest possible strategies for mitigation. We also determine the subject category distribution in CiteSeerX by classifying a random sample of one million academic papers. 

While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar’s academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars’ research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining. 

We overview CiteSeerX, the pioneer digital library search engine, that has been serving academic communities for more than 20 years (first released in 1998), from three perspectives. The system perspective summarizes its architecture evolution in three phases over the past 20 years. The data perspective describes how CiteSeerX has created searchable scholarly big datasets and made them freely available for multiple purposes. In order to be scalable and effective, AI technologies are employed in all essential modules. To effectively train these models, a sufficient amount of data has been labeled, which can then be reused for training future models. Finally, we discuss the future of CiteSeerX. Our ongoing work is to make Cite- SeerX more sustainable. To this end, we are working to ingest all open access scholarly papers, estimated to be 30-40 million. Part of the plan is to discover dataset mentions and metadata in scholarly articles and make them more accessible via search interfaces. Users will have more opportunities to explore and trace datasets that can be reused and discover other datasets for new research projects. We summarize what was learned to make a similar system more sustainable and useful.