## 3 Materials and Methods
### Implementation
#### 3.2.3 Parallel Triple Extraction  
Our approach aims at distilling knowledge from texts that can be used directly for end-user applications such as structured searches (e.g., \"_Find all triples containing SARS-CoV-2 as a Subject along with all related papers_\"). Therefore, triple extraction is the third and core step of our knowledge extraction  
Figure 2: Word reduction per paper using extractive summarization. The original body text of each article (blue) is replaced by its summary (green). This preprocessing step aims at selecting the most representative sentences of the given documents, despite their original length. pipeline, in an effort to discover relations between the entities of the CORD-19 corpus. Instead of restricting ourselves to a subset of relations in a pre-defined ontology (closed information extraction), we leveraged an open information extraction (OIE) approach, where triple formulation is defined as the task of generating a structured, machine-readable representation of the information in text, with the length of each triple element varying from a single word to a short text phrase. Moreover, instead of limiting ourselves to a single engine, we combined the three most popular OIE systems, namely the Open IE system from the University of Washington (UW) and Indian Institute of Technology, Delhi (IIT Delhi, India) [71], ClausIE from Max Planck Institute (MPI) [72], and the neural OIE model from AllenNLP [73]. A brief explanation of the intuition behind each system is provided below:  
1. Open IE 5.1 from UW and IIT Delhi is a successor to the Ollie learning-based information extraction system [74]. The latest version is based on the combination of four different rule-based and learning-based OIE tools; namely CALMIE (specializing in triple extraction from conjunctive sentences) [75], RelNoun (for noun relations) [76], BONIE (for numerical sentences) [77], and SRLIE (based on semantic role labeling) [78]. 2. ClausIE from MPI follows a clause-based approach, first identifying the clause type of each sentence and then applying specific proposition extraction based on the corresponding grammatical function of the clause's constituents. It also considers nested clauses as independent sentences. Because ClausIE detects useful pieces of information expressed in a sentence before representing them in terms of one or more extractions, it is especially useful in splitting complex sentences into many individual triples [41]. 3. AllenNLP OIE system formulates the triple extraction problem as a sequence BIO tagging problem and applies a bi-LSTM transducer to produce OIE tuples, which are grouped by each sentence's predicate [72]. Given that it relies on supervised learning and contextualized word embeddings to produce independent probability distributions over possible BIO tags for each word, it has the potential of discovering richer and more complex relations. On the downside, it is not guaranteed that the neural sequence tagger will produce exactly two arguments for a given predicate (i.e., a subject and an object), thus complicating the triple extraction process. We implemented a parallel triple extraction sequence based on the aforementioned OIE systems, where the extractive summary of every CORD-19 article from the previous step was passed through each one of the three engines. Given that Open IE 5.1 is based on handcrafted extraction heuristics and automatically constructed triple extractors, that ClausIE follows a rule-based approach which exploits linguistic knowledge about the grammar (clause types) of the English language and that AllenNLP OIE system is dependent on the context's vector representation to detect parts of speech, we relied on the complementarity between the different approaches to ensure maximum recall. The only shortcoming of this method is that it inevitably leads to a higher duplication rate compared to using a single engine; however, this issue was effectively tackled in the next and final stage of our pipeline.