Inside ASCENT : Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering (2021)

Abstract: ASCENT is a fully automated methodology for extracting and consolidating commonsense assertions from web contents (Nguyen et al., 2021). It advances traditional triple-based commonsense knowledge representation by capturing semantic facets like locations and purposes, and composite concepts, i.e., subgroups and related aspects of subjects. In this demo, we present a web portal that allows users to understand its construction process, explore its content, and observe its impact in the use case of question answering. The demo website ( https://ascent.mpi-inf.mpg.de ) and an introductory video ( https://youtu.be/qMkJXqu_Yd4 ) are both available online.

## introduction commonsense knowledge (csk) is an enduring theme of ai @xcite that has been recently revived for the goal of building more robust and reliable applications @xcite . recent years have witnessed the emerging of large pretrained language models (lms), notably bert @xcite , @xcite and their variants which significantly boosted the performance of tasks requiring natural language understanding such as question answering and dialogue systems @xcite . although it has been shown that such lms implicitly store some commonsense knowledge @xcite , this comes with various caveats, for example regarding degree of truth, or negation, and their commercial development is inherently hampered by their low interpretability and explainability. structured knowledge bases (kbs), in contrast, give a great possibility of explaining and interpreting outputs of systems leveraging the resources. there have been great efforts towards building large-scale commonsense knowledge bases 1 https://ascent.mpi-inf.mpg.de 2 https://youtu.be/qmkjxqu_yd4 (cskbs), including expert-annotated kbs (e.g., @xcite ), crowdsourced kbs (e.g., con-ceptnet @xcite and atomic @xcite ) and kbs built by automatic acquisition methods such as webchild @xcite , @xcite , quasimodo @xcite and cskg @xcite . human-created kbs, although possessing high precision, usually suffer from low coverage. on the other hand, automatically-acquired kbs typically have better coverage, but also contain more noise. nonetheless, despite different construction methods, these kbs are all based on a simple subject-predicate-object model, which has major limitations in validity and expressiveness. we recently presented ascent @xcite , a methodology for automatically collecting and consolidating commonsense assertions from the general web. to overcome the limitations of prior works, ascent refines subjects with subgroups (e.g., circus elephant and domesticated elephant) and aspects (e.g., elephant tusk and elephant habitat), and captures semantic facets of assertions (e.g., lawyer, represents, clients, location: in courts or elephant, uses, its trunk, purpose: to suck up water ). for a given concept, ascent searches through the web with pattern-based search queries disambiguated using wordnet @xcite hypernymy. then, irrelevant documents are filtered out based on similarity comparison against the corresponding wikipedia articles. we then use a series of judicious dependency-parse-based rules to collect faceted assertions from the retained texts. the semantic facets, which come from prepositional phrases and supporting adverbs are then labeled by a supervised classifier. finally, assertions are clustered using similarity scores from word2vec @xcite and a fine-tuned roberta @xcite model. we executed the ascent pipeline for 10,000 prominent concepts (selected based on their respective number of assertions in conceptnet) as primary subjects. in @xcite , we showed that the content of the resulting cskb (hereinafter referred to as ascent kb) is a milestone in both salience and recall. as extrinsic evaluation, we conducted a comprehensive evaluation of the contribution of csk to zero-shot question answering (qa) with pre-trained language models @xcite . this paper presents a companion web portal of the ascent kb, which enables the following interactions: the web portal is available at https://ascent. mpi-inf.mpg.de , and a screencast demonstrating the system can be found at https://youtu.be/ qmkjxqu_yd4 . ## ascent two major contributions of ascent are its expressive knowledge model, and its state-of-the-art extraction methodology. details are in the technical paper @xcite . in this section, we revisit the most important points. ## commonsense qa setups one common extrinsic use case of kbs is question answering. recently, it was observed that priming language models (lms) with relevant context can considerably benefit their performance in qalike tasks @xcite . in @xcite , to evaluate the contribution of structured csk to qa, we conducted a comprehensive evaluation consisting of four different setups, all based on the above idea. examples of the qa setups can be seen in setup input sample output mp elephants eat [mask]. [sep] elephants eat roots, grasses, fruit, and bark, and they eat a lot of these things. everything (15.52%), trees (15.32%), plants (11.26%) fg c: elephants eat roots, grasses, fruit, and bark, and they eat... they eat a lot of grasses, fruits, and... q: what do elephants eat? a: gg c: elephants eat roots, grasses, fruit, and bark, and they eat... elephants eat a lot of things. q: what do elephants eat? a: elephants eat sp @xmath0 ## conclusion we presented a web portal for a state-of-the-art commonsense knowledge base-the ascent kb. it allows users to fully explore and search the cskb, inspect the construction process of each assertion, and observe the impact of structured cskbs on different qa tasks. we hope that the portal enables interesting interactions with the as-cent methodology, and that the qa demo allows researchers to explore the potentials of combining structured data with pre-trained language models.