C og IE : An Information Extraction Toolkit for Bridging Texts and C og N et (2021)

Abstract: CogNet is a knowledge base that integrates three types of knowledge: linguistic knowledge, world knowledge and commonsense knowledge. In this paper, we propose an information extraction toolkit, called CogIE, which is a bridge connecting raw texts and CogNet. CogIE has three features: versatile, knowledge-grounded and extensible. First, CogIE is a versatile toolkit with a rich set of functional modules, including named entity recognition, entity typing, entity linking, relation extraction, event extraction and frame-semantic parsing. Second, as a knowledge-grounded toolkit, CogIE can ground the extracted facts to CogNet and leverage different types of knowledge to enrich extracted results. Third, for extensibility, owing to the design of three-tier architecture, CogIE is not only a plug-and-play toolkit for developers but also an extensible programming framework for researchers. We release an open-access online system to visually extract information from texts. Source code, datasets and pre-trained models are publicly available at GitHub, with a short instruction video.

## introduction knowledge bases (kbs) such as framenet @xcite , dbpedia @xcite , wikidata @xcite , and con-ceptnet @xcite are becoming popular for a variety of downstream tasks including information retrieval, recommender system and dialog system. @xcite divide kbs into three categories according to the type of knowledge, respectively linguistic kbs (e.g., framenet), world kbs (e.g., dbpedia, wikidata) and commonsense kbs (e.g., conceptnet). unlike most of the above kbs which focus on a single type of knowledge, cognet @xcite models linguistic, world and commonsense knowledge using a unified representation architecture for better knowledge integration. to apply cognet to downstream tasks, it is challenging to expand cognet and ground raw texts to cognet automatically. for this target, information extraction (ie) is an effective method, which aims to extract entity, relation, event, and other factual information from raw texts and link them to kbs. with the rapid development of ie area, a few remarkable open-source toolkits have been developed in recent years. the mainstream toolkits can be classified into two categories: task-specific toolkits and task-agnostic toolkits. task-specific toolkits focus on one or a few specific tasks, such as flair @xcite for named entity recognition (ner), blink (ledell wu, 2020) for entity linking (el), opennre @xcite for relation extraction (re) and open-sesame @xcite for frame-semantic parsing. on the other end of the spectrum, allennlp @xcite , opennmt @xcite and other task-agnostic toolkits are designed to provide programming framework without the implementation of specific tasks. as mentioned above, various toolkits have been widely used, but they also suffer from several limitations. first, most of the existing nlp toolkits only support one or a few ie functions, and there is a lack of an integrated and efficient ie toolkit. second, very few ie toolkits can align the extracted facts to kbs, which may cause the extracted facts not to be applied directly to downstream tasks. third, for an efficient and effective toolkit, providing application program interfaces (apis) is as important as supporting the secondary development. still, only a few toolkits can do both at the same time. therefore, it is highly desirable to have an opensource toolkit that can implement and integrate various ie tasks and can take advantage of the knowledge resources in kbs to enrich the extracted facts. such a toolkit should achieve the equilibrium among usability, extensibility and efficiency. ## core functions cogie is designed for a series of ie functions, including named entity recognition, entity typing, entity linking, relation extraction, event extraction, and frame-semantic parsing, etc. cogie can also align the extracted facts to cognet via entity linking, relation matching and frame matching. as shown in figure 2 , we give some examples to illustrate these functions. ## system usage our goal of designing cogie is to provide a userfriendly toolkit for users by achieving the equilibrium among usability, extensibility and efficiency. ## system usage our goal of designing cogie is to provide a userfriendly toolkit for users by achieving the equilibrium among usability, extensibility, and efficiency. ## experiment and evaluation in this section, we train and evaluate cogie on several datasets in different tasks. each task's performance is shown in ## conclusion and future work in this paper, we propose cogie, an information extraction toolkit for bridging texts and cognet. we have shown that cogie is a plug-and-play toolkit and an extensible programming framework due to its application -module -code three-tier architecture design. moreover, as an integrated and professional ie toolkit, cogie can extract information from texts while aligning the extracted facts to cognet and other kbs. we conduct experiments on several datasets in different tasks, and the evaluation results demonstrate the models implemented by cogie are efficient. in the future, we consider the following points during improvement: (1) to use cogie on any device, we will further optimize model sizes and speed up computation in cogie while striking a balance between accuracy and efficiency; (2) for making models robust to the texts of different domains and styles, we plan to utilize various sources of consistent data to train a universal model; (3) we will build an open-source community for cogie so that all researchers can contribute their models and participate in long-term maintenance.