Descriptive Knowledge Graph in Biomedical Domain (2023)

Abstract: We present a novel system that automatically extracts and generates informative and descriptive sentences from the biomedical corpus and facilitates the efficient search for relational knowledge. Unlike previous search engines or exploration systems that retrieve unconnected passages, our system organizes descriptive sentences as a relational graph, enabling researchers to explore closely related biomedical entities (e.g., diseases treated by a chemical) or indirectly connected entities (e.g., potential drugs for treating a disease). Our system also uses ChatGPT and a fine-tuned relation synthesis model to generate concise and reliable descriptive sentences from retrieved information, reducing the need for extensive human reading effort. With our system, researchers can easily obtain both high-level knowledge and detailed references and interactively steer to the information of interest. We spotlight the application of our system in COVID-19 research, illustrating its utility in areas such as drug repurposing and literature curation.

## introduction efficiently extracting knowledge from the vast and ever-growing corpus of literature is crucial for researchers to keep up with the latest discoveries and trends in the field. the covid-19 pandemic has highlighted this need, with thousands of related studies being published in a short period when a new disease emerges. however, surveying the latest findings requires significant effort, and researchers may struggle to see the big picture, leading to duplicated work and delaying the development of treatments @xcite . hence, an exploration system that can effectively retrieve comprehensive information from the latest literature corpus is important. existing exploration systems manage information in generally three granularities: documents, sentences, and knowledge facts. document-level retrieval usually takes keyphrases @xcite or questions @xcite as queries and finds relevant documents. using such systems, researchers need to read the retrieved documents to find relevant information, which is still time-consuming @xcite . sentence-level @xcite @xcite retrieval usually takes entities, entity types, or sentences as queries and finds sentences that contain the entities and entity types or are semantically similar to the input sentence. the retrieved sentences require less reading effort, but they are retrieved as independent text pieces, which don't provide a general overview of the knowledge. knowledge facts, on the other hand, are usually @xcite triples extracted from the corpus and stored as a knowledge graph, which concisely reveals the connection between entities. however, systems that retrieve knowledge facts @xcite usually allow queries for entities and entity types only, and the retrieved knowledge facts can only cover the relations in a fixed pre-defined set. to overcome the limitations mentioned above, we develop an exploration system that manages corpus sentences as a descriptive knowledge graph @xcite . the descriptive knowledge graph for explaining entity relationships (deer) is a special knowledge graph where each edge is not a relation label but a set of relational sentences describing the relationship between a pair of entities @xcite @xcite @xcite . we collect entities and sentences from the biomedical corpus to build a domain-specific deer and provide useful tools for users to effectively query and explore the graph. entities relation descriptions sentences corpus articles descriptive knowledge graph entity extraction & linking sentence scoring sentence filtering score filtering edge to interactively retrieve up-to-date, comprehensive, and easily understandable relational sentences, and explore relationships between entities in one-hop or multi-hop connections. additionally, we use chatgpt and a fine-tuned relation synthesis model to generate succinct relation descriptions for entity pairs based on the retrieved sentences to aid users' reading. it is worth mentioning that our system is automatically built without any supervised training or hand-crafted rules, making it seamlessly adaptable to any biomedical corpus with ease, and it can serve as a frontrunner for collecting knowledge in any future emergency. ## graph construction deer @xcite is a form of knowledge representation that balances the openness and informativeness of free text and the structured representation of the knowledge graph. in this graph, nodes are entities, and edges are sentences describing the relationship between the two nodes, called relation descriptions, pointing from the subject node to the object node in the sentences. the previous deer graph @xcite was built upon wikipedia. due to the limitations of the corpus, it does not contain much biomedical domain knowledge. in this section, we will introduce techniques for building a descriptive knowledge graph in the biomedical domain. based on that, users could retrieve sentences with efficient graph queries and view the result from a connected perspective to gain a more holistic understanding of the retrieved information. ## graph query to retrieve sentences from the coviddeer, our system provides a graph query module with some auxiliary tools to allow an interactive and flexible search. below are the queries supported by the graph query module. entity-entity query entity-entity query allows users to retrieve relation descriptions between two entities. this is achieved by extracting sentences that lie on the edge connecting the two entities in the constructed coviddeer graph. unlike from systems that retrieve all or random sentences where the entities co-occur, our system focuses on returning sentences that capture the primary relationships between the target entities. this gives users more informative and clearer sentences and saves users from being distracted by meaningless sentences. entity-type query to obtain a more comprehensive overview of relationships between an entity and an entity type, our system also supports entity-type query, which will retrieve sentences type frequent modifiers nouns treatment (14), chloroquine (6), efficacy (4), hydroxychloroquine (4), therapy (2), option (2), patient (2) verbs show (7), treat (5), use (5), propose (2), include (2) adjs apparent (4), antiviral (3), effective (3), safe (2), severe (2), antimalarial (2) from edges between an entity and all its neighbors belonging to an entity type. for instance, users can set the entity to covid-19 and the entity type to chemicals. then the system will return relation descriptions between covid-19 and all kinds of related chemicals, which provides some insights into the chemical-disease interactions related to covid-19. our system supports all the entity types in the ontologies mentioned in section 2.2. multi-hop query besides finding direct neighbors of an entity, users can also query multi-hop neighbors to explore more indirect connections. by specifying the entities or entity types at each hop, users can retrieve sentences for multi-hop inference. for example, a user may begin with covid-19, set symptom as the first-hop entity type and chemical as the second-hop entity type to explore drugs that can treat covid-19 related symptoms, and thus, could be used for covid-19 treatment. with this tool, our system could beat traditional knowledge graphs by providing the contextualized knowledge, and beat the text-based search engines by allowing multi-hop retrieval with one query. modifier filtering when querying a popular entity, coviddeer may return too many edges, which may distract users from catching the general relationships. to alleviate this, we define the words in a sentence that convey the relation information as the modifiers and allow users to locate interesting edges using the modifiers. we extract the noun phrases, verbs, and adjectives on the dependency path between the two entities as the modifiers. for example, ## relation synthesis model although coviddeer displays the relational sentences in a graph view to reveal the connections between entities, considerable manual effort is still required to read and digest the information on the edges. performing multi-hop logic inference is even harder as users need to find associated sentences across different edges. to reduce users' reading effort, we trained a relation synthesis model @xcite , which is based on a fusion-in-decoder model @xcite trained to take sequences of relation descriptions from the multi-hop paths between two entities in deer and generate one single relation description for the entities. each training data is collected by selecting the highest rds-scored sentences on each edge in the multi-hop paths between a target entity pair as the input and the highest rds-scored sentence on the one-hop path between the target pair as the output. in order to allow summarizing relation descriptions on each edge, we also add the lower-scored sentences on the one-hop path into the input. since large language models have demonstrated strong capabilities through simple prompting @xcite @xcite @xcite , in addition to the fine-tuned model, we also prompt chatgpt (openai, 2022) to generate a short passage to summarize the relationship from the retrieved sentences. detailed steps for finetuning and the prompt for chatgpt can be found in appendix b & c. by reading the generated relation descriptions first, users can get a general idea of the relation between the entities and then decide whether to read the retrieved sentences or not. ## system demonstration & evaluation in this section, we first evaluate the relation synthesis model by assessing the faithfulness of the generation with respect to the input relation descriptions. then we demonstrate our system's capacity in discovering unknown knowledge and locating information of interest with a drug repurposing task and a literature curation task respectively. ## related work exploration system exploration systems aim to help users learn the content in the data sources through simple queries @xcite . some systems are designed to retrieve sentencelevel text pieces for a specific need. @xcite retrieve textual evidence that semantically matches the queried statement. @xcite build a set of scientific challenges and directions extracted from a corpus and retrieves challenges and directions through entity co-occurrence. @xcite develop a lightweight query language to retrieve sentences that syntactically match an example sentence. in contrast, our system collects relational sentences into a graph structure and displays the retrieved sentences in a graphic view that shows the connection between the text pieces, which is not seen in previous works. literature-based discovery literature-based discovery (lbd) tools aim to discover unknown knowledge and generate new hypotheses by connecting current knowledge scattered in different literature together @xcite , which is commonly used in biomedical tasks like drug repurposing and interaction prediction. early lbd tools @xcite require manual effort in organizing information from the passages. recent studies @xcite approach lbd as a link prediction task over knowledge bases, where new knowledge is discovered as predicting new links between concepts. our system is more like the early lbd tools. with the high-quality relational sentences and the advance of llm in language understanding, our system greatly alleviates the user's reading workload and allows a rough verification of the generated hypothesis based on the retrieved sentences. ## conclusion in this work, we developed an exploration system in the biomedical domain that operates on a covidrelated corpus facilitating efficient retrieval of relational knowledge and enabling tasks such as drug repurposing and literature curation. we demonstrate the advantages of managing a raw text corpus in a descriptive knowledge graph, including streamlined management, support for multi-hop reasoning across sentences from various articles, and comprehensive visualization of entity connections in the domain. additionally, we equipped users with a modifier filtering module and a relation synthesis model that offer an overview of the relations on the edge before reading. in future work, we aim to enhance the accuracy and reliability of the relation descriptions generated for user reference.