Knowledge graphs (KGs) are relevant to many NLP tasks, but building a reliable domain-specific KG is time-consuming and expensive . A number of methods for constructing KGs with minimized human interventionhave been proposed, but still require a process to align into the human-annotated knowledge base . To overcome this issue, we propose a novel method to automatically construct a KG from unstructured documents that does not require external alignment and explore its use to extract desired information. To summarize our approach, we first extract knowledge tuples in their surface form from unstructured documents , encode them using a pre-trained language model, and link the surface-entities via the encoding to form the graph structure. We perform experiments with benchmark datasets such as WikiMovies and MetaQA  . The experimental results show that our method can successfully create and search a KG with 18K documents and achieve 69.7\% hits@10 (close to an oracle model) on a query retrieval task .
Knowledge graphs (KGs) have the advantage of providing fine-grained detail for question-answering systems. Unfortunately, building a reliable KG is time-consuming and expensive as it requires human intervention . To overcome this issue, we propose a novel framework to automatically construct a KG from unstructured documents that does not require external alignment . We first extract surface-form knowledge tuples from unstructured documents and encode them with contextual information. Entities with similar context semantics are then linked through internal alignment to form a graph structure. This allows us to extract the desired information from multiple documents by traversing the generated KG without a manual process. We examine its performance in retrieval based QA systems by reformulating the WikiMovies and MetaQA datasets into a tuple-level retrieval task . The experimental results show that our method outperforms traditional retrieval methods by a large margin .
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are relevant to many NLP tasks, but
have the advantage of providing fine-grained detail for question-answering systems. Unfortunately,
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domain-specific
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. A number of methods for constructing KGs with minimized human interventionhave been proposed, but still require a process to align into the human-annotated knowledge base
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and explore its use to extract desired information. To summarize our approach, we first extract knowledge tuples in their surface form
. We first extract surface-form knowledge tuples
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, encode them using a pre-trained language model, and link the surface-entities via the encoding to form the
and encode them with contextual information. Entities with similar context semantics are then linked through internal alignment to form a
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We perform experiments with benchmark datasets such as
This allows us to extract the desired information from multiple documents by traversing the generated KG without a manual process. We examine its performance in retrieval based QA systems by reformulating the
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datasets into a tuple-level retrieval task
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can successfully create and search a KG with 18K documents and achieve 69.7\% hits@10 (close to an oracle model) on a query retrieval task
outperforms traditional retrieval methods by a large margin