TECHNICAL FIELD

This disclosure relates generally to creating a knowledge graph based on knowledge corpora. More specifically, but not by way of limitation, this disclosure relates to creating a knowledge graph by analyzing relations between noun-phrases contained in general text-based knowledge corpora to create the knowledge graph.

BACKGROUND

Knowledge corpora for a specific domain, such as textbooks, notes, and academic papers, contain a large amount of information about this domain. For example, a textbook in the domain of computer networks typically contains various information about computer networks, such as the types of computer networks, the components in a computer network, the ways to construct a network, the benefits and drawbacks of using a computer network, and so on. Likewise, a textbook on marketing normally describes various aspects of marketing, such as market segmentation, market targeting, positioning, differentiation, etc. Some of the knowledge is factoid that can be used to concisely and definitely answer a knowledge query, such as the various types of a computer network, while others are non-factoid that can be used to answer open-ended questions, such as the knowledge for answer questions like “what are the effects of different ways of market segmentation”?

The vast amount of knowledge in these corpora can be helpful in making decisions such as the network deployment strategy or marketing strategy. However, automatically extracting knowledge from these corpora, especially for non-factoid knowledge, is challenging. This is because, for example, entities mentioned in the corpora, such as noun-phrases in the corpora, do not have a taxonomy and the typical corpora are not tightly worded leading to non-informative content. Existing methods are insufficient because knowledge graphs built by the existing methods are for factoid knowledge with a taxonomy. As a result, these knowledge graphs cannot be used to build knowledge graphs for non-factoid knowledge or general world knowledge that contains both factoid and non-factoid knowledge.

SUMMARY

Certain embodiments involve generating a knowledge graph by analyzing text-based knowledge corpora and determining relations between entities mentioned in the knowledge corpora. In one example, a knowledge graph generation system extracts a set of sentences from a text-based knowledge corpus and determines relations between noun-phrases for each of the sentences based on dependency paths of the sentence and a relation classifier configured to predict a relation between a pair of noun-phrases. The knowledge graph generation system further generates a sub-graph for each of the sentences based on the relations between the noun-phrases in each sentence. The sub-graph includes entities representing the noun-phrases in the sentence and edges connecting the entities. Each edge represents the relation between two noun-phrases connected by the edge. The knowledge graph generation system generates a knowledge graph by merging the sub-graphs for the set of sentences. The knowledge graph generation system or another system identifies one or more entities or one or more relations from the knowledge graph based on a knowledge request and transmits the one or more entities or one or more relations to an online platform. The one or more entities or one or more relations are usable for modifying the online platform.

These illustrative embodiments are mentioned not to limit or define the disclosure, but to provide examples to aid understanding thereof. Additional embodiments are discussed in the Detailed Description, and further description is provided there.

