OpenIE-based approach for Knowledge Graph construction from text

Martínez-Rodríguez, José-Lázaro; López-Arévalo, Iván; Ríos-Alvarado, Ana B.

doi:10.1016/j.eswa.2018.07.017

Cited by 93 publications

(45 citation statements)

References 16 publications

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“…In many of the proposed solutions, IE relies on Part-Of-Speech (PoS) tagging and various type of patterns, morphological or syntactical [28,22], often complementing themselves to compensate for reduced coverage. The most recent approaches exploit various resources to develop ensemble methodologies [18]. If we consider IE as the combination of two main tasks, extracting entities and identifying relations from text, the latter has proven without doubt the most challenging.…”

Section: Related Workmentioning

confidence: 99%

“…The AI-KG ontology is available online 17 and builds on SKOS 18 , PROV-O 19 , and OWL 20 . Each statement in AI-KG is associated with a triple describing the relationship between two entities and a number of relevant metadata.…”

Section: Ai-kg Overviewmentioning

confidence: 99%

“…The recent advancements in deep learning architectures have fostered the emergence of several excellent tools that extract information from research publications with a fair accuracy [4,11,18,16]. However, integrating the output of these tools in a coherent and comprehensive knowledge graph is still an open challenge.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

AI-KG: An Automatically Generated Knowledge Graph of Artificial Intelligence

Dessı̀

Osborne

Recupero

et al. 2020

Lecture Notes in Computer Science

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Scientific knowledge has been traditionally disseminated and preserved through research articles published in journals, conference proceedings, and online archives. However, this article-centric paradigm has been often criticized for not allowing to automatically process, categorize, and reason on this knowledge. An alternative vision is to generate a semantically rich and interlinked description of the content of research publications. In this paper, we present the Artificial Intelligence Knowledge Graph (AI-KG), a large-scale automatically generated knowledge graph that describes 820K research entities. AI-KG includes about 14M RDF triples and 1.2M reified statements extracted from 333K research publications in the field of AI, and describes 5 types of entities (tasks, methods, metrics, materials, others) linked by 27 relations. AI-KG has been designed to support a variety of intelligent services for analyzing and making sense of research dynamics, supporting researchers in their daily job, and helping to inform decision-making in funding bodies and research policymakers. AI-KG has been generated by applying an automatic pipeline that extracts entities and relationships using three tools: DyGIE++, Stanford CoreNLP, and the CSO Classifier. It then integrates and filters the resulting triples using a combination of deep learning and semantic technologies in order to produce a high-quality knowledge graph. This pipeline was evaluated on a manually crafted gold standard, yielding competitive results. AI-KG is available under CC BY 4.0 and can be downloaded as a dump or queried via a SPARQL endpoint.

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Section: Related Workmentioning

confidence: 99%

Section: Ai-kg Overviewmentioning

confidence: 99%

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AI-KG: An Automatically Generated Knowledge Graph of Artificial Intelligence

Dessı̀

Osborne

Recupero

et al. 2020

Lecture Notes in Computer Science

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“…RDF store allows storing triples and SPARQL queries are used to retrieve them [7]. Many data publishers are relied on the benefits of the Semantic web for quick publishing, processing the data by machines and parsing [8]. Many techniques tend to find the relevant answer to the user query based on semantic web.…”

Section: Introductionmentioning

confidence: 99%

A Query Search Technique for Semantic Web Based on The Dynamic Distribution of Backjumping Method

2019

ijeat

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In the resource description language of the semantic web, vagueness of Resource Description Framework (RDF) data is playing an important role. The effective querying of the RDF data is increasing importance in semantic web. In this research, the Dynamic Distribution of BackJumping (DDBJ) algorithm is proposed in the fuzzy graph due to the ability of the algorithm to maintain its autonomy in the data. The fuzzy graph is generated from the triplets in the RDF and the vertices, edges are extracted from pattern matching techniques. The vertices and edges are applied in the DDBJ to suggest the query in the semantic web. To analyze the effectiveness of the proposed DDBJ, the two real datasets are used. The proposed DDBJ method has the f-measure of 59 %, while the state-of-art method such as backtracking has achieved 56 %. The result shows that the DDBJ method has the higher performance than existing method in query processing.

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“…In the other part, we import a definition for each concept in OMRKBS. First, this program pre-processes a definition to turn the long text into features using the OpenIE [22] and some rules. Then, the program discovers each word in the text as a concept in the system and creates a mapping expression to embed the features.…”

mentioning

confidence: 99%

Comprehensive structured knowledge base system construction with natural language presentation

Khanam

Liu

Chen

2019

Hum. Cent. Comput. Inf. Sci.

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IntroductionMachine readable knowledge bases are used to store datasets so that these datasets can be accessible through systems. Machine-readable knowledge base construction (MRKBC) involves the automated extraction and integration of data from different sources and generating meaningful information with interoperable knowledge [1]. There is a large body of research on the automatic extraction of information for MRKBC. Initially, the research focused on syntactic information extraction [2, 3], but more recently, the extraction of lexical semantic information has received more interest from the research community [4,5]. Knowledge base systems (KBS) which use traditional databases are not effective due to the limited operational and analytical workload and AbstractConstructing an ontology-based machine-readable knowledge base system from different sources with minimum human intervention, also known as ontology-based machine-readable knowledge base construction (OMRKBC), has been a long-term outstanding problem. One of the issues is how to build a large-scale OMRKBC process with appropriate structural information. To address this issue, we propose Natural Language Independent Knowledge Representation (NLIKR), a method which regards each word as a concept which should be defined by its relations with other concepts. Using NLIKR, we propose a framework for the OMRKBC process to automatically develop a comprehensive ontology-based machine-readable knowledge base system (OMRKBS) using well-built structural information. Firstly, as part of this framework, we propose formulas to discover concepts and their relations in the OMRKBS. Secondly, the challenges in obtaining rich structured information are resolved through the development of algorithms and rules. Finally, rich structured information is built in the OMRKBS. OMRKBC allows the efficient search of words and supports word queries with a specific attribute. We conduct experiments and analyze the results of relational information extraction, with the results showing that OMRKBS had an accuracy of 84% which was higher than the other knowledge base systems, namely ConceptNet, DBpedia and WordNet.

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OpenIE-based approach for Knowledge Graph construction from text

Cited by 93 publications

References 16 publications

AI-KG: An Automatically Generated Knowledge Graph of Artificial Intelligence

AI-KG: An Automatically Generated Knowledge Graph of Artificial Intelligence

A Query Search Technique for Semantic Web Based on The Dynamic Distribution of Backjumping Method

Comprehensive structured knowledge base system construction with natural language presentation

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