Information extraction meets the Semantic Web: A survey

Martínez-Rodríguez, José-Lázaro; Hogan, Aidan; López-Arévalo, Iván

doi:10.3233/sw-180333

Cited by 131 publications

(82 citation statements)

References 241 publications

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“…Figure 7 shows spatial entities (in blue), spatial concepts (in green), and relations (underlined) that may be extracted from a small passage. Semantic information extraction aims at eliciting salient, specific types of information from unstructured or semi-structured data sources [105]. Entities, concepts, and/or semantic relations that are implicit in a given source are made explicit to support semantic annotation, content-based exploration, semantic search, and data-driven geographic analysis.…”

Section: Geospatial Semantic Information Extraction and Enrichmentmentioning

confidence: 99%

“…For example, in Figure 2, Maldives and Tuvalu are named spatial entities. Named entity recognition (NER) [105] is used to identify named entity mentions in text and classify them in pre-defined categories. Gazetteer lists that provide words or phrases representing individual instances of a specific category (e.g., location, time, and organization) are widely used in NER tasks.…”

Section: Geospatial Semantic Information Extraction and Enrichmentmentioning

confidence: 99%

“…Gazetteer lists that provide words or phrases representing individual instances of a specific category (e.g., location, time, and organization) are widely used in NER tasks. Named entity disambiguation (NED) [105] is used to determine the identity of a named entity mention and link it to a unique entity in a knowledge base. NED is crucial when the same entity mention may refer to different instances, e.g., the entity mention "Washington" may refer to different real-world entities such as George Washington, the State of Washington, or Washington D.C.…”

Section: Geospatial Semantic Information Extraction and Enrichmentmentioning

confidence: 99%

“…Concept extraction techniques are used to identify words or phrases that denote semantic classes, such as the concepts: "atoll", "coastal area", and "island" in Figure 2. Concept extraction involves related processes such as term extraction, keyword/key phrase extraction, and topic modeling [105]. These processes are applied on a text collection, which usually describes one or several domains.…”

Section: Geospatial Semantic Information Extraction and Enrichmentmentioning

confidence: 99%

See 3 more Smart Citations

A Review of Geospatial Semantic Information Modeling and Elicitation Approaches

Kokla

Guilbert

2020

IJGI

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The present paper provides a review of two research topics that are central to geospatial semantics: information modeling and elicitation. The first topic deals with the development of ontologies at different levels of generality and formality, tailored to various needs and uses. The second topic involves a set of processes that aim to draw out latent knowledge from unstructured or semi-structured content: semantic-based extraction, enrichment, search, and analysis. These processes focus on eliciting a structured representation of information in various forms such as: semantic metadata, links to ontology concepts, a collection of topics, etc. The paper reviews the progress made over the last five years in these two very active areas of research. It discusses the problems and the challenges faced, highlights the types of semantic information formalized and extracted, as well as the methodologies and tools used, and identifies directions for future research.

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Section: Geospatial Semantic Information Extraction and Enrichmentmentioning

confidence: 99%

Section: Geospatial Semantic Information Extraction and Enrichmentmentioning

confidence: 99%

Section: Geospatial Semantic Information Extraction and Enrichmentmentioning

confidence: 99%

Section: Geospatial Semantic Information Extraction and Enrichmentmentioning

confidence: 99%

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A Review of Geospatial Semantic Information Modeling and Elicitation Approaches

Kokla

Guilbert

2020

IJGI

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“…Knowledge graphs (KGs) [1] are introduced to organize the knowledge of natural language descriptions in a way that can be processed by computers. KGs link the semantic content of entities into a network, namely Semantic Web [2], that facilitates knowledge interoperability. This semantic content describes the entity's attributes in the form of the object-attribute-value triple [3], commonly written as A (O, V).…”

Section: Introductionmentioning

confidence: 99%

A Knowledge-Based Filtering Method for Open Relations among Geo-Entities

Qiu

Gao

et al. 2019

IJGI

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Knowledge graphs (KGs) are crucial resources for supporting geographical knowledge services. Given the vast geographical knowledge in web text, extraction of geo-entity relations from web text has become the core technology for construction of geographical KGs; furthermore, it directly affects the quality of geographical knowledge services. However, web text inevitably contains noise and geographical knowledge can be sparsely distributed, both of which greatly restrict the quality of geo-entity relationship extraction. We propose a method for filtering geo-entity relations based on existing knowledge bases (KBs). Accordingly, ontology knowledge, fact knowledge, and synonym knowledge are integrated to generate geo-related knowledge. Then, the extracted geo-entity relationships and the geo-related knowledge are transferred into vectors, and the maximum similarity between vectors is the confidence value of one extracted geo-entity relationship triple. Our method takes full advantage of existing KBs to assess the quality of geographical information in web text, which is helpful to improve the richness and freshness of geographical KGs. Compared with the Stanford OpenIE method, our method decreased the mean square error (MSE) from 0.62 to 0.06 in the confidence interval [0.7, 1], and improved the area under the receiver operating characteristic (ROC) curve (AUC) from 0.51 to 0.89.

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Table understanding: Problem overview

Shigarov

2022

WIREs Data Min & Knowl

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Tables are probably the most natural way to represent relational data in various media and formats. They store a large number of valuable facts that could be utilized for question answering, knowledge base population, natural language generation, and other applications. However, many tables are not accompanied by semantics for the automatic interpretation of the information they present. Table Understanding (TU) aims at recovering the missing semantics that enables the extraction of facts from tables. This problem covers a range of issues from table detection in document images to semantic table interpretation with the help of external knowledge bases. To date, the TU research has been ongoing on for 30 years. Nevertheless, there is no common point of view on the scope of TU; the terminology still needs agreement and unification. In recent years, science and technology have shown a rapidly increasing interest in TU. Nowadays, it is especially important to check the meaning of this research problem once again. This article gives a comprehensive characterization of the TU problem, including a description of its subproblems, tasks, subtasks, and applications. It also discusses the common limitations used in the existing problem statements and proposes some directions for further research that would help overcome the corresponding limitations. This article is categorized under: Algorithmic Development > Text Mining Algorithmic Development > Web Mining

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Information extraction meets the Semantic Web: A survey

Cited by 131 publications

References 241 publications

A Review of Geospatial Semantic Information Modeling and Elicitation Approaches

A Review of Geospatial Semantic Information Modeling and Elicitation Approaches

A Knowledge-Based Filtering Method for Open Relations among Geo-Entities

Table understanding: Problem overview

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