The expression information in the web
The following is a list of expressive information in the web information.
- Schedule and event information: Calendar and event pages on the web display dates, times, locations, and event titles and descriptions in tabular form, and such information is usually presented in row and column format, with each row representing an individual event or schedule entry.
- Product listings and price lists: On online shopping sites and company websites, product listings and price lists are displayed in tabular form, where each row represents a product or service and the columns are represented in such a way that each row contains information such as product name, description, price, and availability.
- Database search results: Database search results on the Web are typically presented in a tabular format, with the data extracted based on the search query presented in row and column format. Examples include online library catalogs and business listings.
- Statistics and reports: Survey results, statistical data, and reports are also displayed in tabular form. Tables are useful for comparing values of different categories or elements, and information such as numbers, percentages, and dates are commonly presented in tables.
While these are common examples, there are many other types of tabular information on the Web and for many other purposes. These information can be extracted by web scraping and data extraction as described in “Overview of web crawling technology and its implementation in Python/Clojure” to identify the table information and extract the necessary data according to the purpose.
Next, we will discuss the issues involved in utilizing these data.
Challenges in Extracting and Utilizing Tabular Data from Web Information
The following issues exist when extracting and utilizing tabular data from Web information.
- Data integrity and reliability: Information on the Web may be collected from multiple sources. Therefore, the integrity and reliability of the extracted data must be ensured, and it is necessary to carefully verify that the information is accurate and up-to-date, and that the data sources are reliable.
- Variety of data structures: Information on the Web may be represented in a variety of formats and structures. In extracting data, flexible algorithms and methods that can adapt to different web page structures are needed, and extraction rules and data processing must be flexible enough to accommodate multiple web sites and data sources.
- Page changes and updates: The design and structure of web pages can change frequently, and the data extraction process needs to be flexible enough to accommodate changes in web pages. It is important to continuously maintain the data extraction program with regular monitoring and updates.
- Data preprocessing and normalization: Extracted data are often incomplete and contain noise. To correct for these, data preprocessing and normalization should be performed to format the necessary information and maintain consistency. In addition, efforts need to be made to improve data quality by handling missing values, converting data types, and eliminating duplicate data.
- Copyright and legal restrictions: Web scraping and data extraction must comply with the terms of use and legal restrictions of the website. Therefore, permission must be obtained from the website owner/operator or a legitimate data source must be used, and legal restrictions and ethical considerations must be kept in mind when extracting and using data.
Thus, in order to extract and utilize tabular data from the web, it is necessary to deal with data integrity and reliability, as well as the diversity of data and web page structures, all of which are difficult to do automatically.
In the following, we summarize information from a workshop at the International Society for Web Content Creation (ISWC), where approaches to these issues were discussed.
Extraction of tabular data from the Web and documents and semantic annotation (SemTab) learning
There are countless table information on the Web and in documents, which can be very useful as knowledge information compiled manually. In general, the task of extracting and structuring such information is called an information extraction task. Among them, a task specialized for recent table information has been attracting attention, and workshops have been held at international conferences (ISWC, etc.).
The technology used there is not only the conventional string matching technology, but also the table locality function learned by hybrid neural networks (HNN), the column-to-column semantics function learned by knowledge bases (KB), and other machines such as detailed prediction models that can fully utilize the context semantics of the table. Approaches using machine learning, such as detailed predictive models that can take advantage of this information, are being taken.
Tabular data are also associated with relational databases, and ontology matching and schema matching techniques applied to them, combined with knowledge information, or combined with probabilistic approaches (probabilistic relational models), etc. The following is a list of these approaches.
The following is a description of the contents of the ISWC SemTab report.
The usefulness of tabular data, such as Web tables, is highly dependent on understanding their semantics. In this study, we focus on predicting column types of tables without metadata. Unlike traditional lexical matching-based methods, deep prediction that can fully exploit the context semantics of tables, including table locality features learned by Hybrid Neural Networks (HNN) and column-to-column semantics features learned by a knowledge base Proposed model (KB ) This http URL responsive lookup and query performs well not only on individual table sets, but also when transferring from one table set to another.
This paper introduces the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2020), a system called MTab4Wikidata for three semantic annotation tasks: cell-entity annotation (CEA), column type annotation (CTA), and column relation- Property Annotation (CPA), and a system called MTab4Wikidata (MTab4Wikidata). In particular, we introduce (1) fuzzy entity retrieval for misspellings in table cells, (2) fuzzy statement retrieval for ambiguous cells, (3) a statement enrichment module for Wikidata shift problems, and (4) efficient and effective post-processing for matching tasks. Introduction. The system achieved excellent empirical performance on three annotation tasks and won the top award at SemTab 2020: MTab4Wikidata won first place in two tasks, CEA and CPA, and second place in the CTA task. It won first place in the task, second place in the Round 1, 2, and 3 datasets, and first place in the Round 4 dataset. and first place in the Tough Tables (2T) data set.
This paper presents a new approach used in the DAGOBAH system, which performs three tasks by semantically annotating tables with Wikidata entities and relations. Column-Property Annotation (CPA), Cell-Entity Annotation (CEA), and Column-Type Annotation (CTA). In this system, the initial score of disambiguation affects the output of the CPA, which in turn affects the output of the CEA. Finally, the CTA is computed using the type hierarchy of the knowledge graph, and the column is assigned the optimal fine-grained type. This approach using interactions between annotations allowed DAGOBAH to be very competitive in all tasks of the SemTab2020 Challenge.
This paper presents a novel unsupervised automated approach for semantic table interpretation. The method is performed on DBpedia and Wikidata and can be easily applied to other knowledge graphs (KGs). In addition, a tool (LamAPI) is provided to efficiently obtain the data needed for semantic table interpretation (STI). Provide a tool (LamAPI) to efficiently retrieve data required for table interpretation (STI) tasks from KG dumps.
Much information is conveyed in tables and can be semantically annotated by human or (semi-)automatic approaches. Nevertheless, many applications do not take full advantage of semantic annotation due to its low quality. Several methodologies exist for assessing the quality of semantic annotation of tabular data, but they do not automatically assess quality as a multidimensional concept through various quality dimensions. The quality dimensions are implemented in STILTool 2, a web application that automates the quality assessment of annotations. The evaluation is done by comparing the quality of semantic annotations to a gold standard. The work presented here has been applied to at least three use cases. The results show that our approach gives hints about quality issues and how to deal with them.
The purpose of this assignment is to benchmark systems that handle the problem of matching tabular data with KGs to facilitate comparison and reproducibility of results on the same criteria. There is a discussion group to share the latest news on this assignment.
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