Fuzzy Annotation of Web Data Tables Driven by a Domain Ontology

Hignette, Gaëlle; Buche, Patrice; Dibie-Barthelemy, Juliette; Haemmerlé, Ollivier

doi:10.1007/978-3-642-02121-3_47

Cited by 24 publications

(45 citation statements)

References 13 publications

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“…Hignette et al [14,15] and Buche et al [3] propose methods to identify concepts represented by table columns and detect relations present in tables in a domain-specific context. An NE-column is annotated based on two factors: similarity between the header text of the column and the name of a candidate concept; plus the similarities calculated for each cell in the column and each term in the hierarchical paths containing the candidate concept.…”

Section: Semantic Table Interpretationmentioning

confidence: 99%

“…This is done by using simple regular expressions that examine the syntactic features of cell text, such as number of words, capitalization, mentions of months or days in a week. A vast amount of literature can be found on this topic [3,14,15]. Then each column is assigned a most frequent datatype by counting the number of cells belonging to that type.…”

Section: Preprocessingmentioning

confidence: 99%

“…On the other hand, search engines typically ignore underlying semantics of such structures at indexing, hence performing poorly on tabular data [21,26]. Research directed to this particular problem is Semantic Table Interpretation [14,15,21,27,34,25,35,36,3,26,23], which deals with three types of annotation tasks in tables. Starting with the input of a well-formed relational table (e.g., Figure 1), and reference sets of concepts (or classes, types), named entities (or simply 'entities') and relations, semantic table interpretation aims to: (1) link entity men-tions in content cells (or simply 'cells') to the reference entities (disambiguation); (2) annotate columns with semantic concepts if they contain entity mentions (NE-columns), or properties of concepts if they contain data literals (literal-columns); and (3) identify the semantic relations between columns.…”

Section: Introductionmentioning

confidence: 99%

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Effective and efficient Semantic Table Interpretation using TableMiner+

Zhang

2017

101

125

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Abstract. This article introduces TableMiner + , a Semantic Table Interpretation method that annotates Web tables in a both effective and efficient way. Built on our previous work TableMiner, the extended version advances state-of-the-art in several ways. First, it improves annotation accuracy by making innovative use of various types of contextual information both inside and outside tables as features for inference. Second, it reduces computational overheads by adopting an incremental, bootstrapping approach that starts by creating preliminary and partial annotations of a table using 'sample' data in the table, then using the outcome as 'seed' to guide interpretation of remaining contents. This is then followed by a message passing process that iteratively refines results on the entire table to create the final optimal annotations. Third, it is able to handle all annotation tasks of Semantic Table Interpretation (e.g., annotating a column, or entity cells) while state-of-the-art methods are limited in different ways. We also compile the largest dataset known to date and extensively evaluate TableMiner + against four baselines and two reimplemented (near-identical, as adaptations are needed due to the use of different knowledge bases) state-of-the-art methods. TableMiner + consistently outperforms all models under all experimental settings. On the two most diverse datasets covering multiple domains and various table schemata, it achieves improvement in F1 by between 1 and 42 percentage points depending on specific annotation tasks. It also significantly reduces computational overheads in terms of wall-clock time when compared against classic methods that 'exhaustively' process the entire table content to build features for inference. As a concrete example, compared against a method based on joint inference implemented with parallel computation, the non-parallel implementation of TableMiner + achieves significant improvement in learning accuracy and almost orders of magnitude of savings in wall-clock time.

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Section: Semantic Table Interpretationmentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effective and efficient Semantic Table Interpretation using TableMiner+

Zhang

2017

101

125

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“…[23] utilized search engines to discover and annotate entities in Web tables. Hignette [15] annotated tables with ontology concepts based on relevance degrees. Wang et.…”

Section: Related Workmentioning

confidence: 99%

Scalable column concept determination for web tables using large knowledge bases

Deng

Jiang

et al. 2013

Proc. VLDB Endow.

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Tabular data on the Web has become a rich source of structured data that is useful for ordinary users to explore. Due to its potential, tables on the Web have recently attracted a number of studies with the goals of understanding the semantics of those Web tables and providing effective search and exploration mechanisms over them. An important part of table understanding and search is column concept determination, i.e., identifying the most appropriate concepts associated with the columns of the tables. The problem becomes especially challenging with the availability of increasingly rich knowledge bases that contain hundreds of millions of entities.In this paper, we focus on an important instantiation of the column concept determination problem, namely, the concepts of a column are determined by fuzzy matching its cell values to the entities within a large knowledge base. We provide an efficient and scalable MapReduce-based solution that is scalable to both the number of tables and the size of the knowledge base and propose two novel techniques: knowledge concept aggregation and knowledge entity partition. We prove that both the problem of finding the optimal aggregation strategy and that of finding the optimal partition strategy are NP-hard, and propose efficient heuristic techniques by leveraging the hierarchy of the knowledge base. Experimental results on real-world datasets show that our method achieves high annotation quality and performance, and scales well.

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“…The current version of @Web (see [4,5]), a Webenabled data warehouse, has been implemented using the W3C recommended languages (see [6] for details about these languages): OWL to represent the domain ontology, RDF to annotate Web tables and SPARQL to query annotated Web tables.…”

Section: Introductionmentioning

confidence: 99%

Data Reliability Assessment in a Data Warehouse Opened on the Web

Destercke

Buche

Charnomordic

2011

Flexible Query Answering Systems

Self Cite

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Abstract. This paper presents an ontology-driven workflow that feeds and queries a data warehouse opened on the Web. Data are extracted from data tables in Web documents. As web documents are very heterogeneous in nature, a key issue in this workflow is the ability to assess the reliability of retrieved data. We first recall the main steps of our method to annotate and query Web data tables driven by a domain ontology. Then we propose an original method to assess Web data table reliability from a set of criteria by the means of evidence theory. Finally, we show how we extend the workflow to integrate the reliability assessment step.

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Fuzzy Annotation of Web Data Tables Driven by a Domain Ontology

Cited by 24 publications

References 13 publications

Effective and efficient Semantic Table Interpretation using TableMiner+

Effective and efficient Semantic Table Interpretation using TableMiner+

Scalable column concept determination for web tables using large knowledge bases

Data Reliability Assessment in a Data Warehouse Opened on the Web

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