User driven Information Extraction with LODIE

Abstract

Information Extraction (IE) is the technique for transforming unstructured or semi-structured data into structured representation that can be understood by machines. In this paper we use a user-driven Information Extraction technique to wrap entity-centric Web pages. The user can select concepts and properties of interest from available Linked Data. Given a number of websites containing pages about the concepts of interest, the method will exploit (i) recurrent structures in the Web pages and (ii) available knowledge in Linked data to extract the information of interest from the Web pages.

Introduction

rules to previously unseen websites of the same domain [ 9,10 ]. Completely unsupervised methods (e.g. RoadRunner [ 11 ] and EXALG [ 12 ]) do not require any training data, nor an initial extraction template (indicating which concepts and attributes to extract), and they only assume the homogeneity of the considered pages. The drawback of unsupervised methods is that the semantic of produced results is left as a post-process to the user. Hybrid methods [ 2 ] intend to nd a tradeo with these two limitations by proposing a supervised strategy, where the training data is automatically generated exploiting Linked Data. In this work we perform IE using the method proposed in [ 2,3 ] and follow the general IE paradigm from [ 4 ]. 3

User-driven Information Extraction

In LODIE we adopt a user driven paradigm for IE. As rst step, the user must de ne her/his information need. This is done via a visual exploration of linked data (Figure 1).

The user can explore underlying linked data using the A ective Graphs visualization tool [ 13 ] and select concepts and properties she/he is interested in (a screenshot is shown in Figure 1). These concepts and properties get added to the side panel. Once the selection is nished, she/he can start the IE process. The IE starts with a dictionary generation phase. A dictionary di;k consists of values for the attribute ai;k of instances of concept ci. Noisy entries in the dictionaries are removed using a cleaning procedure detailed in [ 3 ]. As a running example we will assume the user wants to extract title and author for the concept Book. We retrieve from the Web k websites containing entity-pages of the concept types selected by the user, and save the pages Wci;k. Following the Book example, Barnes&Noble2 or AbeBooks3 websites can be used, and pages collected in Wbook;barnesandnoble and Wbook;abebooks.

For each Wci;k we generate a set of extraction patterns for every attribute. In our example we will produce 4 sets of patterns, one per each website and 2 http://www.barnesandnoble.com/ 3 http://www.abebooks.co.uk attribute. To produce the patterns we (i) use our dictionaries to generate bruteforce annotations on the pages in Wci;k and then (ii) use statistical (occurrence frequency) and structural (position of the annotations in the webpage) clues to choose the nal extraction patterns.

Brie y, a page is transformed to a simpli ed page representation Pci : a collection of pairs 〈xpath4, text value〉. Candidates are generated matching the dictionaries di;k against possible text values in Pci (Figure 2). /HTML[ 1 ]/BODY[ 1 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 1 ]/H2[ 1 ]/text()[ 1 ] breaking dawn /HTML[ 1 ]/BODY[ 1 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 4 ]/DIV[ 1 ]/H2[ 1 ]/EM[ 1 ]/text()[ 1 ] breaking dawn /HTML[ 1 ]/BODY[ 1 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 4 ]/TABLE[ 10 ]/TBODY[ 1 ]/TR[ 1 ]/TD[ 3 ]/B[ 1 ]/A[ 1 ]/text()[ 1 ] breaking dawn /HTML[ 1 ]/BODY[ 1 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 4 ]/TABLE[ 1 ]/TBODY[ 1 ]/TR[ 1 ]/TD[ 3 ]/B[ 1 ]/A[ 1 ]/text()[ 1 ] breaking dawn /HTML[ 1 ]/BODY[ 1 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 4 ]/TABLE[ 2 ]/TBODY[ 1 ]/TR[ 1 ]/TD[ 3 ]/B[ 1 ]/A[ 1 ]/text()[ 1 ] breaking dawn /HTML[ 1 ]/BODY[ 1 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 4 ]/TABLE[ 3 ]/TBODY[ 1 ]/TR[ 1 ]/TD[ 3 ]/B[ 1 ]/A[ 1 ]/text()[ 1 ] breaking dawn /HTML[ 1 ]/BODY[ 1 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 4 ]/TABLE[ 6 ]/TBODY[ 1 ]/TR[ 1 ]/TD[ 3 ]/B[ 1 ]/A[ 1 ]/text()[ 1 ] breaking dawn /HTML[ 1 ]/BODY[ 1 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 4 ]/TABLE[ 8 ]/TBODY[ 1 ]/TR[ 1 ]/TD[ 3 ]/B[ 1 ]/A[ 1 ]/text()[ 1 ] breaking dawn /HTML[ 1 ]/BODY[ 1 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 3 ]/DIV[ 3 ]/UL[ 1 ]/LI[ 2 ]/A[ 1 ]/text()[ 1 ] the host /HTML[ 1 ]/BODY[ 1 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 3 ]/DIV[ 3 ]/UL[ 1 ]/LI[ 5 ]/A[ 1 ]/text()[ 1 ] new moon Fig. 2: Example of candidates for book title for a Web page on the book \Breaking Dawn", from the website AbeBooks.

Final patterns are chosen amongst the candidates exploiting frequency information and other heuristics. Details of the method can be found in [ 2,3 ]. In the running example, higher scoring patterns for extracting book title from AbeBooks website are shown in Figure 3. /HTML[ 1 ]/BODY[ 1 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 1 ]/H2[ 1 ]/text()[ 1 ] 329.0 /HTML[ 1 ]/BODY[ 1 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 2 ]/DIV[ 4 ]/DIV[ 1 ]/H2[ 1 ]/EM[ 1 ]/text()[ 1 ] 329.0

Fig. 3: Extraction patterns for book titles from AbeBooks website.

All extraction patterns are then used to extract target values from all Wci;k. Results are produced as linked data, using the concept and properties initially selected by the user for representation, and made accessible to the user via an exploration interface (Figure 4), implemented using Simile Widgets5.

A video showing the proposed system used with the running Book example can be found at http://staffwww.dcs.shef.ac.uk/people/A.L.Gentile/ demo/iswc2014.html.

4 Conclusions and future work

In this paper we describe the LODIE approach to perform IE on user de ned extraction tasks. The user is prompted a visual tool to explore available linked data and choose concepts for which she/he wants to mine additional material from the Web. We learn extraction patterns to wrap relevant websites and return structured results to the user. 4 http://www.w3.org/TR/xpath/ 5 http://www.simile-widgets.org/

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