Incremental Knowledge Acquisition Approach for Information Extraction on both Semi-Structured and Unstructured Text from the Open Domain Web

Incremental Knowledge Acquisition Approach for Information Extraction on both Semi-structured and Unstructured Text from the Open Domain Web Maria Myung Hee Kim Defence Science Technology Group Edinburgh, SA 5111, Australia maria.kim@dst.defence.gov.au Abstract Extracting information from semi- structured text has been studied only for limited domain sources due to its hetero- geneous formats. This paper proposes a Ripple-Down Rules (RDR) based ap- proach to extract relations from both semi- structured and unstructured text in open domain Web pages. We find that RDR's 'case-by-case' incremental knowledge ac- quisition approach provides practical flex- ibility for (1) handling heterogeneous for- mats of semi-structured text; (2) conduct- ing knowledge engineering on any Web pages with minimum start-up cost and (3) allowing open-ended settings on relation schema. The efficacy of the approach has been demonstrated by extracting contact information from randomly collected open domain Web pages. The rGALA system achieved 0.87 F1 score on a testing dataset of 100 Web pages, after only 7 hours of knowledge engineering on a training set of 100 Web pages. 1 Introduction Open Information Extraction (Open IE) (Banko et al., 2007; Wu and Weld, 2010; Fader et al., 2011) was introduced to extract information from the open domain Web where the relations of in- terest cannot be pre-defined in advance due to its heterogeneity in domain. Its purpose is to avoid specifying target relations and developing extrac- tion models for individual target relations. The Open IE systems focus on discovering a binary relation candidate tuple in the form of (E1, RelText, E2) by identifying two entities of inter- est E1 and E2, and the salient textual cues RelText (aka 'relational text') between the two entities. Then, they classify whether any binary relation R exists between the two entities in a given tuple to extract a binary relation tuple like (E1, R, E2). To date extracting information from the open domain Web has mainly focused on the unstruc- tured text (i.e., where text is formatted in para- graphs and expressed as full sentences). Howev- er, most Web pages generally contain infor- mation expressed in semi-structured text includ- ing tables, lists, isolated words or text snippets as well as unstructured text. Therefore, it is im- portant to develop an IE capability that is able to process both semi-structured and unstructured text from the open domain Web. Unlike unstructured text, semi-structured text usually includes HTML tags, which is primarily for formatting purposes. The variability in the way people use HTML tags impedes IE process. Above all, HTML tags have the following char- acteristics which hinder the IE task: (1) HTML's tabular structure is often abused to arrange the graphical aspect instead of using cas- cading style sheets; and (2) HTML tags can be deeply nested mixing rel- evant content with web noise in a loose manner. Processing semi-structured text from the open domain Web is very challenging task as it needs to deal with heterogeneous formats as well as heterogeneous domains. It is difficult to create sufficient labelled data for semi-structured text in …

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