Cross-lingual entity matching and infobox alignment in Wikipedia

Wikipedia has grown to a huge, multi-lingual source of encyclopedic knowledge. Apart from textual content, a large and everincreasing number of articles feature so-called infoboxes, which provide factual information about the articles’ subjects. As the different language versions evolve independently, they provide different information on the same topics. Correspondences between infobox attributes in different language editions can be leveraged for several use cases, such as automatic detection and resolution of inconsistencies in infobox data across language versions, or the automatic augmentation of infoboxes in one language with data from other language versions. We present an instance-based schema matching technique that exploits information overlap in infoboxes across different language editions. As a prerequisite we present a graph-based approach to identify articles in different languages representing the same real-world entity using (and correcting) the interlanguage links in Wikipedia. To account for the untyped nature of infobox schemas, we present a robust similarity measure that can reliably quantify the similarity of strings with mixed types of data. The qualitative evaluation on the basis of manually labeled attribute correspondences between infoboxes in four of the largest Wikipedia editions demonstrates the effectiveness of the proposed approach. 1. Entity and Attribute Matching across Wikipedia Languages Wikipedia is a well-known public encyclopedia. While most of the information contained in Wikipedia is in textual form, the so-called infoboxes provide semi-structured, factual information. They are displayed as tables in many Wikipedia articles and state basic facts about the subject. There are different templates for infoboxes, each targeting a specific category of articles and providing fields for properties that are relevant for the respective subject type. For example, in the English Wikipedia, there is a class of infoboxes about companies, one to describe the fundamental facts about countries (such as their capital and population), one for musical artists, etc. However, each of the currently 281 language versions1 defines and maintains its own set of infobox classes with their own set of properties, as well as providing sometimes different values for corresponding attributes. Figure 1 shows extracts of the English and German infoboxes for the city of Berlin. The arrows indicate matches between properties. It is already apparent that matching purely based on property names is futile: The terms Population density and Bevölkerungsdichte or Governing parties and Reg. Parteien have no textual similarity. However, their property values are more revealing: <3,857.6/km2> and <3.875 Einw. je km2> or <SPD/Die Linke> and <SPD und Die Linke> have a high textual similarity, respectively. Email addresses: daniel.rinser@alumni.hpi.uni-potsdam.de (Daniel Rinser), dustin.lange@hpi.uni-potsdam.de (Dustin Lange), naumann@hpi.uni-potsdam.de (Felix Naumann) 1as of March 2011 Our overall goal is to automatically find a mapping between attributes of infobox templates across different language versions. Such a mapping can be valuable for several different use cases: First, it can be used to increase the information quality and quantity in Wikipedia infoboxes, or at least help the Wikipedia communities to do so. Inconsistencies among the data provided by different editions for corresponding attributes could be detected automatically. For example, the infobox in the English article about Germany claims that the population is 81,799,600, while the German article specifies a value of 81,768,000 for the same country. Detecting such conflicts can help the Wikipedia communities to increase consistency and information quality across language versions. Further, the detected inconsistencies could be resolved automatically by fusing the data in infoboxes, as proposed by . Finally, the coverage of information in infoboxes could be increased significantly by completing missing attribute values in one Wikipedia edition with data found in other editions. An infobox template does not describe a strict schema, so that we need to collect the infobox template attributes from the template instances. For the purpose of this paper, an infobox template is determined by the set of attributes that are mentioned in any article that reference the template. The task of matching attributes of corresponding infoboxes across language versions is a specific application of schema matching. Automatic schema matching is a highly researched topic and numerous different approaches have been developed for this task as surveyed in  and . Among these, schema-level matchers exploit attribute labels, schema constraints, and structural similarities of the schemas. However, in the setting of Wikipedia infoboxes these Preprint submitted to Information Systems October 19, 2012  A mapping between the English and German infoboxes for Berlin techniques are not useful, because infobox definitions only describe a rather loose list of supported properties, as opposed to a strict relational or XML schema. Attribute names in infoboxes are not always sound, often cryptic or abbreviated, and the exact semantics of the attributes are not always clear from their names alone. Moreover, due to our multi-lingual scenario, attributes are labeled in different natural languages. This latter problem might be tackled by employing bilingual dictionaries, if the previously mentioned issues were solved. Due to the flat nature of infoboxes and their lack of constraints or types, other constraint-based matching approaches must fail. On the other hand, there are instance-based matching approaches, which leverage instance data of multiple data sources. Here, the basic assumption is that similarity of the instances of the attributes reflects the similarity of the attributes. To assess this similarity, instance-based approaches usually analyze the attributes of each schema individually, collecting information about value patterns and ranges, amongst others, such as in . A different, duplicate-based approach exploits information overlap across data sources . The idea there is to find two representations of same real-world objects (duplicates) and then suggest mappings between attributes that have the same or similar values. This approach has one important requirement: The data sources need to share a sufficient amount of common instances (or tuples, in a relational setting), i.e., instances describing the same real-world entity. Furthermore, the duplicates either have to be known in advance or have to be discovered despite a lack of knowledge of corresponding attributes. The approach presented in this article is based on such duplicate-based matching. Our approach consists of three steps: Entity matching, template matching, and attribute matching. The process is visualized in Fig. 2. (1) Entity matching: First, we find articles in different language versions that describe the same real-world entity. In particular, we make use of the crosslanguage links that are present for most Wikipedia articles and provide links between same entities across different language versions. We present a graph-based approach to resolve conflicts in the linking information. (2) Template matching: We determine a cross-lingual mapping between infobox templates by analyzing template co-occurrences in the language versions. (3) Attribute matching: The infobox attribute values of the corresponding articles are compared to identify matching attributes across the language versions, assuming that the values of corresponding attributes are highly similar for the majority of article pairs. As a first step we analyze the quality of Wikipedia’s interlanguage links in Sec. 2. We show how to use those links to create clusters of semantically equivalent entities with only one entity from each language in Sec. 3. This entity matching approach is evaluated in Sec. 4. In Sec. 5, we show how a crosslingual mapping between infobox templates can be established. The infobox attribute matching approach is described in Sec. 6 and in turn evaluated in Sec. 7. Related work in the areas of ILLs, concept identification, and infobox attribute matching is discussed in Sec. 8. Finally, Sec. 9 draws conclusions and discusses future work. 2. Interlanguage Links Our basic assumption is that there is a considerable amount of information overlap across the different Wikipedia language editions. Our infobox matching approach presented later requires mappings between articles in different language editions