One challenge for content providers on the Web is determining who consumes their content. For instance, online newspapers want to know who is reading their articles. Previous approaches have tried to determine such audience demographics by placing cookies on users' systems, or by directly asking consumers (e.g., through surveys). The first approach may make users uncomfortable, and the second is not scalable. In this paper we focus on determining the demographics of a Website's audience by analyzing the blogs that link to the Website. We analyze both the text of the blogs and the network connectivity of the blog network to determine demographics such as whether a person "is married" or "has pets." Presumably bloggers linking to sites also consume the content of those sites. Therefore, the discovered demographics for the bloggers can be used to represent a proxy set of demographics for a subset of the Website's consumers. We demonstrate that in many cases we can infer sub-audiences for a site from these demographics. Further, this feasibility demonstrates that very specific demographics for sites can be generated as we improve the methods for determining them (e.g., finding people who play video games). In our study we analyze blogs collected from more than 590,000 bloggers collected over a six month period that link to more than 488,000 distinct, external websites.

Recently, much attention has been given to extracting tables from Web data. In this problem, the column definitions and tuples (such as what "company" is headquartered in what "city,") are extracted from Web text, structured Web data such as lists, or results of querying the deep Web, creating the table of interest. In this paper, we examine the problem of extracting and discovering multiple tables in a given domain, generating a truly multi-relational database as output. Beyond discovering the relations that define single tables, our approach discovers and leverages "within column" set membership relations, and discovers relations across the extracted tables (e.g., joins). By leveraging within-column relations our method can extract table instances that are ambiguous or rare, and by discovering joins, our method generates truly multi-relational output. Further, our approach uses taxonomic queries to bootstrap the extraction, rather than the more traditional "seed instances." Creating seeds often requires more domain knowledge than taxonomic queries, and previous work has shown that extraction methods may be sensitive to which input seeds they are given. We test our approach on two real world domains: NBA basketball and cancer information. Our results demonstrate that our approach generates databases of relevant tables from disparate Web information, and discovers the relations between them. Further, we show that by leveraging the "within column" relation our approach can identify a significant number of relevant tuples that would be difficult to do so otherwise.

Vast amounts of text on the Web are unstructured and ungrammatical, such as classified ads, auction listings, forum postings, etc. We call such text “posts.” Despite their inconsistent structure and lack of grammar, posts are full of useful information. This paper presents work on semi-automatically building tables of relational information, called “reference sets,” by analyzing such posts directly. Reference sets can be applied to a number of tasks such as ontology maintenance and information extraction. Our reference-set construction method starts with just a small amount of background knowledge, and constructs tuples representing the entities in the posts to form a reference set. We also describe an extension to this approach for the special case where even this small amount of background knowledge is impossible to discover and use. To evaluate the utility of the machine-constructed reference sets, we compare them to manually constructed reference sets in the context of reference-set-based information extraction. Our results show the reference sets constructed by our method outperform manually constructed reference sets. We also compare the reference-set-based extraction approach using the machine-constructed reference set to supervised extraction approaches using generic features. These results demonstrate that using machine-constructed reference sets outperforms the supervised methods, even though the supervised methods require training data.

In order for agents to act on behalf of users, they will have to retrieve and integrate vast amounts of textual data on the World Wide Web. However, much of the useful data on the Web is neither grammatical nor formally structured, making querying difficult. Examples of these types of data sources are online classifieds like Craigslist and auction item listings like eBay. We call this unstructured, ungrammatical data "posts." The unstructured nature of posts makes query and integration difficult because the attributes are embedded within the text. Also, these attributes do not conform to standardized values, which prevents queries based on a common attribute value. The schema is unknown and the values may vary dramatically making accurate search difficult. Creating relational data for easy querying requires that we define a schema for the embedded attributes and extract values from the posts while standardizing these values. Traditional information extraction (IE) is inadequate to perform this task because it relies on clues from the data, such as structure or natural language, neither of which are found in posts. Furthermore, traditional information extraction does not incorporate data cleaning, which is necessary to accurately query and integrate the source. The two-step approach described in this paper creates relational data sets from unstructured and ungrammatical text by addressing both issues. To do this, we require a set of known entities called a "reference set." The first step aligns each post to each member of each reference set. This allows our algorithm to define a schema over the post and include standard values for the attributes defined by this schema. The second step performs information extraction for the attributes, including attributes not easily represented by reference sets, such as a price. In this manner we create a relational structure over previously unstructured data, supporting deep and accurate queries over the data as well as standard values for integration. Our experimental results show that our technique matches the posts to the reference set accurately and efficiently and outperforms state-of-the-art extraction systems on the extraction task from posts.

Record linkage is the process of determining that two records refer to the same entity. A key subprocess is evaluating how well the individual fields, or attributes, of the records match each other. One approach to matching fields is to use hand-written domain-specific rules. This "expert systems" approach may result in good performance for specific applications, but it is not scalable. This paper describes a new machine learning approach that creates expert-like rules for field matching. In our approach, the relationship between two field values is described by a set of heterogeneous transformations. Previous machine learning methods used simple models to evaluate the distance between two fields. However, our approach enables more sophisticated relationships to be modeled, which better capture the complex domain specific, common-sense phenomena that humans use to judge similarity. We compare our approach to methods that rely on simpler homogeneous models in several domains. By modeling more complex relationships we produce more accurate results.