This paper investigates the “named-entity disambiguation” task on the Web—identifying the referent of a string, found on an arbitrary Web page. The GROUNDER system, introduced in this paper, addresses two challenges not considered by previous work: how to utilize a priori information (e.g., Bill Clinton is more prominent on the Web than Clinton County) to improve disambiguation, and how to compose this prior information with contextual evidence. GROUNDER addresses both challenges by leveraging the user-contributed knowledge in Wikipedia and providing a novel formulation of the task. On a sample of strings drawn from the Web, GROUNDER achieves precision of 1.0 at recall 0.34, and precision 0.90 at recall 0.60.

Interlinking text documents with Linked Open Data enables the Web of Data to be used as background knowledge within document-oriented applications such as search and faceted browsing. As a step towards interconnecting the Web of Documents with the Web of Data, we developed DBpedia Spotlight, a system for automatically annotating text documents with DBpedia URIs. DBpedia Spotlight allows users to configure the annotations to their specific needs through the DBpedia Ontology and quality measures such as prominence, topical pertinence, contextual ambiguity and disambiguation confidence. We compare our approach with the state of the art in disambiguation, and evaluate our results in light of three baselines and six publicly available annotation systems, demonstrating the competitiveness of our system. DBpedia Spotlight is shared as open source and deployed as a Web Service freely available for public use.

Tables are a universal idiom to present relational data. Billions of tables on Web pages express entity references, attributes and relationships. This representation of relational world knowledge is usually considerably better than completely unstructured, free-format text. At the same time, unlike manually-created knowledge bases, relational information mined from “organic ” Web tables need not be constrained by availability of precious editorial time. Unfortunately, in the absence of any formal, uniform schema imposed on Web tables, Web search cannot take advantage of these high-quality sources of relational information. In this paper we propose new machine learning techniques to annotate table cells with entities that they likely mention, table columns with types from which entities are drawn for cells in the column, and relations that pairs of table columns seek to express. We propose a new graphical model for making all these labeling decisions for each table simultaneously, rather than make separate local decisions for entities, types and relations. Experiments using the YAGO catalog, DB-Pedia, tables from Wikipedia, and over 25 million HTML tables from a 500 million page Web crawl uniformly show the superiority of our approach. We also evaluate the impact of better annotations on a prototype relational Web search tool. We demonstrate clear benefits of our annotations beyond indexing tables in a purely textual manner. 1.

Abstract. In the named entity normalization task, a system identifies a canonical unambiguous referent for names like Bush or Alabama. Resolving synonymy and ambiguity of such names can benefit end-to-end information access tasks. We evaluate two entity normalization methods based on Wikipedia in the context of both passage and document retrieval for question anwering. We find that even a simple normalization method leads to improvements of early precision, both for document and passage retrieval. Moreover, better normalization results in better retrieval performance. 1

A great share of applications in modern information technology can benefit from large coverage, machine accessible knowledge bases. However, the bigger part of todays knowledge is provided in the form of unstructured data, mostly plain text. As an initial step to exploit such data, we present Wanderlust, an algorithm that automatically extracts semantic relations from natural language text. The procedure uses deep linguistic patterns that are defined over the dependency grammar of sentences. Due to its linguistic nature, the method performs in an unsupervised fashion and is not restricted to any specific type of semantic relation. The applicability of the proposed approach is examined in a case study, in which it is put to the task of generating a semantic wiki from the English Wikipedia corpus. We present an exhaustive discussion about the insights obtained from this particular case study including considerations about the generality of the approach.

Twitter and other microblogs have rapidly become a significant means by which people communicate with the world and each other in near realtime. There has been a large number of studies surrounding these social media, focusing on areas such as information spread, various centrality measures, topic detection and more. However, one area which has not received much attention is trying to better understand what information is being spread and why it is being spread. This work looks to get a better understanding of what makes people spread information in tweets or microblogs through the use of retweeting. Several retweet behavior models are presented and evaluated on a Twitter data set consisting of over 768,000 tweets gathered from monitoring over 30,000 users for a period of one month. We evaluate the proposed models against each user and show how people use different retweet behavior models. For example, we find that although users in the majority of cases do not retweet information on topics that they themselves Tweet about as or from people who are “like them ” (hence anti-homophily), we do find that models which do take homophily, or similarity, into account fits the observed retweet behaviors much better than other more general models which do not take this into account. We further find that, not surprisingly, people’s retweeting behavior is better explained through multiple different models rather than one model. 1

Nowadays the Web represents a growing collection of an enormous amount of contents where the need for better ways to find and organize the available data is becoming a fundamental issue, in order to deal with information overload. Keyword based Web searches are actually the preferred mean to seek for contents related to a specific topic. Search engines and collaborative tagging systems make possible the search for information thanks to the association of descriptive keywords to Web resources. All of them show problems of inconsistency and consequent reduction of recall and precision of searches, due to polysemy, synonymy and in general all the different lexical forms that can be used to refer to a particular meaning. A possible way to face or at least reduce these problems is represented by the introduction

In many search domains, both contents and searches are frequently tied to named entities such as a person, a company or similar. An example of such a domain is a news archive. One challenge from an information retrieval point of view is that a single entity can have more than one way of referring to it. In this paper we describe how to use Wikipedia contents to automatically generate a dictionary of named entities and synonyms that are all referring to the same entity. This dictionary can subsequently be used to improve search quality, for example using query expansion. Through an experimental evaluation we show that with our approach, we can find named entities and their synonyms with a high degree of accuracy. 2 1

In this article we present Supervised Semantic Indexing (SSI) which defines a class of nonlinear (quadratic) models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score. Like Latent Semantic Indexing (LSI), our models take account of correlations between words (synonymy, polysemy). However, unlike LSI our models are trained from a supervised signal directly on the ranking task of interest, which we argue is the reason for our superior results. As the query and target texts are modeled separately, our approach is easily generalized to different retrieval tasks, such as crosslanguage retrieval or online advertising placement. Dealing with models on all pairs of words features is computationally challenging. We propose several improvements to our basic model for addressing this issue, including low rank (but diagonal preserving) representations, correlated feature hashing (CFH) and sparsification. We provide an empirical study of all these methods on retrieval tasks based on Wikipedia documents as well as an Internet advertisement task. We obtain state-of-the-art performance while providing realistically scalable methods.

Abstract—We investigate the automatic generation of topic pages as an alternative to the current Web search paradigm. Topic pages explicitly aggregate information across documents, filter redundancy, and promote diversity of topical aspects. We propose a novel framework for building rich topical aspect models and selecting diverse information from the Web. In particular, we use Web search logs to build aspect models with various degrees of specificity, and then employ these aspect models as input to a sentence selection method that identifies relevant and non-redundant sentences from the Web. Automatic and manual evaluations on biographical topics show that topic pages built by our system compare favorably to regular Web search results and to MDS-style summaries of the Web results on all metrics employed. Keywords-Web search; topic page; query log; aspect model. I.