Web searches tend to be short and ambiguous. It is therefore not surprising that Web query disambiguation is an actively researched topic. To provide a personalized experience for a user, most existing work relies on search engine log data in which the search activities of that particular user, as well as other users, are recorded over long periods of time. Such approaches may raise privacy concerns and may be difficult to implement for pragmatic reasons. We present an approach to Web query disambiguation that bases its predictions only on a short glimpse of user search activity, captured in a brief session of 4-6 previous searches on average. Our method exploits the relations of the current search session to previous similarly short sessions of other users in order to predict the user’s intentions and is based on Markov logic, a statistical relational learning model that has been successfully applied to challenging language problems in the past. We present empirical results that demonstrate the effectiveness of our proposed approach on data collected from a commercial general-purpose search engine.
We consider mining unusual patterns from text T. Unlike existing methods which assume probabilistic models and use simple estimation methods, we employ a set B of background text in addition to T and compositions w=xy of x and y as patterns. A string w is peculiar if there exist x and y such that w=xy, each of x and y is more frequent in B than in T, and conversely w=xy is more frequent in T. The frequency of xy in T is very small since x and y are infrequent in T, but xy is relatively abundant in T compared to xy in B. Despite these complex conditions for peculiar compositions, we develop a fast algorithm to find peculiar compositions using the suffix tree. Experiments using DNA sequences show scalability of our algorithm due to our pruning techniques and the superiority of the concept of the peculiar composition.
Traditional machine learning methods only consider relationships between feature values within individual data instances while disregarding the dependencies that link features across instances. In this work, we develop a general approach to supervised learning by leveraging higher-order dependencies between features. We introduce a novel Bayesian framework for classification named Higher Order Naive Bayes (HONB). Unlike approaches that assume data instances are independent, HONB leverages co-occurrence relations between feature values across different instances. Additionally, we generalize our framework by developing a novel data-driven space transformation that allows any classifier operating in vector spaces to take advantage of these higher-order co-occurrence relations. Results obtained on several benchmark
text corpora demonstrate that higher-order approaches achieve significant improvements in classification accuracy over the baseline (first-order) methods.
We present a novel method for key term extraction from text documents. In our method, document is modeled as a graph of semantic relationships between terms of that document. We exploit the following remarkable feature of the graph: the terms related to the main topics of the document tend to bunch up into densely interconnected subgraphs or commu- nities, while non-important terms fall into weakly intercon- nected communities, or even become isolated vertices. We apply graph community detection techniques to partition the graph into thematically cohesive groups of terms. We introduce a criterion function to select groups that contain key terms discarding groups with unimportant terms. To weight terms and determine semantic relatedness between them we exploit information extracted from Wikipedia. Using such an approach gives us the following two ad- vantages. First, it allows effectively processing multi-theme documents. Second, it is good at filtering out noise infor- mation in the document, such as, for example, navigational bars or headers in web pages. Evaluations of the method show that it outperforms exist- ing methods producing key terms with higher precision and recall. Additional experiments on web pages prove that our method appears to be substantially more effective on noisy and multi-theme documents than existing methods.
Measuring the similarity between semantic relations that hold among entities is an important and necessary step in various Web related tasks such as relation extraction, information retrieval and analogy detection. For example, consider the case in which a person knows a pair of entities (e.g. Google, YouTube), between which a partic- ular relation holds (e.g. acquisition). The person is interested in retrieving other such pairs with similar relations (e.g. Microsoft, Powerset). Existing keyword-based search engines cannot be ap- plied directly in this case because, in keyword-based search, the goal is to retrieve documents that are relevant to the words used in a query – not necessarily to the relations implied by a pair of words. We propose a relational similarity measure, using a Web search en- gine, to compute the similarity between semantic relations implied by two pairs of words. Our method has three components: repre- senting the various semantic relations that exist between a pair of words using automatically extracted lexical patterns, clustering the extracted lexical patterns to identify the different patterns that ex- press a particular semantic relation, and measuring the similarity between semantic relations using a metric learning approach. We evaluate the proposed method in two tasks: classifying semantic relations between named entities, and solving word-analogy ques- tions. The proposed method outperforms all baselines in a relation classification task with a statistically significant average precision score of 0.74. Moreover, it reduces the time taken by Latent Relational Analysis to process 374 word-analogy questions from 9 days to less than 6 hours, with an SAT score of 51%.
In this paper we present Triplify – a simplistic but effective approach to publish Linked Data from relational databases. Triplify is based on mapping HTTP-URI requests onto relational database queries. Triplify transforms the resulting relations into RDF statements and publishes the data on the Web in various RDF serializations, in particular as Linked Data. The rationale for developing Triplify is that the largest part of information on the Web is already stored in structured form, often as data contained in relational databases, but usually published by Web applications only as HTML mixing structure, layout and content. In order to reveal the pure structured information behind the current Web, we have implemented Triplify as a light-weight software component, which can be easily integrated into and deployed by the numerous, widely installed Web applications. Our approach includes a method for publishing update logs to enable incremental crawling of linked data sources. Triplify is complemented by a library of configurations for common relational schemata and a REST-enabled data source registry. Triplify configurations containing mappings are provided for many popular Web applications, including osCommerce, WordPress, Drupal, Gallery, and phpBB. We will show that despite its light-weight architecture Triplify is usable to publish very large datasets, such as 160GB of geo data from the OpenStreetMap project.
This paper presents SOFIE, a system for automated on- tology extension. SOFIE can parse natural language docu- ments, extract ontological facts from them and link the facts into an ontology. SOFIE uses logical reasoning on the exist- ing knowledge and on the new knowledge in order to disam- biguate words to their most probable meaning, to reason on the meaning of text patterns and to take into account world knowledge axioms. This allows SOFIE to check the plau- sibility of hypotheses and to avoid inconsistencies with the ontology. The framework of SOFIE unites the paradigms of pattern matching, word sense disambiguation and ontolog- ical reasoning in one unified model. Our experiments show that SOFIE delivers high-quality output, even from unstruc- tured Internet documents.