9,714 research outputs found
Toward Entity-Aware Search
As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. In my Ph.D. study, we focus on a novel type of Web search that is aware of data entities inside pages, a significant departure from traditional document retrieval. We study the various essential aspects of supporting entity-aware Web search. To begin with, we tackle the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We also report a prototype system built to show the initial promise of the proposal. Then, we aim at distilling and abstracting the essential computation requirements of entity search. From the dual views of reasoning--entity as input and entity as output, we propose a dual-inversion framework, with two indexing and partition schemes, towards efficient and scalable query processing. Further, to recognize more entity instances, we study the problem of entity synonym discovery through mining query log data. The results we obtained so far have shown clear promise of entity-aware search, in its usefulness, effectiveness, efficiency and scalability
Semantic Modeling of Analytic-based Relationships with Direct Qualification
Successfully modeling state and analytics-based semantic relationships of
documents enhances representation, importance, relevancy, provenience, and
priority of the document. These attributes are the core elements that form the
machine-based knowledge representation for documents. However, modeling
document relationships that can change over time can be inelegant, limited,
complex or overly burdensome for semantic technologies. In this paper, we
present Direct Qualification (DQ), an approach for modeling any semantically
referenced document, concept, or named graph with results from associated
applied analytics. The proposed approach supplements the traditional
subject-object relationships by providing a third leg to the relationship; the
qualification of how and why the relationship exists. To illustrate, we show a
prototype of an event-based system with a realistic use case for applying DQ to
relevancy analytics of PageRank and Hyperlink-Induced Topic Search (HITS).Comment: Proceedings of the 2015 IEEE 9th International Conference on Semantic
Computing (IEEE ICSC 2015
WikiM: Metapaths based Wikification of Scientific Abstracts
In order to disseminate the exponential extent of knowledge being produced in
the form of scientific publications, it would be best to design mechanisms that
connect it with already existing rich repository of concepts -- the Wikipedia.
Not only does it make scientific reading simple and easy (by connecting the
involved concepts used in the scientific articles to their Wikipedia
explanations) but also improves the overall quality of the article. In this
paper, we present a novel metapath based method, WikiM, to efficiently wikify
scientific abstracts -- a topic that has been rarely investigated in the
literature. One of the prime motivations for this work comes from the
observation that, wikified abstracts of scientific documents help a reader to
decide better, in comparison to the plain abstracts, whether (s)he would be
interested to read the full article. We perform mention extraction mostly
through traditional tf-idf measures coupled with a set of smart filters. The
entity linking heavily leverages on the rich citation and author publication
networks. Our observation is that various metapaths defined over these networks
can significantly enhance the overall performance of the system. For mention
extraction and entity linking, we outperform most of the competing
state-of-the-art techniques by a large margin arriving at precision values of
72.42% and 73.8% respectively over a dataset from the ACL Anthology Network. In
order to establish the robustness of our scheme, we wikify three other datasets
and get precision values of 63.41%-94.03% and 67.67%-73.29% respectively for
the mention extraction and the entity linking phase
Detecting and Explaining Causes From Text For a Time Series Event
Explaining underlying causes or effects about events is a challenging but
valuable task. We define a novel problem of generating explanations of a time
series event by (1) searching cause and effect relationships of the time series
with textual data and (2) constructing a connecting chain between them to
generate an explanation. To detect causal features from text, we propose a
novel method based on the Granger causality of time series between features
extracted from text such as N-grams, topics, sentiments, and their composition.
The generation of the sequence of causal entities requires a commonsense
causative knowledge base with efficient reasoning. To ensure good
interpretability and appropriate lexical usage we combine symbolic and neural
representations, using a neural reasoning algorithm trained on commonsense
causal tuples to predict the next cause step. Our quantitative and human
analysis show empirical evidence that our method successfully extracts
meaningful causality relationships between time series with textual features
and generates appropriate explanation between them.Comment: Accepted at EMNLP 201
How Unique is Your .onion? An Analysis of the Fingerprintability of Tor Onion Services
Recent studies have shown that Tor onion (hidden) service websites are
particularly vulnerable to website fingerprinting attacks due to their limited
number and sensitive nature. In this work we present a multi-level feature
analysis of onion site fingerprintability, considering three state-of-the-art
website fingerprinting methods and 482 Tor onion services, making this the
largest analysis of this kind completed on onion services to date.
Prior studies typically report average performance results for a given
website fingerprinting method or countermeasure. We investigate which sites are
more or less vulnerable to fingerprinting and which features make them so. We
find that there is a high variability in the rate at which sites are classified
(and misclassified) by these attacks, implying that average performance figures
may not be informative of the risks that website fingerprinting attacks pose to
particular sites.
We analyze the features exploited by the different website fingerprinting
methods and discuss what makes onion service sites more or less easily
identifiable, both in terms of their traffic traces as well as their webpage
design. We study misclassifications to understand how onion service sites can
be redesigned to be less vulnerable to website fingerprinting attacks. Our
results also inform the design of website fingerprinting countermeasures and
their evaluation considering disparate impact across sites.Comment: Accepted by ACM CCS 201
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