6,447 research outputs found
Entity Query Feature Expansion Using Knowledge Base Links
Recent advances in automatic entity linking and knowledge base
construction have resulted in entity annotations for document and
query collections. For example, annotations of entities from large
general purpose knowledge bases, such as Freebase and the Google
Knowledge Graph. Understanding how to leverage these entity
annotations of text to improve ad hoc document retrieval is an open
research area. Query expansion is a commonly used technique to
improve retrieval effectiveness. Most previous query expansion
approaches focus on text, mainly using unigram concepts. In this
paper, we propose a new technique, called entity query feature
expansion (EQFE) which enriches the query with features from
entities and their links to knowledge bases, including structured
attributes and text. We experiment using both explicit query entity
annotations and latent entities. We evaluate our technique on TREC
text collections automatically annotated with knowledge base entity
links, including the Google Freebase Annotations (FACC1) data.
We find that entity-based feature expansion results in significant
improvements in retrieval effectiveness over state-of-the-art text
expansion approaches
mARC: Memory by Association and Reinforcement of Contexts
This paper introduces the memory by Association and Reinforcement of Contexts
(mARC). mARC is a novel data modeling technology rooted in the second
quantization formulation of quantum mechanics. It is an all-purpose incremental
and unsupervised data storage and retrieval system which can be applied to all
types of signal or data, structured or unstructured, textual or not. mARC can
be applied to a wide range of information clas-sification and retrieval
problems like e-Discovery or contextual navigation. It can also for-mulated in
the artificial life framework a.k.a Conway "Game Of Life" Theory. In contrast
to Conway approach, the objects evolve in a massively multidimensional space.
In order to start evaluating the potential of mARC we have built a mARC-based
Internet search en-gine demonstrator with contextual functionality. We compare
the behavior of the mARC demonstrator with Google search both in terms of
performance and relevance. In the study we find that the mARC search engine
demonstrator outperforms Google search by an order of magnitude in response
time while providing more relevant results for some classes of queries
NOUS: Construction and Querying of Dynamic Knowledge Graphs
The ability to construct domain specific knowledge graphs (KG) and perform
question-answering or hypothesis generation is a transformative capability.
Despite their value, automated construction of knowledge graphs remains an
expensive technical challenge that is beyond the reach for most enterprises and
academic institutions. We propose an end-to-end framework for developing custom
knowledge graph driven analytics for arbitrary application domains. The
uniqueness of our system lies A) in its combination of curated KGs along with
knowledge extracted from unstructured text, B) support for advanced trending
and explanatory questions on a dynamic KG, and C) the ability to answer queries
where the answer is embedded across multiple data sources.Comment: Codebase: https://github.com/streaming-graphs/NOU
Social Search with Missing Data: Which Ranking Algorithm?
Online social networking tools are extremely popular, but can miss potential discoveries latent in the social 'fabric'. Matchmaking services which can do naive profile matching with old database technology are too brittle in the absence of key data, and even modern ontological markup, though powerful, can be onerous at data-input time. In this paper, we present a system called BuddyFinder which can automatically identify buddies who can best match a user's search requirements specified in a term-based query, even in the absence of stored user-profiles. We deploy and compare five statistical measures, namely, our own CORDER, mutual information (MI), phi-squared, improved MI and Z score, and two TF/IDF based baseline methods to find online users who best match the search requirements based on 'inferred profiles' of these users in the form of scavenged web pages. These measures identify statistically significant relationships between online users and a term-based query. Our user evaluation on two groups of users shows that BuddyFinder can find users highly relevant to search queries, and that CORDER achieved the best average ranking correlations among all seven algorithms and improved the performance of both baseline methods
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