77 research outputs found
Proximity Full-Text Search with a Response Time Guarantee by Means of Additional Indexes
Full-text search engines are important tools for information retrieval. Term
proximity is an important factor in relevance score measurement. In a proximity
full-text search, we assume that a relevant document contains query terms near
each other, especially if the query terms are frequently occurring words. A
methodology for high-performance full-text query execution is discussed. We
build additional indexes to achieve better efficiency. For a word that occurs
in the text, we include in the indexes some information about nearby words.
What types of additional indexes do we use? How do we use them? These questions
are discussed in this work. We present the results of experiments showing that
the average time of search query execution is 44-45 times less than that
required when using ordinary inverted indexes.
This is a pre-print of a contribution "Veretennikov A.B. Proximity Full-Text
Search with a Response Time Guarantee by Means of Additional Indexes" published
in "Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and Applications.
IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868"
published by Springer, Cham. The final authenticated version is available
online at: https://doi.org/10.1007/978-3-030-01054-6_66. The work was supported
by Act 211 Government of the Russian Federation, contract no 02.A03.21.0006.Comment: Alexander B. Veretennikov. Chair of Calculation Mathematics and
Computer Science, INSM. Ural Federal Universit
Query-free news search
Many daily activities present information in the form of a stream of text, and often people can benefit from additional information on the topic discussed. TV broadcast news can be treated as one such stream of text; in this paper we discuss finding news articles on the web that are relevant to news currently being broadcast.We evaluated a variety of algorithms for this problem, looking at the impact of inverse document frequency, stemming, compounds, history, and query length on the relevance and coverage of news articles returned in real time during a broadcast. We also evaluated several postprocessing techniques for improving the precision, including reranking using additional terms, reranking by document similarity, and filtering on document similarity. For the best algorithm, 84%-91% of the articles found were relevant, with at least 64% of the articles being on the exact topic of the broadcast. In addition, a relevant article was found for at least 70% of the topics
Discovering gene annotations in biomedical text databases
<p>Abstract</p> <p>Background</p> <p>Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data.</p> <p>Results</p> <p>In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products.</p> <p>In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general.</p> <p>Conclusion</p> <p>GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values.</p
Theories for influencer identification in complex networks
In social and biological systems, the structural heterogeneity of interaction
networks gives rise to the emergence of a small set of influential nodes, or
influencers, in a series of dynamical processes. Although much smaller than the
entire network, these influencers were observed to be able to shape the
collective dynamics of large populations in different contexts. As such, the
successful identification of influencers should have profound implications in
various real-world spreading dynamics such as viral marketing, epidemic
outbreaks and cascading failure. In this chapter, we first summarize the
centrality-based approach in finding single influencers in complex networks,
and then discuss the more complicated problem of locating multiple influencers
from a collective point of view. Progress rooted in collective influence
theory, belief-propagation and computer science will be presented. Finally, we
present some applications of influencer identification in diverse real-world
systems, including online social platforms, scientific publication, brain
networks and socioeconomic systems.Comment: 24 pages, 6 figure
GIANT: Scalable Creation of a Web-scale Ontology
Understanding what online users may pay attention to is key to content
recommendation and search services. These services will benefit from a highly
structured and web-scale ontology of entities, concepts, events, topics and
categories. While existing knowledge bases and taxonomies embody a large volume
of entities and categories, we argue that they fail to discover properly
grained concepts, events and topics in the language style of online population.
Neither is a logically structured ontology maintained among these notions. In
this paper, we present GIANT, a mechanism to construct a user-centered,
web-scale, structured ontology, containing a large number of natural language
phrases conforming to user attentions at various granularities, mined from a
vast volume of web documents and search click graphs. Various types of edges
are also constructed to maintain a hierarchy in the ontology. We present our
graph-neural-network-based techniques used in GIANT, and evaluate the proposed
methods as compared to a variety of baselines. GIANT has produced the Attention
Ontology, which has been deployed in various Tencent applications involving
over a billion users. Online A/B testing performed on Tencent QQ Browser shows
that Attention Ontology can significantly improve click-through rates in news
recommendation.Comment: Accepted as full paper by SIGMOD 202
Extracting patterns and relations from the world wide web
Abstract. The World Wide Web is a vast resource for information. At the same time it is extremely distributed. A particular type of data such as restaurant lists may be scattered across thousands of independent information sources in many di erent formats. In this paper, we consider the problem of extracting a relation for such a data type from all of these sources automatically. We present a technique which exploits the duality between sets of patterns and relations to grow the target relation starting from a small sample. To test our technique we use it to extract a relation of (author,title) pairs from the World Wide Web.
- …