27,102 research outputs found
Automated schema matching techniques: an exploratory study
Manual schema matching is a problem for many database applications that use multiple data sources including data warehousing and e-commerce applications. Current research attempts to address this problem by developing algorithms to automate aspects of the schema-matching task. In this paper, an approach using an external dictionary facilitates automated discovery of the semantic meaning of database schema terms. An experimental study was conducted to evaluate the performance and accuracy of five schema-matching techniques with the proposed approach, called SemMA. The proposed approach and results are compared with two existing semi-automated schema-matching approaches and suggestions for future research are made
Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)
Real-time analytics that requires integration and aggregation of
heterogeneous and distributed streaming and static data is a typical task in
many industrial scenarios such as diagnostics of turbines in Siemens. OBDA
approach has a great potential to facilitate such tasks; however, it has a
number of limitations in dealing with analytics that restrict its use in
important industrial applications. Based on our experience with Siemens, we
argue that in order to overcome those limitations OBDA should be extended and
become analytics, source, and cost aware. In this work we propose such an
extension. In particular, we propose an ontology, mapping, and query language
for OBDA, where aggregate and other analytical functions are first class
citizens. Moreover, we develop query optimisation techniques that allow to
efficiently process analytical tasks over static and streaming data. We
implement our approach in a system and evaluate our system with Siemens turbine
data
Human-Level Performance on Word Analogy Questions by Latent Relational Analysis
This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, machine translation, and information retrieval. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason/stone is analogous to the pair carpenter/wood; the relations between mason and stone are highly similar to the relations between carpenter and wood. Past work on semantic similarity measures has mainly been concerned with attributional similarity. For instance, Latent Semantic Analysis (LSA) can measure the degree of similarity between two words, but not between two relations. Recently the Vector Space Model (VSM) of information retrieval has been adapted to the task of measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus (they are not predefined), (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data (it is also used this way in LSA), and (3) automatically generated synonyms are used to explore reformulations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying noun-modifier relations, LRA achieves similar gains over the VSM, while using a smaller corpus
Similarity of Semantic Relations
There are at least two kinds of similarity. Relational similarity is
correspondence between relations, in contrast with attributional similarity,
which is correspondence between attributes. When two words have a high
degree of attributional similarity, we call them synonyms. When two pairs
of words have a high degree of relational similarity, we say that their
relations are analogous. For example, the word pair mason:stone is analogous
to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA),
a method for measuring relational similarity. LRA has potential applications in many
areas, including information extraction, word sense disambiguation,
and information retrieval. Recently the Vector Space Model (VSM) of information
retrieval has been adapted to measuring relational similarity,
achieving a score of 47% on a collection of 374 college-level multiple-choice
word analogy questions. In the VSM approach, the relation between a pair of words is
characterized by a vector of frequencies of predefined patterns in a large corpus.
LRA extends the VSM approach in three ways: (1) the patterns are derived automatically
from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency
data, and (3) automatically generated synonyms are used to explore variations of the
word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the
average human score of 57%. On the related problem of classifying semantic relations, LRA
achieves similar gains over the VSM
The Latent Relation Mapping Engine: Algorithm and Experiments
Many AI researchers and cognitive scientists have argued that analogy is the
core of cognition. The most influential work on computational modeling of
analogy-making is Structure Mapping Theory (SMT) and its implementation in the
Structure Mapping Engine (SME). A limitation of SME is the requirement for
complex hand-coded representations. We introduce the Latent Relation Mapping
Engine (LRME), which combines ideas from SME and Latent Relational Analysis
(LRA) in order to remove the requirement for hand-coded representations. LRME
builds analogical mappings between lists of words, using a large corpus of raw
text to automatically discover the semantic relations among the words. We
evaluate LRME on a set of twenty analogical mapping problems, ten based on
scientific analogies and ten based on common metaphors. LRME achieves
human-level performance on the twenty problems. We compare LRME with a variety
of alternative approaches and find that they are not able to reach the same
level of performance.Comment: related work available at http://purl.org/peter.turney
Just below the surface: developing knowledge management systems using the paradigm of the noetic prism
In this paper we examine how the principles embodied in the paradigm of the noetic prism can illuminate the construction of knowledge management systems. We draw on the formalism of the prism to examine three successful tools: frames, spreadsheets and databases, and show how their power and also their shortcomings arise from their domain representation, and how any organisational system based on integration of these tools and conversion between them is inevitably lossy. We suggest how a late-binding, hybrid knowledge based management system (KBMS) could be designed that draws on the lessons learnt from these tools, by maintaining noetica at an atomic level and storing the combinatory processes necessary to create higher level structure as the need arises. We outline the “just-below-the-surface” systems design, and describe its implementation in an enterprise-wide knowledge-based system that has all of the conventional office automation features
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
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