Random Indexing for Finding Similar Nodes within Large RDF Graphs

Damljanović, Danica; Petrak, Johann; Lupu, Mihai; Cunningham, Hamish; Carlsson, Mats; Engström, Gunnar; Andersson, Bo

doi:10.1007/978-3-642-25953-1_13

Cited by 12 publications

(15 citation statements)

References 9 publications

(8 reference statements)

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“…The generalised inner product approximation (12) and the encoding (3) and decoding (5) methods are available in the software implementation of generalised RI [47]. Next we present numerical results which demonstrate that the generalised methods that are introduced above are reasonable.…”

Section: Generalised Vector Semantic Analysismentioning

confidence: 99%

“…The approximation (12) is more efficient than (11) as a result of omitting the numerous projections from state space to decoded weight-vectors in the estimation of the inner product. Furthermore, the simulation experiments presented below show that the variance of the innerproduct approximation error increases when replacing the expectation value operations in (12) by an explicit inner product in state space, but otherwise an explicit evaluation of the inner product is possible in principle. This is expected because the averaging operations reduce the influence of state-space noise, ᾱ in (6), on the approximate inner product.…”

Section: Generalised Vector Semantic Analysismentioning

confidence: 99%

“…We study the generalised RI method presented above with numerical experiments. Ideally, analytical bounds should be derived for the error terms in (9) and the related approximation (12). However, the analysis is complicated because of the sparse ternary index vectors and the dependence on the structure of the data.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“…For example, the accuracy of RI is comparable to SVD-based methods in a TOEFL synonym identification task [31], and that result has been further improved in the case of RI [45]. RI of co-occurrence matrices for semantic analysis works surprisingly well [11,30,43,52], and the method has been adopted in other applications, such as indexing of literature databases [54], event detection in blogs [26], web user clustering and page prefetching [57], graph searching for the semantic web [12], diagnosis code assignment to patients [24], predicting speculation in biomedical literature [55] and failure prediction [19]. In general, there is an increasing interest for randomisation in information processing because it enables the use of simple algorithms, which can be organised to exploit parallel computation in an efficient way [6,22].…”

Section: Introductionmentioning

confidence: 99%

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Random indexing of multidimensional data

Sandin

Emruli²,

Sahlgren

2016

Knowl Inf Syst

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Random indexing (RI) is a lightweight dimension reduction method, which is used, for example, to approximate vector semantic relationships in online natural language processing systems. Here we generalise RI to multidimensional arrays and therefore enable approximation of higher-order statistical relationships in data. The generalised method is a sparse implementation of random projections, which is the theoretical basis also for ordinary RI and other randomisation approaches to dimensionality reduction and data representation. We present numerical experiments which demonstrate that a multidimensional generalisation of RI is feasible, including comparisons with ordinary RI and principal component analysis. The RI method is well suited for online processing of data streams because relationship weights can be updated incrementally in a fixed-size distributed representation, and inner products can be approximated on the fly at low computational cost. An open source implementation of generalised RI is provided.

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Section: Generalised Vector Semantic Analysismentioning

confidence: 99%

Section: Generalised Vector Semantic Analysismentioning

confidence: 99%

Section: Simulation Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Random indexing of multidimensional data

Sandin

Emruli²,

Sahlgren

2016

Knowl Inf Syst

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“…• The retrieved "documents" (as a set of URIs) can be an entry point for exploring large and unknown graphs (see [2]). …”

Section: Using Virtual Documentsmentioning

confidence: 99%

Using virtual documents to move information retrieval and knowledge management closer together

Damljanović

Kruschwitz

Albakour

2011

Proceedings of the Fourth Workshop on Exploiting Semantic Annotations in Information Retrieval

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While Information Retrieval approaches typically rely on a bag-of-word approach and are therefore fairly shallow, Knowledge Management is based on a deep semantic representation. Both allow users to sift through huge volumes of information and to identify the relevant document or answer for a particular information need. Obviously, these areas go beyond this simple information access scenario. Nevertheless, it is also a fact that these areas form fairly separate communities. We propose an idea of how to make use of techniques coming from both ends of the spectrum and combine them in methods that are more powerful than each of them individually. Our idea is based on turning structured information into virtual documents built to preserve the concepts and relations inherent in the semantically rich data before we apply Information Retrieval methods.

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