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1

Optimization of Retrieval Algorithms on Large Scale Knowledge Graphs

Dörpinghaus Jens, Stefan Andreas

Annals of Computer Science and Information Systems

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2020

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Vol. 21

227--236

EN

Knowledge graphs have been shown to play an important role in recent knowledge mining and discovery, for example in the field of life sciences or bioinformatics. Although a lot of research has been done on the field of query optimization, query transformation and of course in storing and retrieving large scale knowledge graphs the field of algorithmic optimization is still a major challenge and a vital factor in using graph databases. Few researchers have addressed the problem of optimizing algorithms on large scale labeled property graphs. Here, we present two optimization approaches and compare them with a naive approach of directly querying the graph database. The aim of our work is to determine limiting factors of graph databases like Neo4j and we describe a novel solution to tackle these challenges. For this, we suggest a classification schema to differ between the complexity of a problem on a graph database. We evaluate our optimization approaches on a test system containing a knowledge graph derived biomedical publication data enriched with text mining data. This dense graph has more than 71M nodes and 850M relationships. The results are very encouraging and -- depending on the problem -- we were able to show a speedup of a factor between 44 and 3839.

2

Knowledge Detection and Discovery using Semantic Graph Embeddings on Large Knowledge Graphs generated on Text Mining Results

Dörpinghaus Jens, Jacobs Marc

Annals of Computer Science and Information Systems

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2020

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Vol. 21

169--178

EN

Knowledge graphs play a central role in big data integration, especially for connecting data from different domains. Bringing unstructured texts, e.g. from scientific literature, into a structured, comparable format is one of the key assets. Here, we use knowledge graphs in the biomedical domain working together with text mining based document data for knowledge extraction and retrieval from text and natural language structures. For example cause and effect models, can potentially facilitate clinical decision making or help to drive research towards precision medicine. However, the power of knowledge graphs critically depends on context information. Here we provide a novel semantic approach towards a context enriched biomedical knowledge graph utilizing data integration with linked data applied to language technologies and text mining. This graph concept can be used for graph embedding applied in different approaches, e.g with focus on topic detection, document clustering and knowledge discovery. We discuss algorithmic approaches to tackle these challenges and show results for several applications like search query finding and knowledge discovery. The presented remarkable approaches lead to valuable results on large knowledge graphs.

3

Quick text retrieval algorithm supporting synonyms based on fuzzy logic

Saxena P. Ch., Gupta N.

Computing, Multimedia and Intelligent Techniques

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2006

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Vol. 2, nr 1

7-24

EN

Traditional information retrieval techniques become inadequate for the increasingly vast amounts of text data. Here we show a method of query processing, which retrieve the documents containing not only the query terms but also documents having their synonyms. The method performs the query processing by retrieving and scanning the inverted index document list. We show that query response time for conjunctive Boolean queries can be dramatically reduced, at cost in terms of secondary storage, by applying range partition feature of Oracle to reduce the primary memory storage space requirement for looking the inverted list. The proposed method is based on fuzzy relations and fuzzy reasoning to retrieve only top ranking documents from the database and grouping of the retrieved documents through Suffix tree clustering.

4

Aktualizowalne perspektywy w przetwarzaniu rozproszonych zapytań

Kuliberda K.

Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

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2003

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T. 7, z. 3

755-766

PL

Tematem niniejszego artykułu jest krótki przegląd stanu wiedzy na temat aktualizacji danych w rozproszonych bazach danych za pomocą aktualizowanych perspektyw, a także podstawy przetwarzania rozproszonych zapytań. W artykule zaprezentowano nowe podejście do aktualizowanych perspektyw na podstawie obiektowego języka zapytań SBQL (Stack Base Query Language). Przedstawiono krótką charakterystykę najczęściej stosowanych mechanizmów wykorzystywania perspektyw w bazach danych.

EN

The article presents the short review of knowledge about updating data with Updateable Views and basic processing methods in distributed queries. Also the article introduces a new approach based on Stack Base Query Language for Updateable Views. Finally the article characterizes shortly mechanism using in data bases views.

5

Optimization of object-oriented queries by factoring out independent subqueries

Płodzień J., Subieta K.

Prace Instytutu Podstaw Informatyki Polskiej Akademii Nauk

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1999

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nr 889

1-16

EN

We generalize query optimization methods based on rewriting for a general object-oriented model and a formalized OQL-like query language. Our approach makes it possible to detect and factor out independent subqueries in queries bulit upon traditional or new query operators, including dependent joins of OQL, quantifiers, generalized path expressions and method invocations. In contrast to well-known methods relying on specific patterns of algebraic or calculus expressions, our methodis based on a formal analysis of scoping and binding rules for names occuring in a query and its subqueries. It neither depends on the complexity of an independent subquery nor on the operator connecting this subquery to its parent query. Being very general, the method is simple to understand and analyze. We follow the stack-based approach to object-oriented query languages (having roots in the semantics of programming languages), rather than object algebras or calculi.

PL

W pracy przedstawiamy uogólnienie metod optymalizacji zapytań opartych na przepisywaniu dla ogólnego modelu obiektowego i sformalizowanego języka zapytań w duchu OQL. Nasze podejście umożliwia wykrywanie i "wyciąganie przed nawias" niezależnych podzapytań wystepujących w zapytaniach konstruowanych przy pomocy tradycyjnych i nowych operatorów, m. in. zależnych złączeń z języka OQL, kwantyfikatorów, uogólnionych wyrażeń ścieżkowych i wywołań metod. W przeciwieństwie do dobrze znanych metod operujących na specyficznych wzorach wyrażeń algebraicznych lub wyrażeń pewnego rachunku, nasza metoda jest oparta na formalnej analizie zakresów i reguł wiązania dla nazw wystepujących w danym zapytaniu i jego podzapytaniach. Nie zależy ona ani od stopnia złożoności niezależnego podzapytania, ani od rodzaju operatora lączącego go z pytaniem nadrzędnym. Metoda ta jest jednocześnie ogólna oraz łatwa do zrozumienia i wykorzystania do analizy zapytań. W naszej metodzie używamy podejścia stosowego do obiektowych zapytań (które ma korzenie w semantyce języków programowania) zamiast obiektowych algebr i rachunków.