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graph-based managing and mining of processes and data in the domain of intellectual property

INFORMATION SYSTEMS 2022年 106卷 101844-101844页

作者： Huebscher, Gerd Geist, Verena Auer, Dagmar Ekelhart, Andreas Mayer, Rudolf Nadschlaeger, Stefan Kueng, Josef Hubscher & Partner Patentanwalte GmbH Spittelwiese 4 A-4020 Linz Austria Software Competence Ctr Hagenberg GmbH Softwarepk 32a A-4232 Hagenberg Austria Johannes Kepler Univ Linz Altenbergerstr 69 A-4040 Linz Austria Polymind GmbH Salierigasse 1-5 A-1180 Vienna Austria SBA Res gGmbH Floragasse 7 A-1040 Vienna Austria

Digitalization of knowledge work in communication-intensive domains such as intellectual property protection poses great challenges but also opportunities to improve today's working environments. The legal domain is strongly characterized by knowledge work, whereby, despite a common legal framework, creativity of individual experts is decisive. This knowledge-intensive work deals with a great amount of data objects, not only as a working basis, but also as a result. While experts heavily follow individual working styles, they still rely on a vast amount of administrative tasks, which are carried out by the supporting staff. These tasks are expected to be performed regularly, reliably and without errors, despite necessary adjustments to the current case and the changing legal framework. Today, knowledge work and administrative tasks are typically supported by different tools that are hardly integrated. Therefore, the tracing of continuous work processes based on exchanged data objects is a great challenge. This traceability is crucial, not only for legal security reasons, but also to enable mining and learning of applicable knowledge about processes. In this paper, we propose a bottom up approach, which applies a continuously evolving graph of integrated data objects and tasks to model and store static and dynamic aspects of administrative as well as knowledge work, and test the approach in a real-world setting in the domain of intellectual property. We further present initial results of a novel dependency-based mining approach to learn data-dependent task sequences in the graph-based model and discuss several methods for enabling privacy-preserving sharing and mining. (C)& nbsp;2021 The Authors. Published by Elsevier Ltd.

关键词： graph-structured data Business process management Knowledge work Process mining graph mining Privacy

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Ranking structured Objects with graph Neural Networks

Ranking Structured Objects with Graph Neural Networks

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24th International Conference on Discovery Science (DS)

作者： Damke, Clemens Huellermeier, Eyke Paderborn Univ Heinz Nixdorf Inst Paderborn Germany Univ Munich Munich Germany

ISBN: (纸本)9783030889425;9783030889418

graph neural networks (GNNs) have been successfully applied in many structured data domains, with applications ranging from molecular property prediction to the analysis of social networks. Motivated by the broad applicability of GNNs, we propose the family of so-called RankGNNs, a combination of neural Learning to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences between graphs, suggesting that one of them is preferred over the other. One practical application of this problem is drug screening, where an expert wants to find the most promising molecules in a large collection of drug candidates. We empirically demonstrate that our proposed pair-wise RankGNN approach either significantly outperforms or at least matches the ranking performance of the naive point-wise baseline approach, in which the LtR problem is solved via GNN-based graph regression.

关键词： graph-structured data graph neural networks Preference learning Learning to rank

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Stable Model Semantics for Recursive SHACL 20

Stable Model Semantics for Recursive SHACL

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29th Web Conference (WWW)

作者： Andresel, Medina Corman, Julien Ortiz, Magdalena Reutter, Juan L. Savkovic, Ognjen Simkus, Mantas TU Wien Vienna Austria Free Univ Bozen Bolzano Bolzano Italy PUC Chile Santiago Chile IMFD Chile Santiago Chile

ISBN: (纸本)9781450370233

SHACL (SHape Constraint Language) is a W3C recommendation for validating graph-based data against a set of constraints (called shapes). Importantly, SHACL allows to define recursive shapes, i.e. a shape may refer to itself, directly of indirectly. The recommendation left open the semantics of recursive shapes, but proposals have emerged recently to extend the official semantics to support recursion. These proposals are based on the principle of possibility (or non-contradiction): a graph is considered valid against a schema if one can assign shapes to nodes in such a way that all constraints are satisfied. This semantics is not constructive, as it does not provide guidelines about how to obtain such an assignment, and it may lead to unfounded assignments, where the only reason to assign a shape to a node is that it allows validating the graph. In contrast, we propose in this paper a stricter, more constructive semantics for SHACL, based on stable models, which are well-known in Answer Set Programming (ASP). This semantics additionally requires a shape assignment to be properly justified by the input constraints. We further exploit the connection to logic programming, and show that SHACL constraints can be naturally represented as logic programs, and that the validation problem for a graph and a SHACL schema can be encoded as an ASP reasoning task. The proposed semantics also enjoys computationally tractable validation in the presence of constraints with stratified negation (as opposed to the previous semantics). We also extend our semantics to 3-valued stable models, which yields a more relaxed notion of validation, tolerant to certain faults in the schema or data. By exploiting a connection between 3-valued stable model semantics and the well-founded semantics for logic programs, we can use our translation into ASP to show another tractability result. Finally, we provide a preliminary evaluation of the approach, which leverages an ASP solver to perform graph valida

关键词： SHACL graph-structured data answer set programming

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Matched signal detection on graphs: Theory and application to brain imaging data classification

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NEUROIMAGE 2016年 125卷 587-600页

作者： Hu, Chenhui Sepulcre, Jorge Johnson, Keith A. Fakhri, Georges E. Lu, Yue M. Li, Quanzheng Massachusetts Gen Hosp NMMI Ctr Adv Med Imaging Sci Radiol Boston MA 02114 USA Harvard Univ Sch Engn & Appl Sci Cambridge MA 02138 USA Massachusetts Gen Hosp NMMI Radiol Boston MA 02114 USA

Motivated by recent progress in signal processing on graphs, we have developed a matched signal detection (MSD) theory for signals with intrinsic structures described by weighted graphs. First, we regard graph Laplacian eigenvalues as frequencies of graph-signals and assume that the signal is in a subspace spanned by the first few graph Laplacian eigenvectors associated with lower eigenvalues. The conventional matched subspace detector can be applied to this case. Furthermore, we study signals that may not merely live in a subspace. Concretely, we consider signals with bounded variation on graphs and more general signals that are randomly drawn from a prior distribution. For bounded variation signals, the test is a weighted energy detector. For the random signals, the test statistic is the difference of signal variations on associated graphs, if a degenerate Gaussian distribution specified by the graph Laplacian is adopted. We evaluate the effectiveness of the MSD on graphs both with simulated and real data sets. Specifically, we apply MSD to the brain imaging data classification problem of Alzheimer's disease (AD) based on two independent data sets: 1) positron emission tomography data with Pittsburgh compound-B tracer of 30 AD and 40 normal control (NC) subjects, and 2) resting-state functional magnetic resonance imaging (R-fMRI) data of 30 early mild cognitive impairment and 20 NC subjects. Our results demonstrate that the MSD approach is able to outperform the traditional methods and help detect AD at an early stage, probably due to the success of exploiting the manifold structure of the data. (C) 2015 Published by Elsevier Inc.

关键词： Matched signal detection graph-structured data Spectral graph theory Brain networks Classification Alzheimer's disease PIB-PET R-fMRI

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PGQL: a Property graph Query Language 4

PGQL: a Property Graph Query Language

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4th International Workshop on graph data Management Experiences and Systems (GRADES)

作者： van Rest, Oskar Hong, Sungpack Kim, Jinha Meng, Xuming Chafi, Hassan Oracle Labs Austin TX 78721 USA

ISBN: (纸本)9781450347808

graph-based approaches to data analysis have become more widespread, which has given need for a query language for graphs. Such a graph query language needs not only SQL-like functionality for querying structured data, but also intrinsic support for typical graph-style applications: reachability analysis, path finding and graph construction. We propose a new query language for the popular Property graph (PG) data model: the Property graph Query Language (PGQL). PGQL is based on the paradigm of graph pattern matching, closely follows syntactic structures of SQL, and provides regular path queries with conditions on labels and properties to allow for reachability and path finding queries. Besides intrinsic vertex, edge and path types, PGQL also has the graph as intrinsic type and allows for graph construction and query composition.

关键词： graph query languages graph-structured data property graphs

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Keyword Query Routing

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2014年第2期26卷 363-375页

作者： Thanh Tran Zhang, Lei Karlsruhe Inst Technol Inst AIFB D-76128 Karlsruhe Germany

Keyword search is an intuitive paradigm for searching linked data sources on the web. We propose to route keywords only to relevant sources to reduce the high cost of processing keyword search queries over all sources. We propose a novel method for computing top-k routing plans based on their potentials to contain results for a given keyword query. We employ a keyword-element relationship summary that compactly represents relationships between keywords and the data elements mentioning them. A multilevel scoring mechanism is proposed for computing the relevance of routing plans based on scores at the level of keywords, data elements, element sets, and subgraphs that connect these elements. Experiments carried out using 150 publicly available sources on the web showed that valid plans (precision@1 of 0.92) that are highly relevant (mean reciprocal rank of 0.89) can be computed in 1 second on average on a single PC. Further, we show routing greatly helps to improve the performance of keyword search, without compromising its result quality.

关键词： Keyword search keyword query keyword query routing graph-structured data RDF

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An efficient structural index for graph-structured data 08

An efficient structural index for graph-structured data

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7th IEEE/ACIS International Conference on Computer and Information Science in Conjunction with 2nd IEEE/ACIS International Workshop on e-Activity

作者： Fan, Yingjie Zhang, Chenghong Wang, Shuyun Hao, Xiulan Hu, Yunfa Fudan Univ Sch Management Shanghai 200433 Peoples R China Fudan Univ Dept Comp & Informat Technol Shanghai 200433 Peoples R China

ISBN: (纸本)9780769531311

To speed up queries over XML and semi-structured data, a number of structural indexes have been proposed. The structural index is usually a labeled directed graph defined by partitioning nodes in the XML data graph into equivalence classes and storing equivalence classes as index nodes. On the basis of the Inter-Relevant Successive Trees (IRST), we propose an efficient adaptive structural index, IRST(k)-index. Compared with the previous indexes, such as the A (k)-index, D(k)index, and M(k)-index, our experiment results show that the IRST(k)-index performs more efficiently in terms of space consumption and query performance, while using significantly less construction time.

关键词： XML graph-structured data equivalence relation structural summary

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Retrieving keyworded subgraphs with graph ranking score

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第5期39卷 4647-4656页

作者： Kim, Seung Lee, Wookey Arora, Nidhi R. Jo, Tae-Chang Kang, Suk-Ho Inha Univ Dept Ind Engn Inchon 402751 South Korea Seoul Natl Univ Dept Ind Engn Seoul 151744 South Korea Inha Univ Dept Math Inchon 402751 South Korea

Keyword queries have long been popular to search engines and to the information retrieval community and have recently gained momentum for its usage in the expert systems community. The conventional semantics for processing a user query is to find a set of top-k web pages such that each page contains all user keywords. Recently, this semantics has been extended to find a set of cohesively interconnected pages, each of which contains one of the query keywords scattered across these pages. The keyword query having the extended semantics (i.e., more than a list of keywords hyperlinked with each other) is referred to the graph query. in case of the graph query, all the query keywords may not be present on a single Web page. Thus, a set of Web pages with the corresponding hyperlinks need to be presented as the search result. The existing search systems reveal serious performance problem due to their failure to integrate information from multiple connected resources so that an efficient algorithm for keyword query over graph-structured data is proposed. It integrates information from multiple connected nodes of the graph and generates result trees with the occurrence of all the query keywords. We also investigate a ranking measure called graph ranking score (GRS) to evaluate the relevant graph results so that the score can generate a scalar value for keywords as well as for the topology. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： graph-structured data Steiner tree graph ranking score (GRS)

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graph-based learning for information systems

Graph-based learning for information systems

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作者： Li, Xin The University of Arizona

学位级别：Ph.D.

The advance of information technologies (IT) makes it possible to collect a massive amount of data in business applications and information systems. The increasing data volumes require more effective knowledge discovery techniques to make the best use of the data. This dissertation focuses on knowledge discovery on graph-structured data, i.e., graph-based learning. graph-structured data refers to data instances with relational information indicating their interactions in this study. graph-structured data exist in a variety of application areas related to information systems, such as business intelligence, knowledge management, e-commerce, medical informatics, etc. Developing knowledge discovery techniques on graph-structured data is critical to decision making and the reuse of knowledge in business applications.@pqdt@break@In this dissertation, I propose a graph-based learning framework and identify four major knowledge discovery tasks using graph-structured data: topology description, node classification, link prediction, and community detection. I present a series of studies to illustrate the knowledge discovery tasks and propose solutions for these example applications. As to the topology description task, in Chapter 2 I examine the global characteristics of relations extracted from documents. Such relations are extracted using different information processing techniques and aggregated to different analytical unit levels. As to the node classification task, Chapter 3 and Chapter 4 study the patent classification problem and the gene function prediction problem, respectively. In Chapter 3, I model knowledge diffusion and evolution with patent citation networks for patent classification. In Chapter 4, I extend the context assumption in previous research and model context graphs in gene interaction networks for gene function prediction. As to the link prediction task, Chapter 5 presents an example application in recommendation systems. I frame the recommendation pro

关键词： data mining graph-based learning graph-structured data Information systems Knowledge discovery Knowledge managment

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Joint Cluster Analysis of Attribute and Relationship data Without A-Priori Specification of the Number of Clusters

Joint Cluster Analysis of Attribute and Relationship Data Wi...

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13th International Conference on Knowledge Discovery and data Mining

作者： Moser, Flavia Ge, Rong Ester, Martin Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9781595936097

In many applications, attribute and relationship data are available, carrying complementary information about real world entities. In such cases, a joint analysis of both types of data can yield more accurate results than classical clustering algorithms that either use only attribute data or only relationship (graph) data. The Connected k-Center (CkC) has been proposed as the first joint cluster analysis model to discover k clusters which are cohesive on both attribute and relationship data. However, it is well-known that prior knowledge on the number of clusters is often unavailable in applications such as community identification and hotspot analysis. In this paper, we introduce and formalize the problem of discovering an a-priori unspecified number of clusters in the context of joint cluster analysis of attribute and relationship data, called Connected X Clusters (CXC) problem. True clusters are assumed to be compact and distinctive from their neighboring clusters in terms of attribute data and internally connected in terms of relationship data. Different from classical attribute-based clustering methods, the neighborhood of clusters is not defined in terms of attribute data but in terms of relationship data. To efficiently solve the CXC problem, we present JointClust, an algorithm which adopts a dynamic two-phase approach. In the first phase, we find so called cluster atoms. We provide a probability analysis for this phase, which gives us a probabilistic guarantee, that each true cluster is represented by at least one of the initial cluster atoms. In the second phase, these cluster atoms are merged in a bottom-up manner resulting in a dendrogram. The final clustering is determined by our objective function. Our experimental evaluation on several real datasets demonstrates that JointClust indeed discovers meaningful and accurate clusterings without requiring the user to specify the number of clusters.

关键词： Algorithms Clustering graph-structured data Joint Cluster Analysis Community Identification Hotspot Analysis

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