34 research outputs found
Evaluation of Text Document Clustering Using K-Means
The fundamentals of human communication are language and written texts. Social media is an essential source of data on the Internet, but email and text messages are also considered to be one of the main sources of textual data. The processing and analysis of text data is conducted using text mining methods. Text Mining is the extension of Data Mining to text files to extract relevant information from large amounts of text data and to recognize patterns. Cluster analysis is one of the most important text mining methods. Its goal is the automatic partitioning of a number of objects into a finite set of homogeneous groups (clusters). The objects should be as similar as possible within a group. Objects from different groups, however, should have different characteristics. The starting-point of cluster analysis is a precise definition of the task and the selection of representative data objects. A challenge regarding text documents is their unstructured form, which requires extensive pre-processing. For the automated processing of natural language Natural Language Processing (NLP) is used. The conversion of text files into a numerical form can be performed using the Bag-of-Words (BoW) approach or neural networks. Each data object can finally be represented as a point in a finite-dimensional space, where the dimension corresponds to the number of unique tokens, here words. Prior to the actual cluster analysis, a measure must also be defined to determine the similarity or dissimilarity between the objects. To measure dissimilarity, metrics such as Euclidean distance, for example, are used. Then clustering methods are applied. The cluster methods can be divided into different categories. On the one hand,there are methods that form a hierarchical system, which are also called hierarchical cluster methods. On the other hand, there are techniques that provide a division into groups by determining a grouping on the basis of an optimal homogeneity measure, whereby the number of groups is predetermined. The procedures of this class are called partitioning methods. An important representative is the k-Means method which is used in this thesis. The results are finally evaluated and interpreted. In this thesis, the different methods used in the individual cluster analysis steps are introduced. In order to make a statement about which method seems to be the most suitable for clustering documents, a practical investigation was carried out on the basis of three different data sets
Automatic Structured Text Summarization with Concept Maps
Efficiently exploring a collection of text documents in order to answer a complex question is a challenge that many people face. As abundant information on almost any topic is electronically available nowadays, supporting tools are needed to ensure that people can profit from the information's availability rather than suffer from the information overload. Structured summaries can help in this situation: They can be used to provide a concise overview of the contents of a document collection, they can reveal interesting relationships and they can be used as a navigation structure to further explore the documents. A concept map, which is a graph representing concepts and their relationships, is a specific form of a structured summary that offers these benefits. However, despite its appealing properties, only a limited amount of research has studied how concept maps can be automatically created to summarize documents. Automating that task is challenging and requires a variety of text processing techniques including information extraction, coreference resolution and summarization. The goal of this thesis is to better understand these challenges and to develop computational models that can address them. As a first contribution, this thesis lays the necessary ground for comparable research on computational models for concept map--based summarization. We propose a precise definition of the task together with suitable evaluation protocols and carry out experimental comparisons of previously proposed methods. As a result, we point out limitations of existing methods and gaps that have to be closed to successfully create summary concept maps. Towards that end, we also release a new benchmark corpus for the task that has been created with a novel, scalable crowdsourcing strategy. Furthermore, we propose new techniques for several subtasks of creating summary concept maps. First, we introduce the usage of predicate-argument analysis for the extraction of concept and relation mentions, which greatly simplifies the development of extraction methods. Second, we demonstrate that a predicate-argument analysis tool can be ported from English to German with low effort, indicating that the extraction technique can also be applied to other languages. We further propose to group concept mentions using pairwise classifications and set partitioning, which significantly improves the quality of the created summary concept maps. We show similar improvements for a new supervised importance estimation model and an optimal subgraph selection procedure. By combining these techniques in a pipeline, we establish a new state-of-the-art for the summarization task. Additionally, we study the use of neural networks to model the summarization problem as a single end-to-end task. While such approaches are not yet competitive with pipeline-based approaches, we report several experiments that illustrate the challenges - mostly related to training data - that currently limit the performance of this technique. We conclude the thesis by presenting a prototype system that demonstrates the use of automatically generated summary concept maps in practice and by pointing out promising directions for future research on the topic of this thesis
A structural and quantitative analysis of the webof linked data and its components to perform retrieval data
Esta investigación consiste en un análisis cuantitativo y estructural de la Web of Linked Data con el fin de mejorar la búsqueda de datos en distintas fuentes. Para obtener métricas cuantitativas de la Web of Linked Data, se aplicarán técnicas estadísticas. En el caso del análisis estructural haremos un Análisis de Redes Sociales (ARS).
Para tener una idea de la Web of Linked Data para poder hacer un análisis, nos ayudaremos del diagrama de la Linking Open Data (LOD) cloud. Este es un catálogo online de datasets cuya información ha sido publicada usando técnicas de Linked Data. Los datasets son publicados en un lenguaje llamado Resource Description Framework (RDF), el cual crea enlaces entre ellos para que la información pudiera ser reutilizada.
El objetivo de obtener un análisis cuantitativo y estructural de la Web of Linked Data es mejorar las búsquedas de datos. Para ese propósito nosotros nos aprovecharemos del uso del lenguaje de marcado Schema.org y del proyecto Linked Open Vocabularies (LOV).
Schema.org es un conjunto de etiquetas cuyo objetivo es que los Webmasters pudieran marcar sus propias páginas Web con microdata. El microdata es usado para ayudar a los motores de búsqueda y otras herramientas Web a entender mejor la información que estas contienen. LOV es un catálogo para registrar los vocabularios que usan los datasets de la Web of Linked Data. Su objetivo es proporcionar un acceso sencillo a dichos vocabularios.
En la investigación, vamos a desarrollar un estudio para la obtención de datos de la Web of Linked Data usando las fuentes mencionadas anteriormente con técnicas de “ontology matching”. En nuestro caso, primeros vamos a mapear Schema.org con LOV, y después LOV con la Web of Linked Data. Un ARS de LOV también ha sido realizado. El objetivo de dicho análisis es obtener una idea cuantitativa y cualitativa de LOV. Sabiendo esto podemos concluir cosas como: cuales son los vocabularios más usados o si están especializados en algún campo o no. Estos pueden ser usados para filtrar datasets o reutilizar información
Semantics and result disambiguation for keyword search on tree data
Keyword search is a popular technique for searching tree-structured data (e.g., XML, JSON) on the web because it frees the user from learning a complex query language and the structure of the data sources. However, the convenience of keyword search comes with drawbacks. The imprecision of the keyword queries usually results in a very large number of results of which only very few are relevant to the query. Multiple previous approaches have tried to address this problem. Some of them exploit structural and semantic properties of the tree data in order to filter out irrelevant results while others use a scoring function to rank the candidate results. These are not easy tasks though and in both cases, relevant results might be missed and the users might spend a significant amount of time searching for their intended result in a plethora of candidates. Another drawback of keyword search on tree data, also due to the incapacity of keyword queries to precisely express the user intent, is that the query answer may contain different types of meaningful results even though the user is interested in only some of them.
Both problems of keyword search on tree data are addressed in this dissertation. First, an original approach for answering keyword queries is proposed. This approach extracts structural patterns of the query matches and reasons with them in order to return meaningful results ranked with respect to their relevance to the query. The proposed semantics performs comparisons between patterns of results by using different types of ho-momorphisms between the patterns. These comparisons are used to organize the patterns into a graph of patterns which is leveraged to determine ranking and filtering semantics. The experimental results show that the approach produces query results of higher quality compared to the previous ones. To address the second problem, an original approach for clustering the keyword search results on tree data is introduced. The clustered output allows the user to focus on a subset of the results, and to save time and effort while looking for the relevant results. The approach performs clustering at different levels of granularity to group similar results together effectively. The similarity of the results and result clusters is decided using relations on structural patterns of the results defined based on homomor-phisms between path patterns. An originality of the clustering approach is that the clusters are ranked at different levels of granularity to quickly guide the user to the relevant result patterns. An efficient stack-based algorithm is presented for generating result patterns and constructing the clustering hierarchy. The extensive experimentation with multiple real datasets show that the algorithm is fast and scalable. It also shows that the clustering methodology allows the users to effectively retrieve their intended results, and outperforms a recent state-of-the-art clustering approach. In order to tackle the second problem from a different aspect, diversifying the results of keyword search is addressed. Diversification aims to provide the users with a ranked list of results which balances the relevance and redundancy of the results. Measures for quantifying the relevance and dissimilarity of result patterns are presented and a heuristic for generating a diverse set of results using these metrics is introduced
Schema-aware keyword search on linked data
Keyword search is a popular technique for querying the ever growing repositories of RDF graph data on the Web. This is due to the fact that the users do not need to master complex query languages (e.g., SQL, SPARQL) and they do not need to know the underlying structure of the data on the Web to compose their queries. Keyword search is simple and flexible. However, it is at the same time ambiguous since a keyword query can be interpreted in different ways. This feature of keyword search poses at least two challenges: (a) identifying relevant results among a multitude of candidate results, and (b) dealing with the performance scalability issue of the query evaluation algorithms.
In the literature, multiple schema-unaware approaches are proposed to cope with the above challenges. Some of them identify as relevant results only those candidate results which maintain the keyword instances in close proximity. Other approaches filter out irrelevant results using their structural characteristics or rank and top-k process the retrieved results based on statistical information about the data. In any case, these approaches cannot disambiguate the query to identify the intent of the user and they cannot scale satisfactorily when the size of the data and the number of the query keywords grow. In recent years, different approaches tried to exploit the schema (structural summary) of the RDF (Resource Description Framework) data graph to address the problems above. In this context, an original hierarchical clustering technique is introduced in this dissertation. This approach clusters the results based on a semantic interpretation of the keyword instances and takes advantage of relevance feedback from the user. The clustering hierarchy uses pattern graphs which are structured queries and clustering together result graphs with the same structure. Pattern graphs represent possible interpretations for the keyword query. By navigating though the hierarchy the user can select the pattern graph which is relevant to her intent.
Nevertheless, structural summaries are approximate representations of the data and, therefore, might return empty answers or miss results which are relevant to the user intent. To address this issue, a novel approach is presented which combines the use of the structural summary and the user feedback with a relaxation technique for pattern graphs to extract additional results potentially of interest to the user. Query caching and multi-query optimization techniques are leveraged for the efficient evaluation of relaxed pattern graphs. Although the approaches which consider the structural summary of the data graph are promising, they require interaction with the user.
It is claimed in this dissertation that without additional information from the user, it is not possible to produce results of high quality from keyword search on RDF data with the existing techniques. In this regard, an original keyword query language on RDF data is introduced which allows the user to convey his intention flexibly and effortlessly by specifying cohesive keyword groups. A cohesive group of keywords in a query indicates that its keywords should form a cohesive unit in the query results. It is experimentally demonstrated that cohesive keyword queries improve the result quality effectively and prune the search space of the pattern graphs efficiently compared to traditional keyword queries. Most importantly, these benefits are achieved while retaining the simplicity and the convenience of traditional keyword search.
The last issue addressed in this dissertation is the diversification problem for keyword search on RDF data. The goal of diversification is to trade off relevance and diversity in the results set of a keyword query in order to minimize the dissatisfaction of the average user. Novel metrics are developed for assessing relevance and diversity along with techniques for the generation of a relevant and diversified set of query interpretations for a keyword query on an RDF data graph. Experimental results show the effectiveness of the metrics and the efficiency of the approach
Improving Clustering Methods By Exploiting Richness Of Text Data
Clustering is an unsupervised machine learning technique, which involves discovering different clusters (groups) of similar objects in unlabeled data and is generally considered to be a NP hard problem. Clustering methods are widely used in a verity of disciplines for analyzing different types of data, and a small improvement in clustering method can cause a ripple effect in advancing research of multiple fields.
Clustering any type of data is challenging and there are many open research questions. The clustering problem is exacerbated in the case of text data because of the additional challenges such as issues in capturing semantics of a document, handling rich features of text data and dealing with the well known problem of the curse of dimensionality.
In this thesis, we investigate the limitations of existing text clustering methods and address these limitations by providing five new text clustering methods--Query Sense Clustering (QSC), Dirichlet Weighted K-means (DWKM), Multi-View Multi-Objective Evolutionary Algorithm (MMOEA), Multi-objective Document Clustering (MDC) and Multi-Objective Multi-View Ensemble Clustering (MOMVEC). These five new clustering methods showed that the use of rich features in text clustering methods could outperform the existing state-of-the-art text clustering methods.
The first new text clustering method QSC exploits user queries (one of the rich features in text data) to generate better quality clusters and cluster labels.
The second text clustering method DWKM uses probability based weighting scheme to formulate a semantically weighted distance measure to improve the clustering results.
The third text clustering method MMOEA is based on a multi-objective evolutionary algorithm. MMOEA exploits rich features to generate a diverse set of candidate clustering solutions, and forms a better clustering solution using a cluster-oriented approach.
The fourth and the fifth text clustering method MDC and MOMVEC address the limitations of MMOEA. MDC and MOMVEC differ in terms of the implementation of their multi-objective evolutionary approaches.
All five methods are compared with existing state-of-the-art methods. The results of the comparisons show that the newly developed text clustering methods out-perform existing methods by achieving up to 16\% improvement for some comparisons. In general, almost all newly developed clustering algorithms showed statistically significant improvements over other existing methods.
The key ideas of the thesis highlight that exploiting user queries improves Search Result Clustering(SRC); utilizing rich features in weighting schemes and distance measures improves soft subspace clustering; utilizing multiple views and a multi-objective cluster oriented method improves clustering ensemble methods; and better evolutionary operators and objective functions improve multi-objective evolutionary clustering ensemble methods.
The new text clustering methods introduced in this thesis can be widely applied in various domains that involve analysis of text data. The contributions of this thesis which include five new text clustering methods, will not only help researchers in the data mining field but also to help a wide range of researchers in other fields
Instance-based Hierarchical Schema Alignment in Linked Data
학위논문 (박사)-- 서울대학교 대학원 : 치의과학과 의료경영과정보학전공, 2015. 8. 김홍기.Along with the development of Web of documents, there is a natural need for sharing, exchanging, and merging heterogeneous data to provide more comprehensive information and answer users with more complex questions. However, the data published on the Web are raw dumps that sacrifice much of the semantics that can be used for exchanging and integrating data. Resource Description Framework (RDF) and Linked Data are designed to expose the semantics of data by interlinking data represented with well-defined relations. With the profusion of RDF resources and Linked Data, ontology alignment has gained significance in providing highly comprehensive knowledge embedded in disparate sources. Ontology alignment, however, in Linking Open Data (LOD) has traditionally focused more on the instance-level rather than the schema-level. Linked Data supports schema-level matching, provided that instance-level matching is already established. Linked Data is a hotbed for instance-based schema matching, which is considered a better solution for matching classes with ambiguous or obscure names. In this dissertation, the author focuses on three issues in instance-based schema alignment for Linked Data: (1) how to align schemas based on instances, (2) how to scale the schema alignment, (3) how to generate a hierarchical schema structure.
Targeting the first issue, the author has proposed an instance-based schema alignment algorithm called IUT. The IUT builds a unified taxonomy for the classes from two ontologies based on an instance-class matrix and obtains the relations of two classes by the common instances. The author tested the IUT with DBpedia and YAGO2, and compared the IUT with two state-of-the-art methods in four alignment tasks. The experiments show that the IUT outperforms the methods in terms of efficiency and effectiveness (e.g., costs 968 ms to obtain 0.810 F-score on intra-subsumption alignment in DBpedia).
Targeting the second issue, the author has proposed a scaled version of the IUT called IUT(M). The IUT(M) decreases the computations of the IUT from two aspects based on Locality Sensitive Hashing (LSH): (1) decreasing the similarity computations for each pair of classes with MinHash functions, and (2) decreasing the number of similarity computations with banding. The author tested the IUT(M) with YAGO2-YAGO2 intra-subsumption alignment task to demonstrate that the running time of IUT can be reduced by 94% with a 5% loss in F-score.
Targeting the third issue, the author has proposed a method to generate a faceted taxonomy based on object properties on Linked Data. A framework is proposed to build a sub-taxonomy in each facet with sub-data, extracted with an object property, with an Instance-based Concept Taxonomy generation algorithm called ICT. Two experiments demonstrate: (1) The ICT efficiently and effectively generates a sub-taxonomy with rdf:type in DBpedia and YAGO2 (e.g., costs 49 and 11,790 ms to build the concept taxonomies that achieve 0.917 and 0.780 on Taxonomic F-score). (2) The faceted taxonomies for Diseasome and DrugBank, efficiently generated based on multiple object properties (e.g., costs 2,032 and 2,525 ms to build the faceted taxonomies based on 6 and 16 properties), can effectively reduce the search spaces in faceted searches (e.g., obtains 1.65 and 1.03 on Maximum Resolution with 2 facets).1 Introduction 1
1.1 Background and Motivations 1
1.1.1 Data Integration and Schema Alignment 1
1.1.2 From RDF to Linked Data 3
1.1.3 Schema Alignment in Linked Data 5
1.2 Instance-based Schema Alignment 9
1.3 Contributions of this Dissertation 13
1.4 Organization of this Dissertation 15
2 Preliminaries and Related Works 17
2.1 Preliminaries 17
2.1.1 RDF and Linked Data 17
2.1.2 Ontology and Schema Alignment in Linked Data 20
2.2 Related Works 23
2.2.1 Instance-based Schema Alignment 23
2.2.2 Scaling Pairwise Similarity Computations 29
2.2.3 Automatic Taxonomy Generation 32
3 Aligning Schemas with Subsumption and Equivalence Relations 36
3.1 Introduction 36
3.2 Problem Definition 38
3.3 Methods 41
3.3.1 Workflow of Instance-based Schema Alignment 41
3.3.2 Instance-class Matrix Generation 42
3.3.3 Subsumption and Equivalence Relations Discovering 44
3.4 Experiments 48
3.4.1 Schema Alignment Algorithms in Comparison 48
3.4.2 Data and Experiment Design 48
3.5 Results 52
3.5.1 Intra-subsumption Relations for YAGO2-YAGO2 54
3.5.2 Intra-subsumption Relations for DBpedia-DBpedia 58
3.5.3 Inter-Subsumption and Equivalence Relations for YAGO2-DBpedia 61
3.5.4 Effects of χ_s and χ_e for the IUT 67
3.6 Discussions 71
3.7 Conclusion 75
4 Scaling Pair-wise Computations Using the Locality Sensitive Hashing 76
4.1 Introduction 76
4.2 Methods 78
4.2.1 MinHash and Signatures 79
4.2.2 Banding Technique 83
4.2.3 Scaling the IUT with MinHash and Banding 85
4.3 Experiment 87
4.4 Discussions 92
4.5 Conclusion 93
5 Unsupervised Hierarchical Schema Structure Generation in Linked Data 94
5.1 Introduction 94
5.2 Faceted Taxonomy for Linked Data 98
5.3 Framework 101
5.3.1 Facets Extraction 102
5.3.2 Instance Restriction and Redundancy Removal 102
5.3.3 Redundant Object Removal 103
5.3.4 Instance-object Matrix Generation 103
5.4 Generating Faceted Taxonomy 105
5.4.1 The Problem of Generating a Sub-taxonomy for a Facet 105
5.4.2 Concept Definition and Naming 105
5.4.3 Taxonomy Generation Algorithm 108
5.4.4 Instantiation and Taxonomy Refinement 110
5.5 Experiments 112
5.5.1 Task 1-Construction of Taxonomy with rdf:type 112
5.5.2 Task 2-Construction of Multiple Faceted Taxonomies 115
5.6 Results 119
5.6.1 Results of Task 1 119
5.6.2 Results of Task 2 124
5.7 Discussion 131
5.8 Conclusion 133
6 Future Works and Conclusion 134
6.1 Future Works 134
6.1.1 Similarity Measures for Instance-based Schema Alignment 134
6.1.2 Ontology Evolution for Instance-based Schema Alignment 135
6.1.3 Combining the IUT with Structure- and Lexical-based Methods 136
6.1.4 Scaling the IUT with Parallel Computations 137
6.1.5 Faceted Navigation and Search for Linked Data 137
6.2 Conclusion 139
Bibliography 142
초록 152Docto