Knowledge graphs have emerged as a sophisticated advancement and refinement of semantic networks, and their deployment is one of the critical methodologies in contemporary artificial intelligence. The construction of knowledge graphs is a multifaceted process involving various techniques, where researchers aim to extract the knowledge from existing resources for the construction since building from scratch entails significant labor and time costs. However, due to the pervasive issue of heterogeneity, the description diversity across different knowledge graphs can lead to mismatches between concepts, thereby impacting the efficacy of knowledge extraction. This Ph.D. study focuses on automatic knowledge graph extension, i.e., properly extending the reference knowledge graph by extracting and integrating concepts from one or more candidate knowledge graphs. We propose a novel knowledge graph extension framework based on entity type recognition. The framework aims to achieve high-quality knowledge extraction by aligning the schemas and entities across different knowledge graphs, thereby enhancing the performance of the extension. This paper elucidates three major contributions: (i) we propose an entity type recognition method exploiting machine learning and property-based similarities to enhance knowledge extraction; (ii) we introduce a set of assessment metrics to validate the quality of the extended knowledge graphs; (iii) we develop a platform for knowledge graph acquisition, management, and extension to benefit knowledge engineers practically. Our evaluation comprehensively demonstrated the feasibility and effectiveness of the proposed extension framework and its functionalities through quantitative experiments and case studies.

In the past years Open Data has become a trend among governments to increase transparency and public engagement by opening up national, regional, and local datasets. However, while many of these datasets come in semi-structured file formats, they use di ff erent schemata and lack geo-references or semantically meaningful links and descriptions of the corresponding geo-entities. We aim to address this by detecting and establishing links to geo-entities in the datasets found in Open Data catalogs and their respective metadata descriptions and link them to a knowledge graph of geo-entities. This knowledge graph does not yet readily exist, though, or at least, not a single one: so, we integrate and interlink several datasets to construct our (extensible) base geo-entities knowledge graph: (i) the openly available geospatial data repository GeoNames, (ii) the map service OpenStreetMap, (iii) country-specific sets of postal codes, and (iv) the European Union's classification system NUTS. As a second step, this base knowledge graph is used to add semantic labels to the open datasets, i.e., we heuristically disambiguate the geo-entities in CSV columns using the context of the labels and the hierarchical graph structure of our base knowledge graph. Finally, in order to interact with and retrieve the content, we index the datasets and provide a demo user interface. Currently we indexed resources from four Open Data portals, and allow search queries for geo-entities as well as full-text matches at http://data.wu.ac.at/odgraph/ .

Intuitively, most datasets found in Open Data are organised by spatio-temporal scope, that is, single datasets provide data for a certain region, valid for a certain time period. For many use cases (such as for instance data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Therefore, we argue that spatio-temporal search is a crucial need for Open Data portals and across Open Data portals, yet - to the best of our knowledge - no working solution exists. We argue that - just like for for regular Web search - knowledge graphs can be helpful to significantly improve search: in fact, the ingredients for a public knowledge graph of geographic entities as well as time periods and events exist already on the Web of Data, although they have not yet been integrated and applied - in a principled manner - to the use case of Open Data search. In the present paper we aim at doing just that: we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical, as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals, (iii) enable structured, spatio-temporal search over Open Data catalogs through our spatio-temporal knowledge graph, both via a search interface as well as via a SPARQL endpoint, available at data.wu.ac.at/odgraphsearch/ / Series: Working Papers on Information Systems, Information Business and Operations

In the past years Open Data has become a trend among governments to increase transparency and public engagement by opening up national, regional, and local datasets. However, while many of these datasets come in semi-structured file formats, they use different schemata and lack geo-references or semantically meaningful links and descriptions of the corresponding geo-entities. We aim to address this by detecting and establishing links to geo-entities in the datasets found in Open Data catalogs and their respective metadata descriptions and link them to a knowledge graph of geo-entities. This knowledge graph does not yet readily exist, though, or at least, not a single one: so, we integrate and interlink several datasets to construct our (extensible) base geo-entities knowledge graph: (i) the openly available geospatial data repository GeoNames, (ii) the map service OpenStreetMap, (iii) country-specific sets of postal codes, and (iv) the European Union¿s classification system NUTS. As a second step, this base knowledge graph is used to add semantic labels to the open datasets, i.e., we heuristically disambiguate the geo-entities in CSV columns using the context of the labels and the hierarchical graph structure of our base knowledge graph. Finally, in order to interact with and retrieve the content, we index the datasets and provide a demo user interface. Currently we indexed resources from four Open Data portals, and allow search queries for geo-entities as well as full-text matches at http://data.wu.ac.at/odgraph/.

Intuitively, most datasets found on governmental Open Data portals are organized by spatio-temporal criteria, that is, single datasets provide data for a certain region, valid for a certain time period. Likewise, for many use cases (such as, for instance, data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Rich spatio-temporal annotations are therefore a crucial need to enable semantic search for (and across) Open Data portals along those dimensions, yet -- to the best of our knowledge -- no working solution exists. To this end, in the present paper we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals with entities from this knowledge graph, and (iii) enable structured, spatio-temporal search and querying over Open Data catalogs, both via a search interface as well as via a SPARQL endpoint, available at http://data.wu.ac.at/odgraphsearch/ / Series: Working Papers on Information Systems, Information Business and Operations

Intuitively, most datasets found on governmental Open Data portals are organized by spatio-temporal criteria, that is, single datasets provide data for a certain region, valid for a certain time period. Likewise, for many use cases (such as, for instance, data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Rich spatio-temporal annotations are therefore a crucial need to enable semantic search for (and across) Open Data portals along those dimensions, yet -- to the best of our knowledge -- no working solution exists. To this end, in the present paper we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals with entities from this knowledge graph, and (iii) enable structured, spatio-temporal search and querying over Open Data catalogs, both via a search interface as well as via a SPARQL endpoint, available at http://data.wu.ac.at/odgraphsearch/ / Series: Working Papers on Information Systems, Information Business and Operations

Intuitively, most datasets found on governmental Open Data portals are organized by spatio-temporal criteria, that is, single datasets provide data for a certain region, valid for a certain time period. Likewise, for many use cases (such as, for instance, data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Rich spatio-temporal annotations are therefore a crucial need to enable semantic search for (and across) Open Data portals along those dimensions, yet -- to the best of our knowledge -- no working solution exists. To this end, in the present paper we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals with entities from this knowledge graph, and (iii) enable structured, spatio-temporal search and querying over Open Data catalogs, both via a search interface as well as via a SPARQL endpoint, available at http://data.wu.ac.at/odgraphsearch/ / Series: Working Papers on Information Systems, Information Business and Operations

Intuitively, most datasets found on governmental Open Data portals are organized by spatio-temporal criteria, that is, single datasets provide data for a certain region, valid for a certain time period. Likewise, for many use cases (such as, for instance, data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Rich spatio-temporal annotations are therefore a crucial need to enable semantic search for (and across) Open Data portals along those dimensions, yet -- to the best of our knowledge -- no working solution exists. To this end, in the present paper we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals with entities from this knowledge graph, and (iii) enable structured, spatio-temporal search and querying over Open Data catalogs, both via a search interface as well as via a SPARQL endpoint, available at http://data.wu.ac.at/odgraphsearch/ / Series: Working Papers on Information Systems, Information Business and Operations

This master thesis investigates the utilisation of various graph based machine learning models for solving a customer segmentation problem, a task coupled to Customer Relationship Management, where the objective is to divide customers into different groups based on similar attributes. More specifically a customer segmentation problem is solved via an unsupervised machine learning technique named clustering, using the k-means clustering algorithm. Three different representations of customers as a vector of attributes are created and then utilised by the k-means algorithm to divide users into different clusters. The first representation is using a elementary feature vector and the other two approaches are using feature vectors produced by graph based machine learning models. Results show that similar grouping are found but that results vary depending on what data is included in the instantiation and training of the various approaches and their corresponding models.

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