Semantic keyword search for expert witness discovery

In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach

Semantic keyword search for expert witness discovery

In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach

AMaχoS—Abstract Machine for Xcerpt

Web query languages promise convenient and efficient access to Web data such as XML, RDF, or Topic Maps. Xcerpt is one such Web query language with strong emphasis on novel high-level constructs for effective and convenient query authoring, particularly tailored to versatile access to data in different Web formats such as XML or RDF. However, so far it lacks an efficient implementation to supplement the convenient language features. AMaχoS is an abstract machine implementation for Xcerpt that aims at efficiency and ease of deployment. It strictly separates compilation and execution of queries: Queries are compiled once to abstract machine code that consists in (1) a code segment with instructions for evaluating each rule and (2) a hint segment that provides the abstract machine with optimization hints derived by the query compilation. This article summarizes the motivation and principles behind AMaχoS and discusses how its current architecture realizes these principles

The usability of semantic search tools: a review

The goal of semantic search is to improve on traditional search methods by exploiting the semantic metadata. In this paper, we argue that supporting iterative and exploratory search modes is important to the usability of all search systems. We also identify the types of semantic queries the users need to make, the issues concerning the search environment and the problems that are intrinsic to semantic search in particular. We then review the four modes of user interaction in existing semantic search systems, namely keyword-based, form-based, view-based and natural language-based systems. Future development should focus on multimodal search systems, which exploit the advantages of more than one mode of interaction, and on developing the search systems that can search heterogeneous semantic metadata on the open semantic Web

Towards a Linked Semantic Web: Precisely, Comprehensively and Scalably Linking Heterogeneous Data in the Semantic Web

The amount of Semantic Web data is growing rapidly today. Individual users, academic institutions and businesses have already published and are continuing to publish their data in Semantic Web standards, such as RDF and OWL. Due to the decentralized nature of the Semantic Web, the same real world entity may be described in various data sources with different ontologies and assigned syntactically distinct identifiers. Furthermore, data published by each individual publisher may not be complete. This situation makes it difficult for end users to consume the available Semantic Web data effectively. In order to facilitate data utilization and consumption in the Semantic Web, without compromising the freedom of people to publish their data, one critical problem is to appropriately interlink such heterogeneous data. This interlinking process is sometimes referred to as Entity Coreference, i.e., finding which identifiers refer to the same real world entity. In the Semantic Web, the owl:sameAs predicate is used to link two equivalent (coreferent) ontology instances. An important question is where these owl:sameAs links come from. Although manual interlinking is possible on small scales, when dealing with large-scale datasets (e.g., millions of ontology instances), automated linking becomes necessary. This dissertation summarizes contributions to several aspects of entity coreference research in the Semantic Web. First of all, by developing the EPWNG algorithm, we advance the performance of the state-of-the-art by 1% to 4%. EPWNG finds coreferent ontology instances from different data sources by comparing every pair of instances and focuses on achieving high precision and recall by appropriately collecting and utilizing instance context information domain-independently. We further propose a sampling and utility function based context pruning technique, which provides a runtime speedup factor of 30 to 75. Furthermore, we develop an on-the-fly candidate selection algorithm, P-EPWNG, that enables the coreference process to run 2 to 18 times faster than the state-of-the-art on up to 1 million instances while only making a small sacrifice in the coreference F1-scores. This is achieved by utilizing the matching histories of the instances to prune instance pairs that are not likely to be coreferent. We also propose Offline, another candidate selection algorithm, that not only provides similar runtime speedup to P-EPWNG but also helps to achieve higher candidate selection and coreference F1-scores due to its more accurate filtering of true negatives. Different from P-EPWNG, Offline pre-selects candidate pairs by only comparing their partial context information that is selected in an unsupervised, automatic and domain-independent manner.In order to be able to handle really heterogeneous datasets, a mechanism for automatically determining predicate comparability is proposed. Combing this property matching approach with EPWNG and Offline, our system outperforms state-of-the-art algorithms on the 2012 Billion Triples Challenge dataset on up to 2 million instances for both coreference F1-score and runtime. An interesting project, where we apply the EPWNG algorithm for assisting cervical cancer screening, is discussed in detail. By applying our algorithm to a combination of different patient clinical test results and biographic information, we achieve higher accuracy compared to its ablations. We end this dissertation with the discussion of promising and challenging future work

Vermeidung von Repräsentationsheterogenitäten in realweltlichen Wissensgraphen

Knowledge graphs are repositories providing factual knowledge about entities. They are a great source of knowledge to support modern AI applications for Web search, question answering, digital assistants, and online shopping. The advantages of machine learning techniques and the Web's growth have led to colossal knowledge graphs with billions of facts about hundreds of millions of entities collected from a large variety of sources. While integrating independent knowledge sources promises rich information, it inherently leads to heterogeneities in representation due to a large variety of different conceptualizations. Thus, real-world knowledge graphs are threatened in their overall utility. Due to their sheer size, they are hardly manually curatable anymore. Automatic and semi-automatic methods are needed to cope with these vast knowledge repositories. We first address the general topic of representation heterogeneity by surveying the problem throughout various data-intensive fields: databases, ontologies, and knowledge graphs. Different techniques for automatically resolving heterogeneity issues are presented and discussed, while several open problems are identified. Next, we focus on entity heterogeneity. We show that automatic matching techniques may run into quality problems when working in a multi-knowledge graph scenario due to incorrect transitive identity links. We present four techniques that can be used to improve the quality of arbitrary entity matching tools significantly. Concerning relation heterogeneity, we show that synonymous relations in knowledge graphs pose several difficulties in querying. Therefore, we resolve these heterogeneities with knowledge graph embeddings and by Horn rule mining. All methods detect synonymous relations in knowledge graphs with high quality. Furthermore, we present a novel technique for avoiding heterogeneity issues at query time using implicit knowledge storage. We show that large neural language models are a valuable source of knowledge that is queried similarly to knowledge graphs already solving several heterogeneity issues internally.Wissensgraphen sind eine wichtige Datenquelle von Entitätswissen. Sie unterstützen viele moderne KI-Anwendungen. Dazu gehören unter anderem Websuche, die automatische Beantwortung von Fragen, digitale Assistenten und Online-Shopping. Neue Errungenschaften im maschinellen Lernen und das außerordentliche Wachstum des Internets haben zu riesigen Wissensgraphen geführt. Diese umfassen häufig Milliarden von Fakten über Hunderte von Millionen von Entitäten; häufig aus vielen verschiedenen Quellen. Während die Integration unabhängiger Wissensquellen zu einer großen Informationsvielfalt führen kann, führt sie inhärent zu Heterogenitäten in der Wissensrepräsentation. Diese Heterogenität in den Daten gefährdet den praktischen Nutzen der Wissensgraphen. Durch ihre Größe lassen sich die Wissensgraphen allerdings nicht mehr manuell bereinigen. Dafür werden heutzutage häufig automatische und halbautomatische Methoden benötigt. In dieser Arbeit befassen wir uns mit dem Thema Repräsentationsheterogenität. Wir klassifizieren Heterogenität entlang verschiedener Dimensionen und erläutern Heterogenitätsprobleme in Datenbanken, Ontologien und Wissensgraphen. Weiterhin geben wir einen knappen Überblick über verschiedene Techniken zur automatischen Lösung von Heterogenitätsproblemen. Im nächsten Kapitel beschäftigen wir uns mit Entitätsheterogenität. Wir zeigen Probleme auf, die in einem Multi-Wissensgraphen-Szenario aufgrund von fehlerhaften transitiven Links entstehen. Um diese Probleme zu lösen stellen wir vier Techniken vor, mit denen sich die Qualität beliebiger Entity-Alignment-Tools deutlich verbessern lässt. Wir zeigen, dass Relationsheterogenität in Wissensgraphen zu Problemen bei der Anfragenbeantwortung führen kann. Daher entwickeln wir verschiedene Methoden um synonyme Relationen zu finden. Eine der Methoden arbeitet mit hochdimensionalen Wissensgrapheinbettungen, die andere mit einem Rule Mining Ansatz. Beide Methoden können synonyme Relationen in Wissensgraphen mit hoher Qualität erkennen. Darüber hinaus stellen wir eine neuartige Technik zur Vermeidung von Heterogenitätsproblemen vor, bei der wir eine implizite Wissensrepräsentation verwenden. Wir zeigen, dass große neuronale Sprachmodelle eine wertvolle Wissensquelle sind, die ähnlich wie Wissensgraphen angefragt werden können. Im Sprachmodell selbst werden bereits viele der Heterogenitätsprobleme aufgelöst, so dass eine Anfrage heterogener Wissensgraphen möglich wird

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