From Text to Knowledge

The global information space provided by the World Wide Web has changed dramatically the way knowledge is shared all over the world. To make this unbelievable huge information space accessible, search engines index the uploaded contents and provide efficient algorithmic machinery for ranking the importance of documents with respect to an input query. All major search engines such as Google, Yahoo or Bing are keyword-based, which is indisputable a very powerful tool for accessing information needs centered around documents. However, this unstructured, document-oriented paradigm of the World Wide Web has serious drawbacks, when searching for specific knowledge about real-world entities. When asking for advanced facts about entities, today's search engines are not very good in providing accurate answers. Hand-built knowledge bases such as Wikipedia or its structured counterpart DBpedia are excellent sources that provide common facts. However, these knowledge bases are far from being complete and most of the knowledge lies still buried in unstructured documents. Statistical machine learning methods have the great potential to help to bridge the gap between text and knowledge by (semi-)automatically transforming the unstructured representation of the today's World Wide Web to a more structured representation. This thesis is devoted to reduce this gap with Probabilistic Graphical Models. Probabilistic Graphical Models play a crucial role in modern pattern recognition as they merge two important fields of applied mathematics: Graph Theory and Probability Theory. The first part of the thesis will present a novel system called Text2SemRel that is able to (semi-)automatically construct knowledge bases from textual document collections. The resulting knowledge base consists of facts centered around entities and their relations. Essential part of the system is a novel algorithm for extracting relations between entity mentions that is based on Conditional Random Fields, which are Undirected Probabilistic Graphical Models. In the second part of the thesis, we will use the power of Directed Probabilistic Graphical Models to solve important knowledge discovery tasks in semantically annotated large document collections. In particular, we present extensions of the Latent Dirichlet Allocation framework that are able to learn in an unsupervised way the statistical semantic dependencies between unstructured representations such as documents and their semantic annotations. Semantic annotations of documents might refer to concepts originating from a thesaurus or ontology but also to user-generated informal tags in social tagging systems. These forms of annotations represent a first step towards the conversion to a more structured form of the World Wide Web. In the last part of the thesis, we prove the large-scale applicability of the proposed fact extraction system Text2SemRel. In particular, we extract semantic relations between genes and diseases from a large biomedical textual repository. The resulting knowledge base contains far more potential disease genes exceeding the number of disease genes that are currently stored in curated databases. Thus, the proposed system is able to unlock knowledge currently buried in the literature. The literature-derived human gene-disease network is subject of further analysis with respect to existing curated state of the art databases. We analyze the derived knowledge base quantitatively by comparing it with several curated databases with regard to size of the databases and properties of known disease genes among other things. Our experimental analysis shows that the facts extracted from the literature are of high quality

Extraction of semantic biomedical relations from text using conditional random fields

<p>Abstract</p> <p>Background</p> <p>The increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of automated information extraction tools. Named entity recognition of well-defined objects, such as genes or proteins, has achieved a sufficient level of maturity such that it can form the basis for the next step: the extraction of relations that exist between the recognized entities. Whereas most early work focused on the mere detection of relations, the classification of the type of relation is also of great importance and this is the focus of this work. In this paper we describe an approach that extracts both the existence of a relation and its type. Our work is based on Conditional Random Fields, which have been applied with much success to the task of named entity recognition.</p> <p>Results</p> <p>We benchmark our approach on two different tasks. The first task is the identification of semantic relations between diseases and treatments. The available data set consists of manually annotated PubMed abstracts. The second task is the identification of relations between genes and diseases from a set of concise phrases, so-called GeneRIF (Gene Reference Into Function) phrases. In our experimental setting, we do not assume that the entities are given, as is often the case in previous relation extraction work. Rather the extraction of the entities is solved as a subproblem. Compared with other state-of-the-art approaches, we achieve very competitive results on both data sets. To demonstrate the scalability of our solution, we apply our approach to the complete human GeneRIF database. The resulting gene-disease network contains 34758 semantic associations between 4939 genes and 1745 diseases. The gene-disease network is publicly available as a machine-readable RDF graph.</p> <p>Conclusion</p> <p>We extend the framework of Conditional Random Fields towards the annotation of semantic relations from text and apply it to the biomedical domain. Our approach is based on a rich set of textual features and achieves a performance that is competitive to leading approaches. The model is quite general and can be extended to handle arbitrary biological entities and relation types. The resulting gene-disease network shows that the GeneRIF database provides a rich knowledge source for text mining. Current work is focused on improving the accuracy of detection of entities as well as entity boundaries, which will also greatly improve the relation extraction performance.</p

Anticipating annotations and emerging trends in biomedical literature

The BioJournalMonitor is a decision support system for the analysis of trends and topics in the biomedical literature. Its main goal is to identify potential diagnostic and therapeu-tic biomarkers for specific diseases. Several data sources are continuously integrated to provide the user with up-to-date information on current research in this field. State-of-the-art text mining technologies are deployed to provide added value on top of the original content, including named en-tity detection, relation extraction, classification, clustering, ranking, summarization, and visualization. We present two novel technologies that are related to the analysis of tem-poral dynamics of text archives and associated ontologies. Currently, the MeSH ontology is used to annotate the sci-entific articles entering the PubMed database with medical terms. Both the maintenance of the ontology as well as the annotation of new articles is performed largely manually. We describe how probabilistic topic models can be used to anno-tate recent articles with the most likely MeSH terms. This provides our users with a competitive advantage because, when searching for MeSH terms, articles are found long be-fore they are manually annotated. We further present a study on how to predict the inclusion of new terms in the MeSH ontology. The results suggest that early prediction of emerging trends is possible. The trend ranking functions are deployed in our system to enable interactive searches for the hottest new trends relating to a disease

Gene-Disease Network Analysis Reveals Functional Modules in Mendelian, Complex and Environmental Diseases

Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download