Extracting and Learning Semantics from Social Web Data

Making machines understand natural language is a dream of mankind that existedsince a very long time. Early attempts at programming machines to converse withhumans in a supposedly intelligent way with humans relied on phrase lists and simplekeyword matching. However, such approaches cannot provide semantically adequateanswers, as they do not consider the specific meaning of the conversation. Thus, if wewant to enable machines to actually understand language, we need to be able to accesssemantically relevant background knowledge. For this, it is possible to query so-calledontologies, which are large networks containing knowledge about real-world entitiesand their semantic relations. However, creating such ontologies is a tedious task, as oftenextensive expert knowledge is required. Thus, we need to find ways to automaticallyconstruct and update ontologies that fit human intuition of semantics and semanticrelations. More specifically, we need to determine semantic entities and find relationsbetween them. While this is usually done on large corpora of unstructured text, previouswork has shown that we can at least facilitate the first issue of extracting entities byconsidering special data such as tagging data or human navigational paths. Here, we donot need to detect the actual semantic entities, as they are already provided because ofthe way those data are collected. Thus we can mainly focus on the problem of assessingthe degree of semantic relatedness between tags or web pages. However, there existseveral issues which need to be overcome, if we want to approximate human intuition ofsemantic relatedness. For this, it is necessary to represent words and concepts in a waythat allows easy and highly precise semantic characterization. This also largely dependson the quality of data from which these representations are constructed.In this thesis, we extract semantic information from both tagging data created by usersof social tagging systems and human navigation data in different semantic-driven socialweb systems. Our main goal is to construct high quality and robust vector representationsof words which can the be used to measure the relatedness of semantic concepts.First, we show that navigation in the social media systems Wikipedia and BibSonomy isdriven by a semantic component. After this, we discuss and extend methods to modelthe semantic information in tagging data as low-dimensional vectors. Furthermore, weshow that tagging pragmatics influences different facets of tagging semantics. We theninvestigate the usefulness of human navigational paths in several different settings onWikipedia and BibSonomy for measuring semantic relatedness. Finally, we proposea metric-learning based algorithm in adapt pre-trained word embeddings to datasetscontaining human judgment of semantic relatedness.This work contributes to the field of studying semantic relatedness between wordsby proposing methods to extract semantic relatedness from web navigation, learn highqualityand low-dimensional word representations from tagging data, and to learnsemantic relatedness from any kind of vector representation by exploiting humanfeedback. Applications first and foremest lie in ontology learning for the Semantic Web,but also semantic search or query expansion.