Name Disambiguation from link data in a collaboration graph using temporal and topological features

In a social community, multiple persons may share the same name, phone number or some other identifying attributes. This, along with other phenomena, such as name abbreviation, name misspelling, and human error leads to erroneous aggregation of records of multiple persons under a single reference. Such mistakes affect the performance of document retrieval, web search, database integration, and more importantly, improper attribution of credit (or blame). The task of entity disambiguation partitions the records belonging to multiple persons with the objective that each decomposed partition is composed of records of a unique person. Existing solutions to this task use either biographical attributes, or auxiliary features that are collected from external sources, such as Wikipedia. However, for many scenarios, such auxiliary features are not available, or they are costly to obtain. Besides, the attempt of collecting biographical or external data sustains the risk of privacy violation. In this work, we propose a method for solving entity disambiguation task from link information obtained from a collaboration network. Our method is non-intrusive of privacy as it uses only the time-stamped graph topology of an anonymized network. Experimental results on two real-life academic collaboration networks show that the proposed method has satisfactory performance.Comment: The short version of this paper has been accepted to ASONAM 201

From general to specialized domain: Analyzing three crucial problems of biomedical entity disambiguation

Entity disambiguation is the task of mapping ambiguous terms in natural-language text to its entities in a knowledge base. Most disambiguation systems focus on general purpose knowledge bases like DBpedia but leave out the question how those results generalize to more specialized domains. This is very important in the context of Linked Open Data, which forms an enormous resource for disambiguation. We implement a ranking-based (Learning To Rank) disambiguation system and provide a systematic evaluation of biomedical entity disambiguation with respect to three crucial and well-known properties of specialized disambiguation systems. These are (i) entity context, i.e. the way entities are described, (ii) user data, i.e. quantity and quality of externally disambiguated entities, and (iii) quantity and heterogeneity of entities to disambiguate, i.e. the number and size of different domains in a knowledge base. Our results show that (i) the choice of entity context that is used to attain the best disambiguation results strongly depends on the amount of available user data, (ii) disambiguation results with large-scale and heterogeneous knowledge bases strongly depend on the entity context, (iii) disambiguation results are robust against a moderate amount of noise in user data and (iv) some results can be significantly improved with a federated disambiguation approach that uses different entity contexts. Our results indicate that disambiguation systems must be carefully adapted when expanding their knowledge bases with special domain entities

Global geometric graph kernels and applications

This thesis explores the topics of graph kernels and classification of graphs. Graph kernels have received considerable attention in the last decade, in part because of their value in many practical applications, such as chemo informatics and molecular biology, in which classification using graph kernels have become the standard model for several problems. Perhaps even more important is the inclusion of graph kernels in the rich field of kernel methods, making a large family of machine learning algorithms, including support vector machines, applicable to data naturally represented as graphs. Graph kernels are similarity functions defined on pairs of graphs. Traditionally, graph kernels compare graphs in terms of features of subgraphs such as walks, paths or tree patterns. For the kernels to remain computationally efficient, these subgraphs are often chosen to be small. Because of this fact, most graph kernels adopt an inherently local perspective on the graph and may fail to discern global properties, such as the girth or the chromatic number, that are not captured in local structure. Furthermore, existing work on graph kernels lack results justifying a particular choice of kernel for a given application. In this thesis we propose two new graph kernels, designed to capture global properties of graphs, as described above. At the core of these kernels is Lov ́asz number, an important concept in graph theory with strong connections to graph properties like the chromatic number and the size of the largest clique. We give efficient sampling approximations to both kernels, allowing them to scale to large graphs. We also show that we can characterize the separation margin induced by these kernels in certain classification tasks. This serves as initial progress towards making theory aid kernel choice. We make an extensive empirical evaluation of both kernels on synthetic data with known global properties, and on real graphs frequently used to benchmark graph kernels. Finally, we present a new application of graph kernels in the field of data mining by redefining an important subproblem of entity disambiguation as a graph classification problem. We show empirically that our proposed method improves on the state-of-the-art

Global geometric graph kernels and applications

This thesis explores the topics of graph kernels and classification of graphs. Graph kernels have received considerable attention in the last decade, in part because of their value in many practical applications, such as chemo informatics and molecular biology, in which classification using graph kernels have become the standard model for several problems. Perhaps even more important is the inclusion of graph kernels in the rich field of kernel methods, making a large family of machine learning algorithms, including support vector machines, applicable to data naturally represented as graphs. Graph kernels are similarity functions defined on pairs of graphs. Traditionally, graph kernels compare graphs in terms of features of subgraphs such as walks, paths or tree patterns. For the kernels to remain computationally efficient, these subgraphs are often chosen to be small. Because of this fact, most graph kernels adopt an inherently local perspective on the graph and may fail to discern global properties, such as the girth or the chromatic number, that are not captured in local structure. Furthermore, existing work on graph kernels lack results justifying a particular choice of kernel for a given application. In this thesis we propose two new graph kernels, designed to capture global properties of graphs, as described above. At the core of these kernels is Lov ́asz number, an important concept in graph theory with strong connections to graph properties like the chromatic number and the size of the largest clique. We give efficient sampling approximations to both kernels, allowing them to scale to large graphs. We also show that we can characterize the separation margin induced by these kernels in certain classification tasks. This serves as initial progress towards making theory aid kernel choice. We make an extensive empirical evaluation of both kernels on synthetic data with known global properties, and on real graphs frequently used to benchmark graph kernels. Finally, we present a new application of graph kernels in the field of data mining by redefining an important subproblem of entity disambiguation as a graph classification problem. We show empirically that our proposed method improves on the state-of-the-art

Joint Inference for Knowledge Base Population

Populating Knowledge Base (KB) with new knowledge facts from reliable text resources usually consists of linking name mentions to KB entities and identifying relationship between entity pairs. However, the task often suffers from errors propagating from upstream entity linkers to downstream relation extractors. In this paper, we propose a novel joint inference framework to allow interactions between the two subtasks and find an optimal assignment by addressing the coherence among preliminary local predictions: whether the types of entities meet the expectations of relations explicitly or implicitly, and whether the local predictions are globally compatible. We further measure the confidence of the extracted triples by looking at the details of the complete extraction process. Experiments show that the proposed framework can significantly reduce the error propagations thus obtain more reliable facts, and outperforms competitive baselines with state-of-the-art relation extraction models. ? 2014 Association for Computational Linguistics.EI

Search beyond traditional probabilistic information retrieval

"This thesis focuses on search beyond probabilistic information retrieval. Three ap- proached are proposed beyond the traditional probabilistic modelling. First, term associ- ation is deeply examined. Term association considers the term dependency using a factor analysis based model, instead of treating each term independently. Latent factors, con- sidered the same as the hidden variables of ""eliteness"" introduced by Robertson et al. to gain understanding of the relation among term occurrences and relevance, are measured by the dependencies and occurrences of term sequences and subsequences. Second, an entity-based ranking approach is proposed in an entity system named ""EntityCube"" which has been released by Microsoft for public use. A summarization page is given to summarize the entity information over multiple documents such that the truly relevant entities can be highly possibly searched from multiple documents through integrating the local relevance contributed by proximity and the global enhancer by topic model. Third, multi-source fusion sets up a meta-search engine to combine the ""knowledge"" from different sources. Meta-features, distilled as high-level categories, are deployed to diversify the baselines. Three modified fusion methods are employed, which are re- ciprocal, CombMNZ and CombSUM with three expanded versions. Through extensive experiments on the standard large-scale TREC Genomics data sets, the TREC HARD data sets and the Microsoft EntityCube Web collections, the proposed extended models beyond probabilistic information retrieval show their effectiveness and superiority.

Towards Name Disambiguation: Relational, Streaming, and Privacy-Preserving Text Data

In the real world, our DNA is unique but many people share names. This phenomenon often causes erroneous aggregation of documents of multiple persons who are namesakes of one another. Such mistakes deteriorate the performance of document retrieval, web search, and more seriously, cause improper attribution of credit or blame in digital forensics. To resolve this issue, the name disambiguation task 1 is designed to partition the documents associated with a name reference such that each partition contains documents pertaining to a unique real-life person. Existing algorithms for this task mainly suffer from the following drawbacks. First, the majority of existing solutions substantially rely on feature engineering, such as biographical feature extraction, or construction of auxiliary features from Wikipedia. However, for many scenarios, such features may be costly to obtain or unavailable in privacy sensitive domains. Instead we solve the name disambiguation task in restricted setting by leveraging only the relational data in the form of anonymized graphs. Second, most of the existing works for this task operate in a batch mode, where all records to be disambiguated are initially available to the algorithm. However, more realistic settings require that the name disambiguation task should be performed in an online streaming fashion in order to identify records of new ambiguous entities having no preexisting records. Finally, we investigate the potential disclosure risk of textual features used in name disambiguation and propose several algorithms to tackle the task in a privacy-aware scenario. In summary, in this dissertation, we present a number of novel approaches to address name disambiguation tasks from the above three aspects independently, namely relational, streaming, and privacy preserving textual data

Personal named entity linking based on simple partial tree matching and context free grammar

Personal name disambiguation is the task of linking a personal name to a unique comparable entry in the real world, also known as named entity linking (NEL). Algorithms for NEL consist of three main components: extractor, searcher, and disambiguator. Existing approaches for NEL use exact-matched look-up over the surface form to generate a set of candidate entities in each of the mentioned names. The exact-matched look-up is wholly inadequate to generate a candidate entity due to the fact that the personal names within a web page lack uniform representation. In addition, the performance of a disambiguator in ranking candidate entities is limited by context similarity. Context similarity is an inflexible feature for personal disambiguation because natural language is highly variable. We propose a new approach that can be used to both identify and disambiguate personal names mentioned on a web page. Our NEL algorithm uses: as an extractor: a control flow graph; AlchemyAPI, as a searcher: Personal Name Transformation Modules (PNTM) based on Context Free Grammar and the Jaro-Winkler text similarity metric and as a disambiguator: the entity coherence method: the Occupation Architecture for Personal Name Disambiguation (OAPnDis), personal name concepts and Simple Partial Tree Matching (SPTM). Experimental results, evaluated on real-world data sets, show that the accuracy of our NEL is 92%, which is higher than the accuracy of previously used methods

Joint Discourse-aware Concept Disambiguation and Clustering

This thesis addresses the tasks of concept disambiguation and clustering. Concept disambiguation is the task of linking common nouns and proper names in a text – henceforth called mentions – to their corresponding concepts in a predefined inventory. Concept clustering is the task of clustering mentions, so that all mentions in one cluster denote the same concept. In this thesis, we investigate concept disambiguation and clustering from a discourse perspective and propose a discourse-aware approach for joint concept disambiguation and clustering in the framework of Markov logic. The contributions of this thesis are fourfold: Joint Concept Disambiguation and Clustering. In previous approaches, concept disambiguation and concept clustering have been considered as two separate tasks (Schütze, 1998; Ji & Grishman, 2011). We analyze the relationship between concept disambiguation and concept clustering and argue that these two tasks can mutually support each other. We propose the – to our knowledge – first joint approach for concept disambiguation and clustering. Discourse-Aware Concept Disambiguation. One of the determining factors for concept disambiguation and clustering is the context definition. Most previous approaches use the same context definition for all mentions (Milne & Witten, 2008b; Kulkarni et al., 2009; Ratinov et al., 2011, inter alia). We approach the question which context is relevant to disambiguate a mention from a discourse perspective and state that different mentions require different notions of contexts. We state that the context that is relevant to disambiguate a mention depends on its embedding into discourse. However, how a mention is embedded into discourse depends on its denoted concept. Hence, the identification of the denoted concept and the relevant concept mutually depend on each other. We propose a binwise approach with three different context definitions and model the selection of the context definition and the disambiguation jointly. Modeling Interdependencies with Markov Logic. To model the interdependencies between concept disambiguation and concept clustering as well as the interdependencies between the context definition and the disambiguation, we use Markov logic (Domingos & Lowd, 2009). Markov logic combines first order logic with probabilities and allows us to concisely formalize these interdependencies. We investigate how we can balance between linguistic appropriateness and time efficiency and propose a hybrid approach that combines joint inference with aggregation techniques. Concept Disambiguation and Clustering beyond English: Multi- and Cross-linguality. Given the vast amount of texts written in different languages, the capability to extend an approach to cope with other languages than English is essential. We thus analyze how our approach copes with other languages than English and show that our approach largely scales across languages, even without retraining. Our approach is evaluated on multiple data sets originating from different sources (e.g. news, web) and across multiple languages. As an inventory, we use Wikipedia. We compare our approach to other approaches and show that it achieves state-of-the-art results. Furthermore, we show that joint concept disambiguating and clustering as well as joint context selection and disambiguation leads to significant improvements ceteris paribus