On Type-Aware Entity Retrieval

Today, the practice of returning entities from a knowledge base in response to search queries has become widespread. One of the distinctive characteristics of entities is that they are typed, i.e., assigned to some hierarchically organized type system (type taxonomy). The primary objective of this paper is to gain a better understanding of how entity type information can be utilized in entity retrieval. We perform this investigation in an idealized "oracle" setting, assuming that we know the distribution of target types of the relevant entities for a given query. We perform a thorough analysis of three main aspects: (i) the choice of type taxonomy, (ii) the representation of hierarchical type information, and (iii) the combination of type-based and term-based similarity in the retrieval model. Using a standard entity search test collection based on DBpedia, we find that type information proves most useful when using large type taxonomies that provide very specific types. We provide further insights on the extensional coverage of entities and on the utility of target types.Comment: Proceedings of the 3rd ACM International Conference on the Theory of Information Retrieval (ICTIR '17), 201

Building simulated queries for known-item topics: an analysis using six european languages

There has been increased interest in the use of simulated queries for evaluation and estimation purposes in Information Retrieval. However, there are still many unaddressed issues regarding their usage and impact on evaluation because their quality, in terms of retrieval performance, is unlike real queries. In this paper, we focus on methods for building simulated known-item topics and explore their quality against real known-item topics. Using existing generation models as our starting point, we explore factors which may influence the generation of the known-item topic. Informed by this detailed analysis (on six European languages) we propose a model with improved document and term selection properties, showing that simulated known-item topics can be generated that are comparable to real known-item topics. This is a significant step towards validating the potential usefulness of simulated queries: for evaluation purposes, and because building models of querying behavior provides a deeper insight into the querying process so that better retrieval mechanisms can be developed to support the user

Dynamical r-matrices and the chiral WZNW phase space

The dynamical generalization of the classical Yang-Baxter equation that governs the possible Poisson structures on the space of chiral WZNW fields with generic monodromy is reviewed. It is explained that for particular choices of the chiral WZNW Poisson brackets this equation reduces to the CDYB equation recently studied by Etingof--Varchenko and others. Interesting dynamical r-matrices are obtained for generic monodromy as well as by imposing Dirac constraints on the monodromy.Comment: Talk given at XXIII International Colloquium on Group Theoretical Methods in Physics, July 31 - August 5, 2000, Dubna, Russia. LaTeX, 9 page

Equilibriumlike invaded cluster algorithm: critical exponents and dynamical properties

We present a detailed study of the Equilibriumlike invaded cluster algorithm (EIC), recently proposed as an extension of the invaded cluster (IC) algorithm, designed to drive the system to criticality while still preserving the equilibrium ensemble. We perform extensive simulations on two special cases of the Potts model and examine the precision of critical exponents by including the leading corrections. We show that both thermal and magnetic critical exponents can be obtained with high accuracy compared to the best available results. The choice of the auxiliary parameters of the algorithm is discussed in context of dynamical properties. We also discuss the relation to the Li-Sokal bound for the dynamical exponent $z$.Comment: 11 pages, 13 figures, accepted for publication in Phys. Rev.

Dusty Cometary Globules in W5

We report the discovery of four dusty cometary tails around low mass stars in two young clusters belonging to the W5 star forming region. Fits to the observed emission profiles from 24 micron observations with the Spitzer Space Telescope give tail lifetimes < 30 Myr, but more likely < 5 Myr. This result suggests that the cometary phase is a short lived phenomenon, occurring after photoevaporation by a nearby O star has removed gas from the outer disk of a young low mass star (see also Balog et al. 2006; Balog et al. 2008).Comment: 11 pages, 3 figures. Accepted for publication to ApJ Letter

Broad expertise retrieval in sparse data environments

Expertise retrieval has been largely unexplored on data other than the W3C collection. At the same time, many intranets of universities and other knowledge-intensive organisations offer examples of relatively small but clean multilingual expertise data, covering broad ranges of expertise areas. We first present two main expertise retrieval tasks, along with a set of baseline approaches based on generative language modeling, aimed at finding expertise relations between topics and people. For our experimental evaluation, we introduce (and release) a new test set based on a crawl of a university site. Using this test set, we conduct two series of experiments. The first is aimed at determining the effectiveness of baseline expertise retrieval methods applied to the new test set. The second is aimed at assessing refined models that exploit characteristic features of the new test set, such as the organizational structure of the university, and the hierarchical structure of the topics in the test set. Expertise retrieval models are shown to be robust with respect to environments smaller than the W3C collection, and current techniques appear to be generalizable to other settings

Graph-Embedding Empowered Entity Retrieval

In this research, we improve upon the current state of the art in entity retrieval by re-ranking the result list using graph embeddings. The paper shows that graph embeddings are useful for entity-oriented search tasks. We demonstrate empirically that encoding information from the knowledge graph into (graph) embeddings contributes to a higher increase in effectiveness of entity retrieval results than using plain word embeddings. We analyze the impact of the accuracy of the entity linker on the overall retrieval effectiveness. Our analysis further deploys the cluster hypothesis to explain the observed advantages of graph embeddings over the more widely used word embeddings, for user tasks involving ranking entities