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Title: Semantic Parsing for Knowledge Graph Question Answering
Authors: Shanza Noor
Journal: International Journal of Human and Society (IJHS)
Publisher: Educational Scholarly Horizons
Country: Pakistan
Year: 2024
Volume: 4
Issue: 1
Language: English
Knowledge graphs are full of information, but understanding them all requires knowing about natural language questions. Semantic understanding plays a big role. It changes questions from users into forms that knowledge graphs can understand. But going over this bridge shows obvious breaks. One problem comes from the big words and hard connections in knowledge graphs. Ask about a "mountain range" between France and Spain. Nowadays, most programs are having problems with many things and connections, so they could get this question wrong, leading to useless answers. Another issue shows up in the area of talking with others. In real life, people often ask questions that are complex and build on previous answers to shape the meaning of future queries. A parser that doesn't understand the context might get confused if someone asks, "What is the capital of the country we talked about earlier?" After chatting about Paris without saying which country it belongs to. The bridge lacks provisions for generalization and interpretability. In this study we have proposed parsers encounter difficulties with novel questions, and their reasoning remains opaque. Envision inquiring about the inventor of the printing press and their motivations. A parser incapable of drawing parallels from comparable historical figures or elucidating its rationale for identifying Gutenberg would furnish users with incomplete answers, eroding trust in the process. Furthermore, our strategy endeavors to overcome these challenges, with the goal of constructing a resilient and efficient semantic parser—a bridge devoid of weaknesses and fissures. This endeavor aims to enable users to pose questions in a natural and conversational manner, thereby unleashing the complete potential of knowledge graphs and furnishing them with the precise answers they seek.
To propose a resilient and efficient semantic parser that overcomes challenges in knowledge graph question answering (KGQA), enabling users to pose questions in a natural and conversational manner.
The study proposes a semantic parsing approach for KGQA, focusing on conversational questions. It introduces the SPICE dataset, designed for semantic parsing of conversational questions using Wikidata, with executable SPARQL annotations. Two baseline models, BertSPG (using oracle annotations) and BertSPS/BertSPA (using automated entity and type linking), are developed and evaluated on the SPICE dataset. The methodology involves analyzing performance based on query types and linguistic phenomena like coreference and ellipsis, and evaluating generalization to unseen query patterns.
graph TD
A["Introduce SPICE Dataset"] --> B["Develop Baseline Models: BertSPG, BertSPS, BertSPA"];
B --> C["Evaluate Models on SPICE Dataset"];
C --> D["Analyze Performance by Query Type and Phenomena"];
D --> E["Assess Generalization to Unseen Intents"];
E --> F["Identify Limitations and Future Work"];
The paper highlights the challenges in semantic parsing for KGQA, including handling complex knowledge graph structures, conversational nuances like ellipsis and coreference, and the need for generalization. The proposed SPICE dataset and baseline models aim to address these issues. The performance analysis reveals the impact of annotation quality and the limitations of current models in capturing full conversational context and unseen query patterns.
- The BertSPG model, with oracle annotations, significantly outperforms BertSPS and BertSPA in match performance.
- BertSPS demonstrates better performance than BertSPA in handling compound named entities and disambiguation.
- Both BertSPS and LasagneSP struggle with resolving references to utterances not immediately preceding them in the conversation.
- Performance on coreference and ellipsis significantly improves with gold annotations.
- Models show limitations in generalizing to unseen query intents and handling complex conversational context.
The research introduces the SPICE dataset for conversational semantic parsing over knowledge graphs, providing executable SPARQL annotations and addressing various linguistic phenomena. Two baseline models are developed and analyzed, demonstrating the importance of annotation quality and highlighting areas for future improvement in conversational semantic parsers, particularly in handling context and generalization.
- The SPICE dataset contains 197 thousand encounters. (Page 35)
- The study proposes two distinct approaches for semantic parsing. (Page 39)
- The BertSPG model with oracle data achieves the best possible performance. (Page 41)
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