TLDR: CQD-SHAP is a novel framework that uses Shapley values from cooperative game theory to explain complex query answering (CQA) on incomplete knowledge graphs. It quantifies the contribution of each query part (atom) to the ranking of a specific answer, distinguishing between neural inference and symbolic retrieval. This approach enhances transparency, helps users trust AI models, and aids in debugging by revealing which query components are most influenced by the neural model’s predictions, ultimately improving the interpretability of neurosymbolic CQA systems.
In the rapidly evolving world of artificial intelligence, knowledge graphs (KGs) serve as powerful tools for organizing and retrieving factual information. However, real-world KGs are often incomplete, meaning they lack many connections and facts. This incompleteness poses a significant challenge for complex query answering (CQA), which involves sophisticated questions requiring multi-hop reasoning across these vast, yet imperfect, data structures.
Traditional methods, known as symbolic execution, rely solely on existing facts in the KG. While precise, they often miss relevant answers due to missing information. To overcome this, neural and neurosymbolic CQA methods have emerged. These approaches use AI models to infer missing knowledge, significantly improving the ability to answer complex questions. However, a major drawback of these advanced models is their ‘black-box’ nature; it’s often unclear why a particular answer is produced, leading to concerns about user trust and the ability to debug potential errors.
Introducing CQD-SHAP: Explaining Complex Query Answers
A new framework called CQD-SHAP, developed by Parsa Abbasi and Stefan Heindorf, addresses this transparency problem. CQD-SHAP provides a novel way to explain the results of complex queries by quantifying the contribution of each part of a query to the ranking of a specific answer. This framework is built upon Shapley values, a concept from cooperative game theory, ensuring a fair and mathematically rigorous attribution of importance.
At its core, CQD-SHAP helps users understand the value of using a neural predictor (which can infer new knowledge) compared to a symbolic approach (which only uses existing facts). It does this by treating each ‘atom’ or constituent part of a query as a ‘player’ in a game. The ‘value’ of a player is determined by how much executing that atom with a neural model affects the ranking of a target answer, averaged across all possible combinations of executing other atoms.
How It Works: Neural vs. Symbolic Execution
Imagine a query like: “Which drugs are prescribed for diabetes AND cause kidney toxicity?” If “Insulin” appears as a top-ranked answer, CQD-SHAP can explain why. It might assign a high contribution score to the “causes kidney toxicity” part, even if that association is incorrect in the KG. This tells the user that the neural model’s inference for *that specific part* heavily influenced the ranking, allowing them to critically examine that piece of information or the model’s behavior.
The framework defines a ‘partial query’ where some atoms are answered neurally (inferring missing links) and others symbolically (relying only on observed facts). By comparing the rank of a target answer under different combinations of neural and symbolic execution for each atom, CQD-SHAP calculates a Shapley value for each atom. A positive Shapley value indicates that neural execution for that atom improves the answer’s rank, while a negative value suggests it worsens it compared to symbolic execution.
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Key Benefits and Evaluation
The ability to quantify atom-level contributions offers several benefits:
- Increased user trust by revealing the neural model’s influence on each query part.
- New insights into neural model behavior, helping identify weaknesses and guide improvements.
- Highlighting query parts where neural inference significantly impacts an answer’s ranking, aiding domain experts in correcting KG gaps or data scientists in debugging models and queries.
CQD-SHAP’s effectiveness was rigorously evaluated using concepts of ‘necessary’ and ‘sufficient’ explanations on two standard knowledge graph benchmarks, FB15k-237 and NELL995. The results consistently showed that CQD-SHAP outperforms baseline methods across almost all query types. For instance, in intersection queries, where answers are aggregated through score products, the most important atom acts as a critical filter, and executing it differently has a strong impact on the output, which CQD-SHAP accurately captures.
A case study involving a query about musicians (e.g., “piano instrumentalists AND artists in the Rock music genre”) demonstrated how CQD-SHAP can explain both correct and incorrect predictions. For a hard answer like “Paul Weller,” the Shapley values revealed that neural inference for “piano instrumentalists” was beneficial, while for “artists in the Rock music genre,” it worsened the rank because the link already existed and symbolic reasoning was more effective. This level of detail helps users understand precisely which parts of the query rely more on the neural model and can assist in debugging when the model returns undesirable results.
While the most important atom identified by CQD-SHAP is generally necessary for a better ranking, it’s not always sufficient on its own, as Shapley values represent average contributions across all possible execution combinations. This nuanced understanding is crucial for interpreting the explanations effectively.
This research marks a significant step towards making complex query answering more transparent and trustworthy. For more technical details, you can refer to the full paper: CQD-SHAP: Explainable Complex Query Answering via Shapley Values.
